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Integrated Biology Workflow Guides

1. Differentiation by Infection and Treatment Workflow A ful Two Variable Data Set ee gs by Infection and Treat 122 936100 14 893 0 oH 25 TER 9 893 100 94343 99 9361 0 ELIM aj EES a 237 0059 0 0 000 14 963 0 000 19 572 238 01323 237 0059 0 FindByMolecu E 141 9465 0 15 854 0 102 15 923 5 182 142 95372 141 9465 0 FindByMolecu 9 Quality Control 232 9753 0 4 843 0 023 4 842 0 056 233 9825 1 232 9753 0 FindByMolecu 14 60 022380 0 021 6 116 6 224 0 019 61 029583 60 0223 0 FindByMolecu 14 EENE 172 9538 0 10 832 0 091 5 155 5 446 173 9611 172 9538 0 FindByMolecu 12 gt 69 9923 0 0 437 8 281 7 535 0 134 71 00075 69 9923 0 FindByMolecu 8 Seen Differentiation by Infection and Trea x 174 0381 0 4 904 1511 4 963 14 411 349 0813 174 0381 0 FindByMolecu 11 samples 202 0331 0 0 941 4 461 4 758 0 225 203 04042 202 033180 FindByMolecu 14 E Interpretations 100 001780 2 279 0 407 0 053 0 215 101 00867 100 0017 0 FindByMolecu 16 Results Interpretations GIL Al Samples 97 968880 4 398 0 137 0 019 0 492 98 976 1
2. COCOCO C S Matched Entities Di Pathway Entities of Organism lt Hs_Signaling_by_Rob 1 Homo sapiens Hs_Fatty_acid triacy 2 Homo sapiens Hs_Signaling_by_EGF 1 Homo sapiens Hs_Metabolism_of_a 5 Homo sapiens Eind Find Next Find Previous Match Case Figure 78 Pathway List Inspector dialog box 68 Integrated Biology operations Pathway Analysis 4 Add or edit descriptive information that is stored with the saved pathway list in the Name and Notes fields 5 Click OK The new pathway list is placed in the Analysis folder within the Experiment Navigator End of the optional procedure to select one or more pathways to save them as a custom pathway list c Click Next Elsingle Experiment Analysis Step 3 of 4 x Single Experiment Analysis Results All pathways associated with the chosen organism and the selected pathway sources in Step 1 are listed along with p values the number of Matched Entities and the number of Pathway Entities of Experiment Type for each experiment no p values are computed for Metabolomics experiments To save a subset of the Pathways select the corresponding rows and click Custom Save Click Next to proceed and save all pathways Experiment Differentiation by Infection and Treatment Entity List Filtered by Frequency conditions 100 0 1 Interpretation Infection Trea
3. 7 GO Analysis 8 Single Experiment Pa Add Parameter Edit Parameter Delete Parameter lt lt Back Next gt gt Einish Cancel Figure 21 Experiment Grouping of the Agilent Expression Single Color Demo sam ple data 28 Example experiments 3 Review the sample quality in QC on samples in the Analysis Biological Sig nificance Step 3 of 8 wizard Al workflow Type Analysis Biological Significance Step 3 of 8 x Creating an expression analysis using the sample array experiment This step provides the first view of the data using a Principal Component Analysis PCA PCA lets you assess the data by viewing a 3D scatter plot of the calculated principal components The PCA scores are shown in each of the selection boxes located along the bottom of the 3D PCA Scores window A higher score indicates that the principal component contains more of the variability of the data The com ponents generated in the 3D PCA Scores graph are represented in the X Y and Z axes and are numbered 1 2 3 in order of their decreasing significance Principal component analysis The mathematical process by which data contain ing a number of potentially correlated variables is transformed into a data set in relation to a smaller number of variables called principal components that account for the most variability in the data The result of the data transformation leads to the identification of the bes
4. lt lt Back Next gt gt Einish Cancel Figure 80 Save Pathway List page Single Experiment Analysis Step 4 of 4 The Mass Profiler Professional Display Plane returns showing your entities and associated pathways in the Pathway View as shown in Figure 81 See section 11 3 5 Working with Pathway Lists in the Mass Profiler Professional User Man ual for more information about navigating the Pathway View 5 x mass Profiler Professional Differentiation by Infection and Treatment Project Search View Tools Annotations Windows Help Baalma EE 3 225 La E e e E E A A E gt lt Differentiation by Infection and Treatment One ariable Data Set i Two Variable Data Set F E gt 8 9 ol 38 4 a as i M oe ee Experiments ii Differentiation by Infection and Treatr e M etabolism_of water soluble vitamins and cofactors a Homo sapiens ihl One Variable Data Set WikiPathways All Pathways Reactome http www res Workflow f Experiment Setup Quick Start Guide Experiment Grouping mj Create Interpretation gt ii Differentiation by Infection and Treal x a Samples Interpretations Qy Analysis S E all Entities Filtered on Flags aceCalls P M filter E Filtered by frequency conditions 1 E a 2way ANOVA E 2Way ANOVA p Corr Infec E 2Way ANOVA p Corr
5. 7 GO Analysis 8 Single Experiment Pa US22502705_251209747393 _Treated txt Treated US22502705_251209747394_Untreated txt Untreated U522502705_251209747404_Treated txt Treated Metric Values E Quality Control Metrics Report E Experiment Grouping X Y Axis i nay ee m ae Z Axis X Axis Component 1 44 x Axis Component 2 27 x Z Axis Component 3 13 x Legend Quality Control Metrics Plot Add Remove Samples lt lt Back Next gt gt Finish Cancel Figure 22 QC on samples for the Agilent Expression Single Color Demo sample data 29 Example experiments Creating an expression analysis using the sample array experiment a Review the Filter Probesets results b Click Re run Filter c Mark Detected and Not Detected as Acceptable Flags d Click OK e Click Next Al workflow Type Analysis Biological Significance Step 4 of 8 Steps Filter Probesets 1 pantie If flag values are present entities are Filtered based on their flag values Otherwise entities are filtered based on their signal intensity values To change the filter criteria click Re run Filter Summary Repor 2 Experiment Grouping Displaying 20187 out of 20227 entities where 1 out of 6 samples have flags in Detected Not Detected 3 QC on samples 5 Significance Analysis 6 Fold Change 7 GO Analysis 8 Single Exp
6. Log values for the conditions in the selected experiment interpretation are sent to IPA for analysis The name of the data set used in IPA is named after the source experiment in Mass Profiler Professional Click OK el choose Interpretation x Lil Differentiation by Infection and Treatment E 433 Interpretations All Samples Infection Treatment Non averaged i li Infection Treatment Cancel Figure 95 Choose Interpretation dialog box 19 Integrated Biology operations Pathway Analysis Review the IPA Server Address Type in the address for the IPA server for exam ple analysis ingenuity com Type the name for the project to be created in Project Name By default the name used for the experiment that was originally selected is used The Project Name is used by IPA under to store the pathway information Note IPA only allows unique names for each data set per project To analyze the same experiment more than once change the name of the experiment or change the Project Name Select whether to Use both Direct and Indirect relationships for the analysis If you select Yes IPA builds networks using both direct and indirect molecular interactions between genes If you select No IPA builds networks using only direct interactions between genes Type in specific Knowledge Base content if applicable Knowledge Base content indicates which database is searched for information to build the netwo
7. Network Data Integration GT woe ner Analysis and Visualization ourer sit 420 in a Box A O ADH1 GERI b dOC28 ae TP RPS24 Cytoscape is an open source software O01 platform for visualizing complex y MFA2BARI moc D Y networks and integrating these with wis cCDC19 any type of attribute data A lot of STE12 PCK1 plugins are available for various kinds of problem domains including bioinformatics social network analysis and semantic web Cytoscape Now Bug Bounty Winners Learn More Figure 114 Cytoscape web site c Click Download Cytoscape Now d Type in your information and accept the terms of the License Agreement on the Cytoscape download page e Click Proceed to Download f Download the Latest Product Version 2 8 x version 2 8 1 or higher and install it in a directory that has all read and write permissions available Note Cytoscape version 3 x may not be compatible with Connect to Cytoscape Contact Agilent support to see if 3 x is supported I Thank you Mozilla Firefox Oj x File Edit View History Bookmarks Tools Help S Thank you i e W www cytoscape org download php C B cyctoscape P A Cyt O da a Se 7 ytoscape n l T Search Thank you p Gs O SA a D Home Introduction Download Apps Documentation Community Report a Bug Getting Help Download Latest Production Version 2 8 3 Older Versions Platform
8. on page 41 for selecting multiple rows 2 Click Custom Save This option is only available if one or more entity lists are selected a selected entity list row is highlighted elrind similar Entity Lists Step 3 of 3 x Find Similar Entity lists Results Entity lists showing significant overlap with the entity list selected for analysis are displayed in the left hand spreadsheet To modify the level of significance click the Change cut off button and enter new p value cut off To save the significant entity lists select the lists and click the Custom Save button Entity Lists showing no overlap with the entity list selected for analysis are displayed in the right hand spreadsheet Displaying 26 Objects satisfying corrected p value cut off 0 05 To change use the control buttons below My Two Variable Experi 2Way ANOVA p Carr 238 238 1 44E 43 4 My Two Variable Experi Union 2Way ANOVA cut azi 271 0 0 My Two Variable Experi Filtered by frequency 1220 1220 0 0 My Two Variable Experi Union 2 Way ANOVA cut 271 Zeal 0 0 My Two Variable Experi Filtered on Flags accCa 2957 2957 Pee 0 0 My Two Variable Experi 2Way ANOVA p Corr l 34 34 2 6919966E 17 My Two Variable Experi 2Way ANOVA p Corr 330 330 0 0 My Two Variable Experi Filtered on Flags accCa 2957 2957 00 My Two Variable Experi 2 Way ANOVA p
9. scomation NPI 03iL gt alumin ive 4 itis 4 roto Oncogine at i X amil ji ro lt lt Back Next gt gt Finish Cancel a Figure 139 Pathway View page Extract Relations via NLP Step 3 of 4 for memory as the search text 6 Save the pathway list in a Review the pathway list Extract Relations via NLP b Step 4 of 4 Type a descriptive Name that is stored with the saved pathway entity list c Edit the Notes that are stored with the saved pathway entity list d Double click a row in the Pathways table to launch the Pathway Inspector to review the entities and relations contained in the new pathway e Click Finish ElExtract Relations via NLP Step 4 of 4 E x Save Pathway List This window displays the details of the Pathway List that will be created on clicking Finish You can change the default Name and edit Notes of the Pathway List here as required The new Pathway List contains the pathway listed in the table To change the name of the new pathway double click the corresponding row and change the name in the Multiple Objects Inspector that opens Name Memory Pathway List NLP Notes Pathway List resulting from NLP with the Following settings Input Source PubMed search Search Key used memory Source NLP Number of Pathways fi Creation date Mon Apr 08 15 17 25 MDT 2013 0 Last modified date Mon Apr 08 15 17 26 MOT 2013 SSS Owner user Pathways Pathway
10. 150 0887 1 026 151 09593 9230 667 1 165 0655 1 0196112 166 07343 30882 5 16 211 0956 1 02 70742 445 17993 1489 0869 640 1849 1 0131905 641 1906 3360 75 642 267 0863 1 0328077 268 09296 9054 923 2 159 0302 1 013 160 0378 2915 6667 1 Eind FindiNext Find Previous 7 Match Case Configure Columns Figure 71 Inspect Technology for an experiment 4 Click OK End of process to change organism c Select the pathway organism in Choose Pathway Organism You can choose an organism for finding matched pathways that is different from the organism of the selected experiment Selecting a different organism is useful when the organism specified in the experiment is less or not sufficiently described in the literature or when you want to observe the effects of one organ ism s pathogen metabolite in another organism By default the Choose Pathway Organism selected is that associated with the Experiment d Select the pathway source for your analysis The following pathway sources are available for Curated pathways only e WikiPathways Analysis e WikiPathways Reactome e WikiPathways GenMAPP e WikiPathways Other 64 Integrated Biology operations How to import pathways from WikiPathways Pathway Analysis e BioCyc MetaCyc includes the pathways that you downloaded from the Agi lent Server using Tools gt Import Pathways from BioCyc
11. IPA Server Address analysis ingenuity com Project Name Two variable Data Set Use both Direct and Indirect relationships Yes v Knowledge Base content Include My Pathways in Enrichment Sco Yes T vae Review Settings and ID Mapping before No Gene Identifier Column Entrez Gene ID x Associated Value Column None Md Associated Value Type Normalized ratio Md Cancel Figure 98 Perform Data Analysis on Entity List dialog box In order to use the Export to MetaCore operation your technology must contain Entrez Gene ID annotation This operation is available for gene probe based entity lists not for compound based entity lists Entrez is a cross database search system that integrates the PubMed database of biomedical literature with other literature and molecular databases including DNA and protein sequence structure gene genome genetic variation and gene expres sion The Entrez search system is comprised of forty 40 molecular and literature databases and grows with advances in biomedical research Entrez is maintained by the National Center for Biotechnology Information NCBI website http www ncbi nim nih gov gquery Note You must have an account with Thomson Reuters System Biology Solutions in order to make use of the Export to MetaCore operation More information is avail able at Thomson Reuters Systems Biology http thomsonreuters com products_services science systems biology
12. NLP Networks iew Tagged Content view tagged content for the selected item Title Journal Authors PMID A conserved role for human nup98 in altering chromatin structure and promoting epigenetic transcriptional memory PLoS Biol Light WH Freaney J Sood V Thompson A D Urso A Horvath CM Brickner JH 23555195 Abstract A conserved role for human nup98 in altering chromatin structure and promoting epigenetic transcriptional memory The interaction of nuclear pore proteins Nups with active genes can promote their transcription In yeast some inducible genes interact with the both when active and for several generations after being repressed a phenomenon called epigenetic transcriptional memory This interaction promotes future reactivation and requires Nup100 a homologue of human A similar phenomenon occurs in human cells for at least four generations after treatment with IFN gamma many inducible genes are induced more rapidly and more strongly than in cells that have not previously been exposed to In both yeast and human cells the recently expressed promoters of genes with memory exhibit persistent dimethylation of lysine 4 H3K4me2 and physically interact with Nups and a poised form of However in human cells unlike yeast these interactions occur in the nucleoplasm In human cells transiently depleted of Nup98 or yeast cells lacking Nup100 transcriptional memory is lost RNA polymerase II does n
13. a Click Export to MetaCore in the Workflow Browser This operation is illustrated with Agilent Expression Single Color Demo sample data provided with your Mass Profiler Professional installation The data is ini tially imported and analyzed following the Creating an expression analysis using the sample array experiment on page 23 of this workflow guide The Export to MetaCore operation has two 2 steps as shown in Figure 99 on page 83 82 Integrated Biology operations 2 Select and enter the parameters in the Export to MetaCore dialog box Pathway Analysis Export to MetaCore Export to Export to Thomson MetaCore MetaCore Reuters Parameters Column selection Systems Biology Solutions Choose Entity List dialog box Choose Interpretation dialog box Figure 99 Flow chart of the Export to MetaCore operation b Click OK in the Error dialog box if you inadvertently launched Export to MetaCore when a compound based entity list was the active entity list CE x x Unable to retrieve the Entrez Gene Id annotation column Export to MetaCore cannot be completed Figure 100 Error dialog box a Review the Entity List The active entity list is selected b Click Choose to select a different Entity List The entity list must be a probe based entity list You can select the All Entities entity list to send all the data in the experiment to MetaCore xi s Agilent Single Color Demo A
14. Do not type a file name at this location b Select the folder or create a new folder for your CEF file in the Choose a file dia log box c Type the File name For example you can type Export for Identifica tion cef d Click Save x Save in rr MPP Data 7 ma E El Recent Items Computer File name Export for Identification Save Network Files of type Compound Exchange Format ceF gt Cancel Figure 58 Choose a file dialog box e Click OK Export inclusion parameters from the specified entity list This operation produces a CSV file format comma separated variable and is applicable to MassHunter Quali tative Analysis MassHunter Qualitative Analysis GC Scan AMDIS and ChemStation experiment creation a Click Export Inclusion List in the Workflow Browser 59 Integrated Biology operations 2 Select the entity list in Export Inclusion List Step 1 of 2 Results Interpretation This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data Is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The Export Inclusion List operation has two 2 steps as shown in Figure 59 Export Inclusion List Entity List and File Filtering Parameters Path Chooser for Inclusion List 1 of 2 2 of 2 Choose Entity List dialog box Choose a file dialog box Fi
15. File Edit View Tools Help Organize Include in library Share with Burn New folder A E A A Favorites Name Date modified E Desktop i AdaptiveJavaHelp jar 5 9 2011 4 21 PM Executable Jar File 2 849 KB J Downloads US CriteriaMapper jar 5 9 2011 4 21 PM Executable Jar File 52 KB Recent Places j i z i CytoscapeConnector 1 0 SNAPSHOT jar 5 9 2011 4 21 PM Executable Jar File 217 KB BB Desktop i GeneSpringConnector 1 0 SNA amp PSHOT jar 5 9 2011 4 21 PM Executable Jar File 214KB 3 Libraries GOElitePlugin jar 5 9 2011 4 21 PM Executable Jar File 102 KB a Documents i HeatMap iewer 2 2 1 jar 5 9 2011 4 21 PM Executable Jar File 1 482 KB a Music i HeatStripPlugin jar 5 9 2011 4 21 PM Executable Jar File 198 KB Pictures Le gpml jar 5 9 2011 4 21 PM Executable Jar File 3 606 KB Pn Videos m PathwaySearchPluginwithLibs jar 5 9 2011 4 21 PM Executable Jar File 7 837 KB a jomeqroup A 30 items 30 items jB Computer Us Figure 118 Jar files copied to the Cytoscape plugins folder g Copy the two 2 class files to the bin packages marray cytoscape 1 0 scripts folder in your MPP installation directory BA c Program Files Agilent MassHunter WorkstationMPP12 5 bin packages marray cytoscape 1 0 scrip 2 0 x Or ye rm Workstation MPP12 5 bin packages marray cytoscape 1 0 scripts marray cytoscape v Search cytoscape O File Edit Yiew Tools Help Organize v Include in library
16. Infec E 2Way ANOVA p Corr Treat i E Union 2Way ANOVA cut off p SEA Filtered by Frequency condil PESEA Filtered by Frequency condil My Favorites Quality Control y Analysis y Hs_TCR_signaling_WP1927_45 1 Hs_Signaling_by_Robo_receptor 1 Hs_Sphingolipid_Metabolism_W 2 Hs_Fatty_acid _triacyglhyceral _a 2 Hs_Signaling_by_EGFR_WP1910 1 Hs_Metabolism_of_amino_acids 5 Hs_Factors_involved_in_megaka 1 Hs_L1CAM_interactions_WP184 1 Hs_Neurotransmitter_uptake_a 1 Hs_Membrane_Trafficking_WP1 1 Hs_Integration_of_energy_meta 2 Hs_Metabolism_of_water solubl 6 Hs_Peroxisomal_lipid_metabolis 1 Hs_Asparagine_N linked_glycos 1 Hs_GPCR_downstream_signalin 2 19 3 1 1 1 1 1 1 a 1 3 2 1 1 1 j Class Prediction y Results Interpretations 2 Find Similar Entity Lists Export for Recursion IDBrowser Identification Export for Identification Export Inclusion List Import Annotations l One Variable Data Set F Samples Interpretations Pathway Analysis Single Experiment Analysis Hs_Metabolism_of_carbohydrat Analysis Hs_Eukaryotic_Translation_Ter 1 Multi Omic Analysis S E All Entities Hs_Platelet homeostasis WP 18 17 5 E Filtered on Flag
17. Share with Burn New folder il a A ee 5 si Favorites Name Date modified Type Size E Desktop U SendGenes ndEnrichmentFilesToCytoscape py class 6 27 2012 3 44 PM CLASS File 28 KB J Downloads 7 SendMetabolitesAndInterpretationToCytoscape py class 6 19 2012 3 34 PM CLASS File 22 KB Recent Places MB Desktop T Libraries Documents z h 2 items State 2 Shared l2 items Figure 119 Class files copied to the MPP bin packages marray cytos cape 1 0 scripts folder h Run Mass Profiler Professional and open your recent project Go to step 2 Enter the Cytoscape Installation Path on page 86 to configure Cytoscape and then launch Connect to Cytoscape 91 Integrated Biology operations NLP Networks NLF Networks NLP Network Discovery MeSH Network Builder Extract Relations via MLP NLP Networks features Interaction Databases Eid NLP Networks NLP Networks drives discovery by creating networks around the entities of interest using a powerful Natural Language Processing NLP algorithm that extracts infor mation from published literature The operations available help you to create path ways from PubMed abstracts the MeSH Medical Subject Headings database selected entities or personal data sources using NLP Note The NLP Networks features in Mass Profiler Professional are part of the Path way Analysis module Pathway Analysis is licensed separately and can only be accessed with a va
18. 0 05 Created from Advanced Analy MassHunterQual LCMS_UNIDE 4 i Displaying results from 1 to 100 of 130 Showing page g fi gt of 2 lt lt Back Finish Cancel Figure 35 Search Results page EntityList Search Wizard Step 2 of 2 e Click Finish entity lists Click Choose EntityList s to rerun the EntityList Search Wizard to add additional This step is only performed if you select Custom for the Target entity lists see Figure 32 The entity list table is now filled with the entity lists that met your search criteria from the EntityList Search Wizard b Select one or more entity lists to remove them from further analysis When an entity list is selected the row is highlighted See Review the search results in EntityList Search Wizard Step 2 of 2 on page 41 for selecting multiple rows c Click Remove List to remove the selected entity lists from further analysis d Click Next Elrind Similar Entity Lists Step 2 of 3 Choose Entity Lists Choose Entity Lists First search for the entity lists of interest by clicking on the Choose Entity List s button Choose Entity List s that you wish to use to Find similar Entity Lists and click Next Selected Entity Lists pes O Created from Significance Testing and Fold Change workflow step Filter By Frequency Enti Filtered by Frequency conditions 100 O 1 Filtered on Flags faccCalls
19. 11 556 688 9695 687 9609 0 FindByMolecu 11 cae 375 9233 0 15 858 3 868 0 194 0 545 376 93088 375 9233 0 FindByMolecu 11 352 0715 0 7 875 0 578 0 178 0 228 353 07797 352 071580 FindByMolecu 14 Export to MetaCore 539 9294 0 8 376 3 831 0 205 0 392 540 9373 539 9294 0 FindByMolecu 13 Connect to Cytoscape 958 9567 0 15 254 0 453 7 577 3 776 959 9633 958 9567 0 FindByMolecu 9 785 9379 0 0 000 7 411 14 696 0 000 786 94586 785 9379 0 FindByMolecu 6 Tees 602 0429 0 3 182 0 203 0 000 0 194 603 0487 602 0429 0 FindByMolecu 15 520 038 0 3 395 0 173 0 108 0 029 521 04614 520 038 0 FindByMolecu 15 NLP Network Discovery 356 0341 0 0 104 0 230 0 470 0 055 357 04056 356 0341 0 FindByMolecu 16 MeSH Network Builder 633 9917 0 0 000 11 000 0 000 15 077 634 99915 633 9917 0 FindByMolecu 7 Era Raoa gt 515 96010 15 988 0 457 2 918 0 073 1032 93 16 5 15 9601 0 FindByMolecu 11 r G 14 889 0 342 1 598 7 462 1360 9393 679 967 0 FindByMolecu 10 Craii a 357 385660 1 277 0 751 0 565 16 049 858 992 3 857 9856 0 FindByMolecu 12 ueis My Lists 1 519 4 212 3 534 8 708 776 99 41 775 9828 0 FindByMolecu 12 Remove Entities with missing 7 206 0 221 7 555 0 321 1274 9972 618 0169 0 FindByMolecu 12 Analysis Significance Testing 536 0132 0 10 915 0 029 11 248 0 088 1110 98
20. A workflow used to build a model and classify samples from mass spectrometry data Class prediction is a supervised learning method and involves three steps vali dation training and prediction The algorithm learns from samples training set with known functional class and builds a prediction model to classify new samples test set of unknown class An individual organism e g a person animal plant or other organism of a class or group that is used as a representative of a whole class or group The specific and quantitative addition of one or more compounds to a sample A chemical or mixture of chemicals selected for use as a basis of comparing the quality of analytical results or for use to measure and compensate the precise offset or drift incurred over a set of analyses A measure of variability among a set of data that is equal to the square root of the arithmetic average of the squares of the deviations from the mean A low standard deviation value indicates that the individual data tend to be very close to the mean whereas a high standard deviation indicates that the data is spread out over a larger range of values from the mean 118 Reference information State Statistics Subject Survey t Test Unidentified compound Variable Volume Wizard Definitions A set of circumstances or attributes characterizing a biological organism at a given time A few sample attributes may include temperature time
21. Integrated Biology operations 5 Enter the options for Perform Data Analysis on Entity List Pathway Analysis a Review the Entity List The active entity list is selected To use a different entity list cancel the operation select a different entity list in the Experiment Navigator and relaunch the operation b Click Choose to select the Experiment Interpretation By default the active inter pretation is already selected Figure 95 Log values for the conditions in the selected experiment interpretation are sent to IPA for analysis The name of the data set used in IPA is named after the source experiment in Mass Profiler Professional c Click OK x ii Differentiation by Infection and Treatment y Interpretations Qll All Samples ull Infection Treatment Non averaged Q Infection Treatment Cancel Figure 97 Choose Interpretation dialog box d Review the IPA Server Address Type in the address for the IPA server for exam ple analysis ingenuity com Type the name for the project to be created in Project Name By default the name used for the experiment that was originally selected is used The Project Name is used by IPA under to store the pathway information Note PA only allows unique names for each data set per project To analyze the same entity list more than once change the name of the experiment or change the Project Name Select whether to Use both Direct and Indirect relationships for the
22. oder for the new entity to be added to the list The default value is gt 2 d Mark the types of entities to evaluate in Select entity type e Select the Limit analysis results based on Local connectivity Allows you to add a certain number of entities to the given network based on their rank with regards to local connectivity New entities are ranked with decreasing priority based on how many entities they are connected with within a given list of entities Local to global connectivity ratio A local global connectivity ratio is computed for each new entity Local connectivity is based on the number of entities to which it connects within a given list and global connectivity is the number of rela tions that it participates in within the entire database New entities are ranked 96 Integrated Biology operations NLP Networks with decreasing priority based on this local global connectivity ratio This is the default value f Type in the Maximum number of new entities to limit the number of entities to add to your network The default value is 50 If your Algorithm is Shortest Connect see Figure 125 on page 98 g Type in the Entity global connectivity for your analysis This is a filter that adds new entities to connect two disconnected network clusters based on the number global entities that must be connected to input entity list in oder for the new entity to be added to the list The default value is lt 500 h Mark th
23. via NLP in the Workflow Browser 3 Choose input parameters in Extract Relations via NLP Step 1 of 4 4 Review tagged content in Extract Relations via NLP Step 2 of 4 NLP Networks a Click Extract Relations via NLP in the Workflow Browser Extract Relations via NLP four 4 steps as shown in Figure 135 The new pathway list is placed in the Analysis folder within the Experiment Navigator Extract Relations via NLP Choose Type View Tagged Pathway View Save l of Input Data Content 3 of 4 Pathway List 1 of 4 2 of 4 4 of 4 Figure 135 Flow chart of the Extract Relations via NLP operation a Select the Input source If the chosen Input source is PubMed search you can specify a search query that is submitted to PubMed You can also choose to run NLP on your local files Non text format files such as doc and pdf files are converted into text using pub licly available converters b Type in your Search text memory or click Choose files depending on your Input source c Click Next Wlextract Relations via NLP Step 1 of 4 x Choose type of input data gt PubMed Search Provide a search term or a PubMed query phrase to run NLP on PubMed abstracts the number of abstracts fetched can be modified via Tools gt Options gt Pathway gt NLP limits gt PubmedFetchLimit gt Extract From Local Files Run NLP on local Text Pdf Doc Html or Medline XML files Input source PubMed search v Se
24. 11 Variation AW cef E 31 Variation CN cefi 1 1 Variation AW Set A 31 Variation C N Set C 12 Variation AW cef E 32 Variation CN cef 12 Variation AW Set A 32 Variaton CN Set C E 13 Variation AW cef E 33 variation CN cef 13 Variation AW set A 33 Variation CN Set C leh 14 variation aw cef E 34 Variation CN cef 14 Variation AW Set A 34 Variation CN Set C B 15 variation AW cef E 35 variation CN cef 15 Variation AW Set A 35 Variation CN Set C E 16 Variation AW cef E 36 Variation CN cef 16 Variation AW Set A 36 Variation CN Set C E 17 Variation Aw cef E 37 Variation CN cef 17 Variation AW Set A 37 Variation CN Set C S 18 Variation Aw cef 38 Variation CN cef 18 Variation AW Set A 38 Variation CN Set C E 19 Variation AW cef E 39 Variation CN cef 19 Variation AW Set A 39 Variation CN Seic eee 20 Variation AW Set A 40 Variation CN Set C 20 One variable experiment sample list and file list Example experiments a i Va m a n f Ei i IG i I e Features of the example mass spectrometry experiments The two variable experiment presents an analysis of a metabolomic response to changes in two independent variables parameters each with two parameter val ues The parameter values of the first parameter represent a control data set associ ated with the organism without perturbation and when the organism was subject to a known perturbation The parameter values of the second parameter represent a pair of metabolite ext
25. Analysis Type is Simple Go to step 3 Select matching entities in NLP Network Discovery Step 2 of 5 if your Analysis Type is Advanced Elnr Network Discovery Step 1 of 5 Input Parameters Choose the algorithm for pathway analysis The simple algorithms have preset defaults to quickly create a pathway view for Further investigation The advanced algorithms provide the ability to specify several Filter criteria For entities and relations IF the pathway analysis is triggered From the workflow browser the active entity list is chosen by default and can be changed here If the pathway analysis is triggered from selected entities of a pathway view the choice of entity list is not necessary ix Input List IDB Metlin Fold change gt 2 0 H i Analysis Type Simple X Algorithm Direct Interactions RA Next gt gt Finish Cancel Figure 121 Input Parameters page NLP Network Discovery Step 1 of 5 3 Select matching entities in This step is only encountered if you selected Advanced for the Analysis Type in NLP Network Discovery Input parameters in NLP Network Discovery Step 1 of 5 If the algorithm you Step 2 of 5 selected does not find any entities that meet the algorithm criteria you are prompted to select a different algorithm or another input list for analysis a Review the matched not matched and redundant entities and their related statis tics b Select any or all of the entities to use in
26. Choose IPA Analysis to run Perform Data Analysis on Experiment Perform Data Analysis on Entity List e Corcel Figure 90 Choose Experiment dialog box 11 Integrated Biology operations 3 Enter the options for Create New Pathway Pathway Analysis a Click Choose to select the Entity List By default the active entity list is already selected Figure 91 b Click OK lchoose Entity List x Differentiation by Infection and Treatment B Analysis All Entities E Filtered on Flags accCalls P M filterCondition samples 2 Filtered by Frequency conditions 100 0 1 B 2way ANOVE E 2Way ANOVA p Corr Infection cut off p lt 0 05 E 2Way ANOVA p Corr Infection Treatment cut off p lt 0 05 2Way ANOVA p Corr Treatment cut off p lt 0 05 Union 2Way ANOVA cut off p lt 0 05 Figure 91 c Elcreate New Pathway i x Create New Pathway Save Pathway Choose Entity List dialog box Review the IPA Server Address Type in the address for the IPA server for exam ple analysis ingenuity com Type the name for the new pathway to be created in Pathway Name By default the name of the entity list that was originally selected is used If you selected a different entity list above the name for the pathway is not updated to reflect the new entity list selection Type the name of the Project Folder that is used by IPA for your analysis The default name
27. Click Next The pathway list is saved Al workflow Type Analysis Biological Significance Step 8 of 8 Single Experiment Pathway Analysis All available pathways From WikiPathways associated with the experiment organism are listed here along with p values the number of Matched Entities and the number of Pathway Entities of Experiment Type To import these pathways into the experiment click Finish To save selected pathways click Custom Save Open the resulting pathway list in the Pathway View to view the pathways and filter the pathway list by p value and minimum number of matches Steps 1 Summary Report 2 Experiment Grouping 3 QC on samples Pathway __ip vatuegagilent Single Color Demo Matched Entities gilent Single Color De Pathway Entities of Experiment Type gil Hs_Gastric_cancer_network 2 WP2363 5946 0 8 Hs_Glycogen_Metabolism_WP500_45329 8 Hs_ApoptosisWP254 41184 8B Paste Licata Hs_Non homologous_end_joiningWP438 45348 Pf B TE eee Hs_Androgen_receptor_signaling_pathway WP138_4797 0p y A D Hs_Ganglio_Sphingolipid_Metabolism_WP1423 45306 Pf Hs_Signaling_of_Hepatocyte_Growth_Factor_Receptor WP313_45129 8 Hs BDNF_Pathway WP2152 59221 0 ft He SIgnBing 1by_Ro00 Fes ept0r WPL918 5209 a OQ E Hs_Cytokines_and_Inflammatory Response_WP530 53161 0 0030932097 8 Hina arpieaton WPL908 95229 0 ggg Hs_Oncostatin_M_Signaling_Pathway WP2374 54418 5138888 ain a E E a Hs_Int
28. Corr l 75 75 6 530945E 14 My Two Variable Experi 2 Way ANOVA p Corr l 75 75 6 530945E 14 p value cut off H 0 05 Finish Cancel Figure 37 Find Similar Entity Lists Results page Find Similar Entity Lists Step 3 of 3 3 Add or edit descriptive information that is stored with the saved entity list in the Name Notes and Experiments fields on the Significant EntityLists page Figure 39 on page 44 4 Click Configure Columns to add remove and reorder the columns in the tabu lar presentation of the entities This opens the Select Annotation Columns dialog box BER Annotation Columns a x rSelect Annotation Columns Selected items Available items Alignment Value ChEBI ID Q CompositeSpectrum S Compound Name KZ Compound lgo Frequency JV Save as default Default for MassHunterQual LCMS_UNIDENTIFIED_COMPOUNDS Default for MassHunterQual LCMS_UNIDENTIFIED_COMPOUNDS My Two Variable Experiment_2011_Dec_19_15_56_0 Cancel Figure 38 Select Annotation Columns dialog box 5 Select column items to add or to remove from the saved entity list 6 Reorder the selected columns to your preference 7 Mark Save as Default if you would like this configuration to be saved as the default for future save entity list steps 8 Select the experiment type for your configuration to be applied 9 Click OK 10 Click OK The entity lists are saved in a folder named Custom
29. Enter a value for Max results per page to adjust how you plan to review the entity lists on the next step of the wizard g Click Next ElEntityList Search Wizard Step 1 of 2 x Advanced Search Parameters Build the search query by specifying the search field condition and value You can add multiple search queries which you can combine using AND or OR operations Please specify the max search results per page Search Conditions Add Remove Search Field Condition Search Value Number of entities Creation Date g February 2013 gt Mon Tue Wed Thu Fri Sat 5 6 12 13 14 15 16 19 20 21 22 23 27 2 1 2 Today None Show User Attributefs Combine search conditions by AND BA Max results per page 100 inish Cancel Figure 34 Advanced Search Parameters page EntityList Search Wizard Step 1 of 2 with two filter criteria the last one requiring the selection of a date a Review the entity lists that met your search criteria b Click Back if you want to adjust and rerun your search criteria c Click the forward and back buttons as necessary to review all of the search results d Select any or all of the entity lists to return the entity list s to the page Find Sim ilar Entity Lists Step 2 of 2 When an entity list is selected the row is high lighted Select a continuous range of entity lists click on the first file and press Shift and click on the last entity list that includes t
30. If your GeneSpring GX module license does not include Connect to Cytoscape contact Agilent support click Help gt Contact Technical Support on the menu bar for assistance Connect to Cytoscape is a separate feature and can only be accessed with a valid GeneSpring GX module license See Getting started requirements on page 62 85 Integrated Biology operations 1 Determine if Connect to Cytoscape is an active fea ture of MPP 2 Enter the Cytoscape Installation Path Pathway Analysis The Connect to Cytoscape operation does not have an intermediate wizard or dialog box like the other Pathway Analysis operations If Connect to Cytoscape is an active feature in your installation Mass Profiler Professional immediately starts transfer ring the entity list from your active experiment and launches Cytoscape when the operation is invoked It is recommended to review all of the steps in this operation before selecting the Connect to Cytoscape operation to make sure your installation of MPP and Cytoscape are enabled to work together Connect to Cytoscape is a separate feature and can only be accessed with a valid GeneSpring GX module license This step determines if your GeneSpring GX license includes Connect to Cytoscape a Click Tools gt Options on the menu bar to launch configuration options ElmMass Profiler Professional Agilent Single Color Demo Project Navigator Project Search view Annotations Windows He
31. N 0 374 C4H7N 76 08 69 0587 a 5 14 Cpd 14 Cycloleucine C6 H1 Cycloleucine C6H11N 02 47 4 129 0788 15 Cpd 15 C12 H12 N2 03 53 C12 H12 N2 03 76 64 327 9993 16 Cpd 16 C18 HE N2 010 0 3 C18 HE N2 010 69 98 410 004 E 17 Cpd 17 1 Phosphatidyl 1D 1 Phosphatidyl 1 C11 H20 016 P2 57 62 470 0239 g H 18 Cpd18 C21 HE N2 012 5 0 C21 HE6 N2 0125 61 96 509 9636 19 Cpd19 C16 H5 CI OG 0 3 CIBHSCIOBS 85 89 359 9496 a gt 20 Cpd 20 Prepacifenol epoxid Prepacifenol epo C15H21 Br2 Cl 54 84 441 9525 4 21 Cpd 21 6 Mercaptopurine ti 6 Mercaptopurin C10H15N4 071 46 51 523 9537 5 22 Cpd 22 C16 H18 N2 015 54 C16 H18 N2 01 80 4 605 9579 F Figure 52 ID Browser user interface after completing the Compound Identification Wizard 6 Review results and enter a Review the content and parameters in the EntityList Inspector dialog box information In the Entity The information and content in the EntityList Inspector dialog box are the same List Inspector dialog box for many operations within Mass Profiler Professional that end with a Save Entity List page The figures and description presented in this step are identical to those in other operations You are referred back to this section when you are prompted 52 Integrated Biology operations Results Interpretation to save your entity list at the completion of other operations available in the Workflow Browser ElEntitylist Inspecto
32. P M FilterCondition samples Created from Significance Testing and Fold Change workflow step Filter Compounds Entity Filter By Frequency with cut off percentage 100 0 Entity List Filtered on Flags accCalls P M FilterCondition samples 2 Interpretation Filtered on Flags accCalls P M filterCondition samples Created from Significance Testing and Fold Change workflow step Filter Compounds Entity Filtered by Frequency conditions 100 0 1 Created From Significance Testing and Fold Change workflow step Filter By Frequency Enti Filtered on Flags accCalls P M filterCondition samples Created From Significance Testing and Fold Change workflow step Filter Compounds Entity T test p lt 0 05 Created From Significance Testing and Fold Change workflow step significance analysis Entit 42 Remove List lt lt Back Next gt gt Finish Cancel Figure 36 Choose Entity Lists page Find Similar Entity Lists Step 2 of 3 Integrated Biology operations Results Interpretation 7 Select and save entity lists a Review your results based on significance in Find Similar Entity Lists Step 3 of 3 b Optional Select one or more entity lists to save them as a custom entity list Save a custom entity list 1 Click one or more entity lists See Review the search results in EntityList Search Wizard Step 2 of 2
33. _and_ketone_body_meta Hs_Nucleosome_assembly_wWP1874_42092 Hs_Signaling_by_EGFR_WP1910_45218 Hs_Interferon_Signaling_wWP1837_46942 Hs_Nucleotide_Excision_Repair_wWwP1980_42219 Hs_Interleukin 1_signaling_WP1839_44873 Hs_Metabolism_of_amino_acids_and_derivatives_WP18 Hs_Factors_involved_in_megakaryocyte_development_a i KF 4 g plell elololelolnloln eleele pjlojwlojojololelelelcelejojojojo Gustom Save lt lt Back F Finish Cancel Figure 86 Multi Omic Results page Multi Omic Analysis Step 3 of 4 14 Integrated Biology operations 5 Enter save pathway list parameters in Multi Omic Analysis Step 4 of 4 Pathway Analysis a Review your pathway list results b Add or edit descriptive information that is stored with the saved pathway list in the Name and Notes fields c Click Next The MOA results are assigned a new project in the Project Navigator and the MOA pathway lists are placed in the Analysis folder within the Experi ment Navigator The Mass Profiler Professional Display Plane returns showing your entities and associated pathways in the Pathway View as shown in Figure 88 on page 6 See section 11 3 5 Working with Pathway Lists in the Mass Profiler Professional User Manual for more information about navigating the Pathway View ElMulti Omic Analysis Step 4 of 4 j x Save Pathway List The Pathway List resulting from th
34. and Marginal means the signal for the entity was saturated A classification of compounds based on their biological purpose or activity A proposition made to explain certain facts and tentatively accepted to provide a basis for further investigation A proposed explanation for observable phenomena 113 Reference information Hypothetical ID Browser Identified compound Independent variable Inorganic compound Interpretation Lipidomics Mass variation Mean Metabolism Metabolite Definitions may or may not be supported by the analytical data Statistical data analysis is per formed to quantify the probability that the hypothesis is true Also known as the sci entific hypothesis A statement based on involving or having the nature of a hypothesis for the pur poses of serving as an example and not necessarily based on an actuality Agilent software that automatically annotates the entity list with the compound names and adds them to any of the various visualization and pathway analysis tools Chromatographic components that have an assigned exact identity such as com pound name and molecular formula based on prior assessment or comparison with a database See also Unidentified Compound An essential element constituent attribute or quality in a data set that is deliber ately controlled in an experiment For example a pharmaceutical compound struc ture and quantity may be controlled as two independe
35. compensate for excessive drift in the acquisition parameters The best results are achieved by maintaining your instrument and using good chromatography Improved data quality for your analysis comes from matching the sampling method ology to the experimental design so that replicate data is collected to span the attri bute values for each condition A larger number of samples appropriate to the population under study results in a better answer to the hypothesis An understand ing of the methodologies used in sampling and using more than one method of sam ple collection have a positive impact on the significance of your results 15 Working with Mass Profiler Professional Advanced operations covered in the MPP workflow guides In many cases the example data used in this workflow is processed using the metabolomics workflow before being analyzed using the integrated biology opera tions Familiarity with the terminology and steps described in the Agilent Metabolo mics Workflow Discovery Workflow Guide with help you use this workflow guide and the advanced operations used in integrated biology Figure 4 shows a summary of the Metabolomics Discovery Workflow and the advanced operations covered by the both the metabolomics and the integrated biology workflow guides Metabolomics Discovery Workflow Optional Recursive Acquire data find features a cs TS Advanced Operations covered Advanced Operations covered in
36. data from the Two variable experiment to pro vide an overview of the wizard options The data Is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The Export for Identification operation has one 1 step as shown in Figure 56 Export for Identification Browse Figure 56 Flow chart of the Export for Identification operation Choose Entity List dialog box Choose a file dialog box a Click Choose in the Export dialog box b Select the entity list to export 04 Integrated Biology operations 3 Enter the export file name and folder Export Inclusion List 1 Launch Export Inclusion List in the Workflow Browser Results Interpretation Since this is an identification operation you do not need to select an entity list that is at least Filtered on Flags from the entity lists in the Choose Entity List dia log box c Click OK aj Export x Export Exports compound information in Compound Exchange Format CEF file Entity List Fittered on Flags acc Choose Output File Data Export for Identification cef Browse Ook Cancel Filtered List on Mass gt 125 Mass lt 200 E Filtered List on Annotations equals C ga My Favorites Help Cancel Figure 57 Export and Choose Entity List dialog boxes a Click Browse in the Export dialog box
37. document is provided as is and is subject to being changed without notice in future editions Further to the maximum extent permitted by applicable law Agilent disclaims all warran ties either express or implied with regard to this manual and any information contained herein including but not limited to the implied warranties of merchantability and fitness for a particular purpose Agilent shall not be liable for errors or for incidental or consequential damages in connection with the furnishing use or performance of this document or of any information contained herein Should Agilent and the user have a separate written agreement with warranty terms covering the material in this document that conflict with these terms the warranty terms in the separate agreement shall con trol Technology Licenses The hardware and or software described in this document are furnished under a license and may be used or copied only in accor dance with the terms of such license Restricted Rights If software is for use in the performance of a U S Government prime contract or subcon tract Software is delivered and licensed as Commercial computer software as defined in DFAR 252 227 7014 June 1995 or as a commercial item as defined in FAR 2 101 a or as Restricted computer soft ware as defined in FAR 52 227 19 June 1987 or any equivalent agency regulation or contract clause Use duplication or disclo sure
38. experiment chapter in the Agilent Metabolomics Workflow Discovery Workflow Guide an ideal experiment involves at least ten 10 replicates for each parameter value Thus an ideal experi ment with a single parameter and four parameter values has a data sample size of at least forty 40 samples In this example the minimum sampling conditions are met In the experiment sample list shown in Figure 7 the parameter values for the inde pendent variable are listed in the Group ID column Since sample names are derived from your actual data file names CEF files in this example it is recommended to develop a concise meaningful file naming convention for your experiment Figure 7 01 Control Control 21 Variation BR Set B 02 Control Control 22 Variation BR Set B Ph oicontolcet Gl 21 variation BR ce 03 Contral Con trol 23 Variation BR Set B E 02 Control cef ee 22 Variation BR ce 04 Control Control 24 Variation BR set B eio3 control cef E 23 Variation BR ce 05 Contral Control 25 Variation BR Set B E04 ontrol cef E 24 Variation BR ce 06 Control Control 26 Variation BR Set B si 05 control cef E 25 Variation BR ce 07 Control Control 2T Variation BR Set B Ej 06 Control cef E 26 Variation BR ce 08 Control Control 28 Variation BR Set B 07 Control cef 27 Variation BR ce 09 Control Control 29 Variation BR SetB amp 08 Control cef 28 Variation BR ce 10 Control Control 30 Variation BR Set B Era ipsa nif ce Sas ee i E
39. experiment organized into sequential s 4 Control ist of the experiments within B gt conan p groups of operations for the analysis f5 p the current project 3 4_Control_250 Analysis a S OA Statistical Analysis 2 3_Infected_000 10 Filter on Volcano Plot 2 4_Infected_000 Fold Change 4 1_Infected_250 Clustering 1o iese 20 j Po Sere 4 4_Infected_zso m x a s ilter On Parameters Interpretations 5 Principal Component Analysis All Sampl ey a a AR Desktop Area Fd is nes Paella 3 Interactive views for selections Cass Prediction 7 or 3 made in the Experiment Navigator xae a E om a bs CENA 3 ie Run Prediction Filtered on Flags accCalls P M filte N f 4 wh 2 Fitered by fr and the Workflow Browser PPG V d S S F Filtered by fre M Export Prediction Model 2 Carey Mo Experiment Navigator i 7 1 T wa Ora ye X Filter req F j i A g k 4 f pS Mo 4 i l s a CJ 2way ANOVA q i i i BA AER Leet E AEF EVN W o 3 AEDE Wee we i AS a A Seana ae a ance Lists samples inte rpretations i VA Ye iN i ii N i i YA i ja e B Feauesoeag analyses and favorites within EES i a SS S E e E e rows Identiication My Favorites n ie P j i 7 7 t Export for Identification the selected experiment ul zF i Sais Wie J ih Import Annotations 4 i v yi i Pathway Analysis a obal Lists yi Single Experiment Analysis a My Lists Multi Omic Analysis Launch IPA a Export to MetaC
40. is formed from the sum of the individual ion abundances within the compound spectrum at each retention time in the specified time window The compound volume generated by MFE is used by Mass Profiler Professional to make quantitative comparisons A sequence of dialog boxes presented by Mass Profiler Professional that guides you through well defined steps to enter information organize data and perform analy Ses 119 Reference information References Manuals Primers Application Notes References This section consists of citations to Agilent manuals primers application notes pre sentations product brochures technical overviews training videos and software that help you use Agilent products and perform your metabolomics analyses e Agilent G38354A MassHunter Mass Profiler Professional Quick Start Guide Agilent publication G3835 90009 Revision A November 2012 e Agilent G3835AA MassHunter Mass Profiler Professional Familiarization Guide Agilent publication G3835 90010 Revision A November 2012 e Agilent G3835AA MassHunter Mass Profiler Professional Application Guide Agilent publication G3835 90011 Revision A November 2012 e Agilent Metabolomics Workflow Discovery Workflow Guide Agilent publication 5990 7067EN Revision B October 2012 e Agilent Metabolomics Workflow Discovery Workflow Overview Agilent publication 5990 7069EN Revision B October 2012 e Agilent Mass Profiler Professional Ag
41. list 22 Example experiments Creating an expression analysis using the sample array experiment Creating an expression analysis using the sample array experiment The workflow for importing and performing an initial analysis of gene probe data is different from the workflow used for mass spectral data as described in the Agi ent Metabolomics Workflow Discovery Workflow Guide This section guides you through steps necessary to import and prepare the Agilent Expression Single Color Demo sample data installed with Mass Profiler Professional to demonstrate some of the advanced operations in this workflow MPP is used to import organize and analyze the data you acquired An experiment based on Expression selected for the Analysis type using the Agilent Expression Single Color Demo sample data includes the following steps 1 create a project and experiment 2 import your data 3 create your initial analysis and 4 perform advanced analysis operations Figure 10 shows these steps to prepare the Agilent Expression Single Color Demo sample data to become familiar with the integrated biology operations The Analysis Biological Significance wizard guides you through eight 8 steps to Organize and enter parameters and values that improve the quality of your results and produce an initial differential expression of the sample data The steps per formed during the Analysis Biological Significance wizard are illustrated in Figure 11 on pa
42. or samples to be used in this experiment Click Finish when all data files or samples have been added 2 us22502705_251209747382_Untreated txt US22502705_251209747392_Treated txt U522502705_251209747393_Treated txt US22502705_251209747394_Untreated txt US22502705_251209747404_Treated txt Choose Samples Reorder Remove OK Cancel Figure 18 Experiment Selection Dialog dialog box 26 Example experiments Creating an expression analysis using the sample array experiment Do Significance Testing and The Analysis Biological Significance wizard starts if Analysis Biological Signifi Fold Change cance was selected as the Workflow type in the New Experiment dialog box Figure 15 on page 25 1 Review the summary report a Review the data change the plot view export selected data or export the plot to in the Analysis Biological a file click and right click features available on the plot Significance Step 1 of 8 b Click Next wizard Al workflow Type Analysis Biological Significance Step 1 of 8 x Steps Summary Report The distribution of normalized intensity values for each sample is displayed in the box whisker plot Entities with intensity values beyond 1 5 times the inter quartile range are shown in red If there are more than 30 samples in the experiment a table with all samples will be shown instead of the box whisker plot 2 Experiment Grouping L Agilent Expressi
43. order to proceed at least one parameter with two values must be assigned 21 Example experiments Creating an expression analysis using the sample array experiment Click Add Parameter b Click the Load experiment parameters from file button to apply a previously created experiment grouping associated with the sample data c Select the file EXPERIMENT PARAMETERS can be loaded from file tsv d Click Open The sample files are automatically grouped and assigned parameter names and parameter values workflow Type Analysis Biological Significance Step 2 of 8 Experiment Grouping Experiment parameters define the grouping or replicate structure of your experiment To enter experiment parameters click Add Parameter You can enter as many parameters as you want but only the first two parameters are used for analysis in this Guided Workflow Other parameters can be used when analyzing your data with the Features available from the Workflow Browser You can also edit and re order parameters and parameter values here Steps 1 Summary Report Displaying 6 sample s with O experiment parameter s To change use the button controls below 3 QC on samples Lh Bae EEE 4 Filter Probesets 5 Significance Analysis 6 Fold Change 7 GO Analysis 8 Single Experiment Pa Look in d Agilent Expression Single Color Demo F Sa EE EXPERIMENT PARAM
44. processes The molecules and processes are depicted as Entities and their biological interactions as Relations In a pathway view entities form the nodes of the graph and the lines depict the relations An organism entity database consists of proteins small molecules processes functions enzymes complexes and families Proteins are organism specific while the other entities of the organism are largely organism independent The Interaction Database is organized in a hierarchical fashion with two levels The top level is generic and contains information that is common across organisms The second level comprises the various organism specific entities predominantly pro teins and relations specific to the organism The public sources used by the interac tion databases is described in section 12 2 2 Database Entities in the Mass Profiler Professional User Manual 92 Integrated Biology operations NLP Network Discovery 1 Launch NLP Network Dis covery in the Workflow Browser NLP Networks You can download and update Interaction Databases from the Agilent Server or with a Mass Profiler Professional update file If you are working with an organism that is not currently available in Mass Profiler Professional you can create a new organism click Annotations gt Create Pathway Organism Valid taxonomy IDs can be found at the Entrez Taxonomy database site http www ncbi nlm nih gov taxonomy See Create Pathway Organism o
45. project If available Click Choose to change the Experiment b Click Choose to select Experiment 2 The experiment selected for Experiment 2 must be different from the experiment selected for Experiment 1 71 Integrated Biology operations Pathway Analysis Choose Experiment x Experiments Wii Differentiation by Infection and Treatment Mione variable Data Set lii One Variable Human Cancel Figure 83 Choose Experiment dialog box c Review the Organism specified in Experiment 1 and Experiment 2 If the speci fied organism is not specified or incorrect you can change the Organism for the experiment in How to change the organism for an existing experiment on page 63 d Select the pathway organism in Choose Pathway Organism You can choose an organism for finding matched pathways that is different from the organism of the selected experiments Selecting a different organism is useful when the organism specified in the experiment is less or not sufficiently described in the literature or when you want to observe the effects of one organ ism s pathogen metabolite in another organism By default the Choose Pathway Organism selected is that associated with the Experiment e Select the pathway source for your analysis The following pathway sources are available for Curated pathways only e WikiPathways Analysis e WikiPathways Reactome WikiPathways GenMAPP WikiPathways Other e BioCyc MetaC
46. see Figure 64 x ld lz Export Inclusion List csv Microsoft Excel OVX Home Insert Page Layout Formulas Data Review View Add Ins 9 o o ep X C1 v fe v A o _ D E F G H iz 1 TargetedMsMsTable O 2 On Prec m z Z Ret time min Delta ret time min 3 TRUE 159 97652 1 0 134 0 25 4 TRUE 100 94342 1 0 13059999 0 25 TRUE 61 125767 1 0 13599999 0 25 6 TRUE 115 95595 1 0 1448 0 25 7 TRUE 230 9026 1 0 1448 0 25 8 TRUE 90 948166 1 0 14400001 0 25 g TRUE 238 01323 1 0 14685714 0 25 10 TRUE 205 98772 1 0 14750001 0 25 ll TRUE 410 96814 1 0 14750001 0 25 12 TRUE 142 953 72 1 0 15311112 0 25 13 TRUE 233 98253 al 0 1495 0 25 1A TEJ IF 79592 1 011395715 01475 x 4 4 gt gt Export Inclusion List 3 4 Illl gt Ready Emm 100 g Figure 64 Contents of an Export Inclusion List CSV file Th is operation imports annotations from an identified CEF file and applies the anno tations to matching entities in your experiment When you invoke this operation you select a CEF file and update annotations for compounds whose Mass Profiler Pro fessional ID match that of the compounds in the imported CEF file All entity lists in your experiment are updated a Click Import Annotations in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data is initially imported and analyzed following the Agilent Metabolomics Workflow Discove
47. the Entity List to be Identified dialog box b Select the entity list to identify Since this is an identification operation you do not need to select an entity list that is at least Filtered on Flags from the entity lists in the Choose Entity List dia log box c Click OK d Click OK to launch ID Browser and transfer the entity list for identification This action can take extra time and displays a progress status box while ID Browser is starting El choose the Entity List to be identified Choose the Entity List to bei Filtered by Frequency Choose Cancel Differentiation by Infection and Treatment QB Analysis S E all Entities Filtered on Flags accCalls P M filterCondition samples 2 edF iltered by frequency conditions 100 0 1 2way ANOVA 2Way ANOVA p Corr Infection cut off p lt 0 05 2Way ANOVA p Corr Infection Treatment cut off p lt 0 05 2Way ANOVA p Corr Treatment cut off p lt 0 05 Union 2Way ANOVA cut off p lt 0 05 my Favorites Cancel Figure 46 Choose the Entity List to be Identified and Choose Entity List dialog boxes 48 Integrated Biology operations 3 Enter the compound selection and identification methods in Compound Identification Wizard 4 Set up the identification techniques in Compound Identification Wizard Results Interpretation When Mass Profiler Professional launches ID Browser the Co
48. then return to this step g Click Next A progress status box is displayed while the pathways are searched based on the organism lmutti omic Analysis Step 1 of 4 Input Experiments The active experiment in the open project is set as Experiment 1 by default Select the second experiment for Multi Omic Analysis choose a Pathway Organism and the sources from which you want to match pathways For the selected organism Any pathway organism can be selected from the drop down regardless of the organisms associated with the chosen experiments for example if you know that your research area is more extensively described in another organism Experiment Chooser Experiment 1 Experiment 1 Differentiation by Infection and Treatment Choose Organism Homo sapiens Experiment 2 Experiment 2 Jone Variable Data Set Choose Organism 2 Rattus norvegicus Choose Pathway Organism Homo sapiens v Literature Derived Networks only C Both Curated pathways JV WikiPathways Analysis 0 pathways JV wikiPathways Reactome 121 pathways JV WikiPathways GenMAPP 0 pathways JV WikiPathways Other 157 pathways 7 BioCyc 295 pathways 7 BioPAX Imported 0 pathways 7 GPML Imported 0 pathways 7 Hand created 0 pathways Legacy 0 pathways lt Bach Next gt gt Finish Cancel Figure 84 Input Experiments page Multi Omic Analysis Step 1 of 4 a Select an interpretation for Choose In
49. your data analysis using Mass Profiler Professional it is recommended that you follow the procedures in the chapter Create an initial analysis in the Agilent Metabolomics Workflow Discovery Workflow Guide before proceeding with the operations available in the integrated biology operations When you click Finish during Create an initial analysis see Figure 5 on page 17 Mass Profiler Professional automatically makes the operations available under the Work flow Browser you have access to all available operations Only some of the operations available in the Workflow Browser are documented in this workflow guide ntegrated Biology with Mass Profiler Professional Workflow Guide This workflow documents the operations that are most relevant to performing your integrated biology analysis e Results Interpretations see Results Interpretation on page 38 e Pathway Analysis see Pathway Analysis on page 61 e NLP Networks see NLP Networks on page 92 The Agilent Metabolomics Workflow Discovery Workflow Guide documents the general experimental data quality and statistical analysis operations e Experiment Setup e Quality Control e Analysis The operations associated with Class Prediction and utilities are documented in Class Prediction with Mass Profiler Professional Workflow Guide More information regarding any of the operations available in the Workflow Browser is found in the Mass Profiler Professiona
50. 0 My Two Variable Experi 2Way ANOVA p Corr 238 238 1 44E 43 My Two Variable Experi Filtered by frequency c 1220 1220 0 0 My Two Variable Experi Filtered by frequency c 1220 1220 0 0 My Two Variable Experi 2 Way ANOVA p Corr 330 330 0 0 My Two Variable Experi 2 Way ANOVA p Corr dl 49 49 2 614524E 9 p value cut off H 0 05 Custom Saye lt lt Back Next gt gt Finish Cancel Figure 40 Choose Entity Lists page Find Similar Entity Lists Step 3 of 3 44 Integrated Biology operations Export for Recursion Recursive finding 1 Launch Export for Recur sion in the Workflow Browser 2 Select the entity list to export Results Interpretation Export the entities in a selected entity list to a CEF file Compound Exchange For mat The entities exported to a CEF file are used by Agilent MassHunter Qualitative Analysis to find targeted features the exported entities from your original sample data files Recursive feature finding combined with replicate samples improves the statistical accuracy of your analysis and reduces the potential for obtaining a false positive or false negative answer to your hypothesis MassHunter Qualitative Analysis Find Compounds by Formula FbF typically uses molecular formula information to calculate the ions and isotope patterns derived from the formula as the basis to find features in the sample data file When the inpu
51. 06203 A_23_P100642 68 Matched 24 Matched jA_23_P111583 1167 Matched JA 23_P79108 A_23_P205480 A23 P135123 ATPSB3 SLC7AB Protein 0 75 Matched Matched Not Matched A23 P8812 Not Matched JA 23 P95619 A23_P117828 JA_23_P211661 Not Matched Not Matched Not Matched JA_23_P39494 Not Matched Not Matched 23_P10663 Not Matched jA_23_P34818 Not Matched JA 23_P15226 Not Matched A_23_P414793 Protein 5 Redundant A_23_P367978 Protein 559 Redundant JA 23_P163782 Protein 4 Redundant Protein 30 Redundant Protein 42 Redundant Protein 173 Redundant lt lt Back Next gt gt Finish Cancel Figure 122 Matching Statistics page NLP Network Discovery Step 2 of 5 This step is only encountered if you selected Advanced for the Analysis Type in Input parameters in NLP Network Discovery Step 1 of 5 a Type in the Relation score for your analysis The score is a value between 1 and 10 with 10 indicating the highest score the best quality The default value is gt 9 b Mark the relations to include in your analysis in Select relation type If your Algorithm is Expanded Interactions see Figure 124 on page 97 c Type in the Entity local connectivity for your analysis This is a filter that specifies the number of entities in the input list that a new entity must be connected to in
52. 468 11 823 0 470 938 8519 449 9422 0 __ FindByMolecu 9 971 9349 0 0 000 7 774 15 748 3 753 972 94305 971 9249 0 FindByMolecu 7 zi kA Profile plot Log2 Normalized B Spreadsheet Log2 Normalized x Experiment Setup Quality Control Analysis Class Prediction Results Interpretations Find Similar Entity Lists Export for Recursion IDBrowser Identification Export For Identification Export Inclusion List Import Annotations Pathway Analysis Single Experiment Analysis Multi Omic Analysis Launch IPA Export to MetaCore Connect to Cytoscape NLP Networks NLP Network Discovery MeSH Network Builder Extract Relations via NLP Utilities Remove Entities with missing Analysis Significance Testing Class Prediction Build and Te Rows 1277 0 selected Columns 10 0 selected G Infected o 14 892534 Figure 55 Spreadsheet view of identified entities in MPP after ID Browser Export for Identification 1 Launch Export for Identifi cation in the Workflow Browser 2 Select the entity list to export For an unidentified experiment this operation allows you to save selected entities for identification with another program Export the entities in a selected entity list to a CEF file Compound Exchange Format a Click Export for Identification in the Workflow Browser This operation is illustrated with
53. 5 97 9688 0 FindByMolecu 15 Find Similar Entity Lists Gill Infection Treatment Non averaged 142 011880 6 880 0 007 0 754 0 330 285 0331 142 0118 0 FindByMolecu 15 rre fao ran Ql Infection Treatment 69 058880 0 040 5 705 0 148 5 664 70 06611 69 0588 0 FindByMolecu 14 SEEN E Analysis 129 0788 0 0 217 0 136 0 392 0 095 130 08607 129 0788 0 FindByMolecu 16 TDErowser Identification 5E all Entities 328 0003 0 3 664 3 656 0 501 3 923 329 00735 328 0003 0 FindByMolecu 13 Export for Identification E Fitered on Flags accCals M fiten 410 0033 0 0 178 0 024 0 525 0 508 821 0095 410 0033 0 FindByMolecu 16 Sipe induson tist 492 0059 0 0 566 0 114 3 452 7 933 493 01346 492 005 9 0 FindByMolecu 13 eine iy an ANOVA 509 9653 0 7 117 3 361 0 347 7 162 510 97165 509 9653 0 FindByMolecu 11 EY 2Way ANOVA p Corr Infe 359 95150 0 191 0 250 4 077 0 063 360 95758 359 95 15 0 FindByMolecu 15 Sway ANOVA p Corr Infe 441 952680 0 097 3 509 11 379 3 640 442 96072 441 9526 0 FindByMolecu 11 Pathway Analysis 2Way ANOVA p Corr Trea 523 955200 0 223 0 103 3 801 0 205 524 963 13 523 9552 0 FindByMolecu 15 Single Experiment Analysis LEB Union 2Way ANOVA cut off 605 9604 0 0 282 7 556 7 505 11 541 606 967 1 1605 960480 FindByMolecu 9 ik enei Ga My Favorites 687 9609 0 0 095 3 763 3 674
54. 54 536 0132 0 FindByMolecu 10 T a E 433 9582 0 15 977 0 312 0 788 0 325 434 965 24 433 9582 0 FindByMolecu 12 695 9493 0 7 760 0 387 0 579 7 940 696 9534 695 9493 0 FindByMolecu 8 955 9598 0 11 167 3 619 1 009 7 461 956 9667 955 9598 0 FindByMolecu 10 po S31 94610 16 623 0 456 12 252 0 235 1081 9153 531 9461 0 FindByMolecu 9 ipai q 693 980860 0 850 0 644 0 322 1 302 1405 988 693 9808 0 FindByMolecu 16 613 9494 0 15 935 3 481 7 486 0 187 1250 8909 613 9494 0 FindByMolecu 9 deraino ge erini Uae Meaney 611 9782 0 0 580 0 590 0 273 1 101 612 9852 611 9782 0 FindByMolecu 16 529 9749 0 0 493 0 532 0 310 1 005 530 98236 529 9749 0 FindByMolecu 16 873 96 0 3 0 102 0 039 0 880 12 349 874 9675 873 96 0 3 FindByMolecu 13 791 9571 0 0 070 0 114 0 939 0 457 792 9644 791 9571 0 FindByMolecu 16 449 9433 0 15 980 0 468 11 823 0 470 938 8519 449 9433 0 FindByMolecu 9 971 9349 0 0 000 7 774 15 748 3 753 972 94305 97 1 9349 0 FindByMolecu 7il kA Profile plot Log2 Normalized Spreadsheet Log2 Normalized x Rows 1277 0 selected Columns 10 0 selected 36 Compound 441 9526 0 3 189M oF 308M M Figure 45 Spreadsheet view of unidentified entities in MPP before ID Browser a Click Choose in the Choose
55. 915 622 9917 0 FindByMolecu 7 Dy C13 H16 Na 15 988 0 457 2 918 0 073 1032 9316 5 15 9601 0__FindByMolecu 11 Zooo A 14 889 0 342 1598 7 462 1360 9393 679 967 0 FindByMolecu 10 SS 357 985660 1 277 0 751 0 565 16 049 858 992 3 857 9856 0 _FindByMolecu 12 C My Lists 775 9828 0 1519 74 212 3 534 8 708 776 99 41 775 9828 0 FindByMolecu 12 C16 H18 N1 7 206 0 221 7 555 0 321 1274 9972 618 0169 0 FindByMolecu 12 C20 H24 07 10 915 0 029 711 248 0 088 1110 9854 536 0132 0 FindByMolecu 10 C14 H6 N6 15 977 0 312 0 788 0 325 434 96524 422 9582 0 FindByMolecu 12 C19 H20 02 7 760 0 387 0 579 7 940 696 9534 695 9492 0 FindByMolecu 8 955 9598 0 11 167 3 619 1 009 7 461 956 9667 955 9598 0 FindByMolecu 10 C14 H16 N2 16 623 0 456 12252 0 235 1081 9153 521 9461 0 FindByMolecu 3 m C20 H18 Ne 0 850 0 644 0 322 1302 1405 988 692 9808 0 FindByMolecu 16 C21H14 N2 715 935 3 481 7 486 0 187 1250 8909 613 9494 0 FindByMolecu 9 dreni sarkin Us amlee C16 H20 OL 0 580 0 590 0 273 1 101 612 9852 611 9782 0 FindByMolecu 16 C14 H18 N4 0 493 0 532 0 310 1005 530 98236 529 9749 0__FindByMolecu 16 873 96 0 3 0 102 0 039 0 880 12 349 874 9675 873 96 0 3 FindByMolecu 13 791 9571 0 0 070 0 114 0 939 0 457 792 9644 1791 9571 0 FindByMolecu 16 C11 H14 01 15 980 0
56. Analysis Provides you with a selection of the most common functional ities of network discovery The default settings for guiding you through the simple network discovery workflow include Advanced Analysis Enables you to change and specify the details at every step of the network discovery and pathway creation process c Select an Algorithm 1 If you selected Simple for the Analysis Type your options are Direct Interactions Find relations that connect the selected entities Network Targets and Regulators Find entities that are upstream and down stream of two or more entities from the original list Network Targets Find downstream entity targets that connect two or more entities from the original list of selected entities Network Regulators Find upstream entity regulators that connect to two or more entities from the original list of selected entities Network Binders Find entities that bind entities that are connected by binding interactions to two or more entities from the original list of selected entities Network Modifiers Find protein entities that are either regulators or targets of biochemical protein modifications of two or more proteins from the origi nal list of selected entities Transcription Regulators Find protein entities regulating mRNA expression of or whose expression is regulated by two or more entities from the origi nal list of selected entities Transport Regulators Find all compounds
57. Annotations Windows Help E e A a eA A TH gt na _ 1 1 Con 1 2_Con 1 3_Con Script Editor R Editor Import Pathways from WikiPathways Import Pathways From BioCyc gt Project Navigator iL IB Test 5j Ey Experiments ii IB Test 1 4_C Import Pathway From File gt Figure 72 Importing pathways from WikiPathways 2 Select Select Organism and then select the specific organism from the Choose Organism dialog box Selecting All Organisms downloads the path ways for all organisms available in WikiPathways and required additional time to complete A progress status box is displayed during downloading 65 Integrated Biology operations 3 Select the interpretation and entity list in Single Experiment Analysis Step 2 of 4 Pathway Analysis x Select the organism For which the pathways have to be downloaded Choose Organism All Organisms Select Organism Help Figure 73 Choose Organism dialog box 3 Review the pathways that were imported into Mass Profiler Professional in the Import Statistics dialog box Elimport Statistics l x The table below indicates the number of new pathways that were imported into GeneSpring for each organism and the number of potential duplicate pathways that were found to have the same name as pathways that already exist in GeneSpring If there are duplicate pathways click Resolve Duplicates to
58. BioPAX Imported e GPML Imported e Hand created e Legacy The following pathway sources are available for Literature Derived Networks only e NLP e MeSH term e The pathway sources include interaction networks you imported or created using the NLP Network Discovery MeSH Network Builder or Extract Rela tions via NLP operations in the Workflow Browser If you select Both then all of the Curated pathways and Literature Derived Net works pathway sources are available Mark the Curated pathways and or Literature Derived Networks to include in your analysis The number of pathways previously imported into Mass Profiler Professional for each of the sources for the selected pathway organism Is dis played in parentheses next to the source name The number of pathways auto matically updates when you choose a different pathway organism for your analysis If the number of pathways previously imported into Mass Profiler Professional is reported as zero 0 for your organism among the sources click Cancel and import pathways for your organism To import pathways for an organism from WikiPathways follow steps in How to import pathways from WikiPathways and then return to this step You can import organism specific pathways into Mass Profiler Professional from WikiPathways 1 Click Tools gt Import Pathways from WikiPathways on the menu bar A prog ress status box Is displayed while the content is updated Project Search View
59. C13 H3 N 04 0 000 14 963 0 000 19 572 238 01323 237 0059 0 FindByMolecu E 141 9465 0 15 854 0 102 15 923 5182 142 95372 141 9465 0 FindByMolecu 3 C5 H3 N3 065 4843 0 023 4842 0 056 233 98251 232 9752 0 FindByMolecu 14 60 022380 0 021 6 116 6 224 0 019 61 029583 ___ 60 0223 0 FindByMolecu 14 172 9538 0 10 832 0 091 5 155 5 446 173 9611 172 9528 0 FindByMolecu 12 gt 69 9923 0 0 437 8 281 7 535 0 134 71 00075 169 9922 0 FindByMolecu 8 ioner e GRU THER ciacicell C7 H10 03 S 4 904 1511 4 963 14 411 349 0813 174 0381 0FindByMolecu 11 ae 5 Hydroxy 2 0 941 4 461 4758 0 225 203 04042 202 023 1 0 FindByMolecu 14 Sa ieren 100 0017 0 2 279 0 407 0 053 0 215 101 00867 100 0017 0 FindByMolecu 16 GIL Al Samples 1 2 Dichloro 4 398 0 137 0 019 0 492 98 976 15 97 9688 0 FindByMolecu 15 Al Infection Treatment Nor averaged C6 H6 02 5 6 880 0 007 0 754 0 330 285 0331 142 0118 0 FindByMolecu 15 Ei infection Treatment C4 H7 N 0 040 5 705 0 148 5 664 70 06611 69 0588 0 FindByMolecu 14 a Arris Cycloleucine 0 217 0 136 0 392 0 095 130 08607 129 0788 0 FindByMolecu 16 2E Al Entities C12 H12 N2 3 664 3 656 0 501 3 923 329 00735 328 0003 0 FindByMolecu 13 5 Ey Fitered on Flags feccCalls P M fiter C18 H6 N2 0 178 0 024 0 525 0 508 821 0095 410 0033 0 _FindByMolecu 16
60. ETERS can be loaded From file tsv Recent Items a File name EXPERIMENT PARAMETERS can be loaded from file tsv Network Files of type Tab separated file tsv X Add Parameter Edit Parameter Delete Parameter lt lt Back Next gt gt Einish cancel Figure 20 Experiment Grouping and loading experiment parameters from file for the Agilent Expression Single Color Demo sample data e Click Next workflow Type Analysis Biological Significance Step 2 of 8 Experiment Grouping Experiment parameters define the grouping or replicate structure of your experiment To enter experiment parameters click Add Parameter You can enter as many parameters as you want but only the first two parameters are used for analysis in this Guided Workflow Other parameters can be used when analyzing your data with the Features available from the Workflow Browser You can also edit and re order parameters and parameter values here Steps 1 Summary Report Displaying 6 sample s with 1 experiment parameter s To change use the button controls below 3 QC on samples 4 Filter Probesets Eal EA BEB US22502705_25 1209747382 _Untreated txt US22502705_25 1209747392_Treated txt Treated US22502705_25 1209747393 Treated txt US22502705_25 1209747394 Untreated txt US22502705_25 1209747404 Treated txt 5 Significance Analysis 6 Fold Change
61. Inclusion List csw Figure 62 Entity List and File Path Chooser page Export Inclusion List Step 1 of 2 a Type values in the Retention time window The default values are 0 0 percent and 0 25 min b Mark the Limit number of precursor ions per compound to check box and type in a value for ion s per compound By default this check box is cleared and the default value is 1 ion c Mark the Minimum ton abundance and type in the minimum ion counts By default this check box is cleared and the default value is 2000 counts If the sample data is from MassHunter Qualitative Analysis all of the filter options are available Sample data from other experiment types non MassHunter Qualitative Analysis sample data cannot be processed using the Positive ions Negative ions Exported m z value and Charge state preference filters d Select Export monoisotopic m z as the monoisotopic value or the value repre sented by the ion with the highest abundance e Select Specify charge state preference order to activate the inactive and active charge state options Specify the highest abundance charge state or as specified by the charge state preference order f Mark the Positive ions and Negative ions that are included in the filter g Click Finish 57 Integrated Biology operations Results Interpretation lexport Inclusion List Step 2 of 2 i x Filtering Parameters for Inclusion List Filtering Parameters for Inclusion List Inclu
62. Integrated Biology with Agilen WERS oe Professional 4 COOH Workflow Guide Prepare for an Find features experiment Results Interpretation Find Similar Entity Lists Export for Recursion ID Browser Identification Export for Identification Export Inclusion List Import Annotations Import and organize data Create an initial Advanced analysis Pathway Analysis Single Experiment Analysis Multi Omic Analysis Launch IPA Export to MetaCore Connect to Cytoscape operations Recursive find features NLP Networks NLP Network Discovery MeSH Network Builder Extract Relations via NLP Create Pathway Organism Agilent Technologies Notices Agilent Technologies Inc 2013 No part of this manual may be reproduced in any form or by any means including elec tronic storage and retrieval or translation into a foreign language without prior agreement and written consent from Agilent Technolo gies Inc as governed by United States and international copyright laws Manual Part Number 5991 1909EN Edition Revision A June 2013 Printed in USA Agilent Technologies Inc 5301 Stevens Creek Blvd Santa Clara CA 95051 Acknowledgements Microsoft is either a registered trademark or trademark of Microsoft Corporation in the United States and or other countries Adobe is a trademark of Adobe Systems Incorporated Warranty The material contained in this
63. KA Profile plot Log2 Normalized Ls SE4 Filtered by frequency conditions 100 0 1 Pathway View x P SEA Filtered by frequency conditions 100 0 1 Pathway View O O UE Description iaaa List View Figure 81 Pathway View after a Single Experiment Analysis 70 Integrated Biology operations Multi Omic Analysis 1 Launch Multi Omics Anal ysis in the Workflow Browser 2 Select the experiment parameters in Multi Omics Analysis Step 1 of 4 Pathway Analysis Multi Omic Analysis MOA compares two experiments and for non metabolomics experiments has options for you to isolate significant pathways based on the p value cut off and the minimum number of matched entities With Multi Omic Analysis you can overlay data from two different experiments on the same pathway thus performing a simultaneous integrated analysis of data from different experiment types You can choose an organism for pathway analysis that differs from the organism associated with your experiment and identify significant pathways for data from any combination of genomics transcriptomics proteomics and metabolomics experiments This operation finds all pathways that contain entities in common to the entities in the selected entity lists Commonness between a pathway and an entity is deter mined via the presence of a shared identifier The operation works with genomics transcripto
64. List NLP Eind Q Find Next Find Previous 7 Match Case Einish Cancel Figure 140 Save Pathway List page Extract Relations via NLP Step 4 of 4 105 Integrated Biology operations Create Pathway Organism NLP Networks If you are working with an organism that is not currently available in Mass Profiler Professional you can create a new pathway organism a Click Annotations gt Create Pathway Organism a Open http www ncbi nim nih gov taxonomy in your Internet browser Valid tax onomy IDs can be found in the Entrez Taxonomy database located at this site b Find the taxonomy for your organism Information displayed on this page is entered into the Create Pathway Organism dialog box I Taxonomy browser Antilocapra americana Mozilla Firefox loj x File Edit View History Bookmarks Tools Help 2 Taxonomy browser Antilocapra americana GP amp 2 www ncbi nim nih gov Taxonomy Browser wwwtax cgi mode Info amp id 989 1 amp ly p srchn 3ain fekeep 1 amp srchmode 1 amp unlock J pronghorn P A Ww Fa Taxonomy J GOS Browser Display I levels using filter none x Antilocapra americana Entrez records Database name Subtree links Direct links Taxonomy ID 9891 Genbank common name pronghorn Nucleotide 151 150 inherited blast name even toed ungulates Protein 88 87 Rank species Genome 1 1 Genetic code Trans
65. N January 31 2007 121 Reference information References BioCyc Pathway Genome Databases Includes BioCyc Pathway Genome databases from the Bioinformatics Research Group at SRI International used under license http www biocyc org Citation based on use of BioCyc Users who publish research results in scientific journals based on use of data from the EcoCyc Pathway Genome database should cite Keseler et al Nucleic Acids Research 39 D583 90 2011 Users who publish research results in scientific journals based on use of data from most other BioCyc Pathway Genome databases should cite Caspi et al Nucleic Acids Research 40 D742 53 2012 In some cases BioCyc Pathway Genome databases are described by other specific publications that can be found by selecting the database and then going to the Sum mary Statistics pages under the Tools menu The resulting page sometimes contains a citation for that database 122 Reference information References 123 www agilent com Agilent Technologies Inc 2013 Revision A June 2013 gt Agilent Technologies
66. O i sees ok Ne E RS fi Name Guanylate cyclase ws Type Enzyme be i ANARE a A a 957 EEE NN ee f LS oseas AISR TN mee hehe REW N sy ag ex y ee lt lt Back Next gt gt Finish Cancel NLP Networks Figure 131 MeSH Pathway page MeSH Network Builder Step 3 of 4 a Review the pathway list b Type a descriptive Name that is stored with the saved pathway entity list c Edit the Notes that are stored with the saved pathway entity list d Double click a row in the Pathways table to launch the Pathway Inspector to review the entities and relations contained in the new pathway e Click Finish ElMesH Network Builder Step 4 of 4 x Save Pathway List This window displays the details of the Pathway List that will be created on clicking Finish You can change the default Name and edit Notes of the Pathway List here as required The new Pathway List contains the pathway listed in the table To change the name of the new pathway double click the corresponding row and change the name in the Multiple Objects Inspector that opens Name Notes Source Number of Pathways Creation date Last modified date Owner Pathways Pathway List MeSH Pathway List MeSH Pathway List resulting From MeSH Network Builder with the Following settings Mesh term memory Selected Mesh Headings Memory Short Term Amnesia Type Exact relations Min Frequency 1 MeSH p A
67. Samples Interpretations E 3a Analysis Export ArrayExpress MAGE TAB F all Entities E Filtered on Fl F Filtered b Migrate from GS 5 E One Migrate from NGS 1 3 1 Import NCBI GEO Experiment Prepare for G57 Migration M gr ate to Worp group My Favorites Task Manager Memory Monitor 5 Agilent Single Color D J Samples Interpretations E Sy Analysis Create Custom Reftconfiguration dialog Backup Repository Edit Pathway Theme Restore Backup Change Repository Figure 133 Launching the configuration options from the menu bar b Click Pathway on the left hand pane in the Configuration Dialog dialog box c Click NLP limits on the left hand pane in the Configuration Dialog dialog box d Type a value lager than 1000 in the Maximum no Pubmed s to search for the NLP limits Note f the NLP limit value is smaller than 1000 you may receive an error No abstracts found on PubMed and Cannot Process Input File when you launch Extract Relations via NLP e Click OK xl NLP limits Maximum no of Pubmed s to searc 1035 L Copy Number Algorithms Data Analysis Algorithms Annotations Pathway FDP Layout QuadTree FDP Layout Pathway server settings L Clustering Algorithms a Class Prediction Algorithms x Cancel Apply Defaults Figure 134 NLP limits in the Configuration Dialog 102 Integrated Biology operations 2 Launch Extract Relations
68. Specific Installers Oo Mac OS x Oo Windows 32bit Oo Windows 64bit Oo Linux Figure 115 Cytoscape download web site a Run the installation file downloaded during the prior step Note The Cytoscape installation directory path cannot have any spaces Choose or create a new directory path from C to install Cytoscape Do not install Cyto 89 Integrated Biology operations 7 Configure Cytoscape to work with Mass Profiler Professional Pathway Analysis scape in the default C Program Files directory since there is a space in this directory path b Download and install the Java Runtime Environment if you are prompted i amp jre 7u17 windows x64 exe L j 31 5 MB java com Cytoscape_2_8_3_windows_64bit exe 86 9 MB cytoscape org Sexua sear P Figure 116 Downloaded Cytoscape installation file and Java Runtime Environment installation file In order to enable Mass Profiler Professional to transfer data and launch Cytoscape you must download the Cytoscape_Patch_n_Plugins zip file and follow the included instructions a Open http basil strandls com downloads Cyto in your browser b Click Cytoscape_Patch_n_Plugins zip c Select Save File in the Opening Cytoscape_Patch_n_Plugins zip dialog box d Click OK Cytoscape_Patch_n_Plugins zip is saved to your downloads folder on your PC lol x ye i Index of downloads cyto i 2 amp ij basil strandls com dow
69. a peptide is sufficiently short that it is easily made synthetically from the constituent amino acids The covalent bond formed by the reaction of a carboxyl group with an amine group between two molecules e g between amino acids Any of the total number of subsets that may be formed by the combination of indi vidual parameters among the independent variables For example the number of per mutations of A and B in variable in combination with X Y and Z in variable 8 equals six 6 2 x 3 and may be represented as AX AY AZ BX BY and BZ Note that the combinations of parameters within a variable are not relevant such as AB XY XZ and YZ The condition of an effect as being positive or negative additive or subtractive with respect to some point of reference such as with respect to the concentration of a metabolite A molecule formed by the covalent bonding of a repeating molecular group to form a larger molecule When the amount of available biological material is very small samples may be com bined into a single sample pooled and then split into different aliquots for multiple analyses By pooling the sample sufficient material exists to obtain replicate analy 116 Reference information Principal component Principal component analysis Protein Proteomics Quality Recursive Recursive finding Definitions ses of each sample where formerly there was insufficient material to obtain repli cate analyti
70. ach of the advanced operations available in the Workflow Browser use a wizard to guide you through the operation The steps and wizard pages may change each time you perform the operation depending on the number of variables in your experiment and analysis features selected The experiment described below allow this workflow to guide you through the options available for your analysis Terms and definitions used in metabolomics and metabolomic analyses vary It is recommended that you refer to the Definitions on page 110 for a list of terms and their definitions as used in Mass Profiler Professional and in this workflow The one variable array experiment presents an analysis of a treated versus untreated sample changes in a single independent variable also referred to as a parameter The data was acquired using two 2 parameter values for the indepen dent variable The parameter values consist of a single control data set that rep resents the sample without perturbation and a data set from a variation where the sample was treated to a conditions established by the experiment design In sum mary the one variable experiment contains a single parameter with two parameter values and three replicate samples for each parameter value Based on the discussion presented in the Prepare for an experiment chapter in the Agilent Metabolomics Workflow Discovery Workflow Guide an ideal experiment involves at least ten 10 replicates for each pa
71. advanced analysis operations Prepare for an Experiment Experiment Design amp Data Acquisition Experiment Design Hypothesis Natural Variability Replicate Sampling System Suitability Sampling Methodology GC MS Data LC MS 12 Find Features Spectral Features MassHunter Qualitative Analysis with DA Reprocessor or MassHunter Profinder Workflow for Untargeted Differential Analysis Import and Organize Data Create an Initial Analysis Advanced Operations Statistical Data Analysis GEE Files FbF Recursion MFE CEF Files Statistical Analyses Interpretations Pathways Filtering Class Prediction Additional Operations Mass Profiler Professional The steps involved in an untargeted differential analysis Mass Profiler Professional is used to import organize and analyze the data you acquired from your experimental samples Your untargeted differential analysis experiment may include eight steps as shown below Mass Profiler Professional begins at step four Figure 2 shows the steps and Agilent tools that are used in your experiment Identify Features as Compounds Identification MassHunter ID Browser I Working with Mass Profiler Professional What is the metabolomics workflow What is the metabolomics workflow Metabolomics is an emerging field of omics research that is concerned with the characterization and identification of the metabolite content of a
72. after importing and performing a biological significance analysis 34 Integrated Biology operations Mass Profiler Professional enables you to analyze data from different high throughput technologies like genomics transcriptomics proteom ics and metabolomics and it also allows you to compare data from these different experiment types in the same project Acquire data Advanced Operations Acquire data is not covered in the Metabolomics or Integrated Biology Workflow Guides Overview of operations Results Interpretation Pathway Analysis NLP Networks Agg Agilent Technologies Integrated Biology operations Overview of operations Layout of the Mass Profiler Professional screen Overview of operations Mass Profiler Professional is one of the solutions developed by Agilent to facilitate multi omic data analysis The operations available in the Workflow Browser of Mass Profiler Professional provide the tools necessary for analyzing features from your mass spectrometry data depending upon the need and aim of the analysis the experimental design and the focus of the study This helps you create different interpretations to carry out the analysis based on the different filtering normaliza tion and standard statistical methods Regardless of your personal expertise the Analysis Significance Testing and Fold Change workflow provides you with quality control to your analysis that improves your results When you begin
73. ally involves either or both GC MS and LC MS analyses Required hardware e PC running Windows e Minimum XP SP3 32 bit or Windows 7 32 bit or 64 bit with 4 GB of RAM e Recommended Windows 7 64 bit with 8 GB or more of RAM e Atleast 50 GB of free space on the C partition of the hard drive Data from an Agilent GC MS LC MS CE MS and or ICP MS system or data that may be imported from another instrument Required software e Agilent Mass Profiler Professional Software B 12 00 or later e Agilent MassHunter Qualitative Analysis software Version B 03 01 B 04 00 B 05 00 SP1 or later e Agilent MassHunter Data Acquisition software Version B 03 02 B 04 00 B 05 00 or later this will include Agilent MassHunter DA Reprocessor e Agilent MassHunter Quantitative Analysis software Version B 03 02 or later Before you begin Required items e Agilent ChemStation software e AMDIS e MassHunter ID Browser B 03 01 or later e METLIN Personal Compound Database and Library e Agilent Fiehn GC MS Metabolomics Library Before you begin er g u E rr anh h iORUOREaEne E f pa bA ALEN ATE ELB Ad ww AS I po Pewee Roles Compliance 21 CFR Part 11 is a result of the efforts of the US Food and Drug Administration FDA and members of the pharmaceutical industry to establish a uniform and enforceable standard by which the FDA considers electronic records equivalent to paper records and electronic signatures equivalent to trad
74. analysis If you select Yes IPA builds networks using both direct and indirect molecular interactions between genes If you select No IPA builds networks using only direct interactions between genes Type in specific Knowledge Base content if applicable Knowledge Base content indicates which database Is searched for information to build the network An empty string indicates to search all available Knowledge Bases and to incorporate information from all sources during the analysis Select whether to Include My Pathways in Enrichment Score If you select Yes all pathways saved under My Pathways in IPA are included in the scoring process Select whether to Review Settings and ID Mapping before Running Analysis If you select Yes you can review and modify settings before running your IPA analysis If you select No IPA data analysis is automatically performed using the settings defined in this dialog box Select the Gene Identifier Column The gene identifier is used to map genes in the entity list to genes in the IPKB 81 Integrated Biology operations Export to MetaCore 1 Launch Export to MetaCore in the Workflow Browser Pathway Analysis k Click OK Your default Internet browser is automatically launched and connected to the IPA server as specified in the IPA Server Address See Figure 93 on page 79 x Perform Data Analysis on Entity List Entity List away ANOVA p Corr Infection cut off p lt 0 05
75. and Software for Metabolomics Research Agilent publication n a September 18 2012 Multi omics Analysis Software for Targeted Identification of Key Biological Path ways Agilent publication n a May 3 2012 Metabolomics LCMS Approach to Identifying Red Wines according to their vari ety and Investigating Malaria infected red blood cells Agilent publication n a November 3 2010 Small Molecule Metabolomics Agilent publication n a November 3 2010 Presentation Metabolome Analysis from Sample Prep through Data Analysis Agilent publication n a November 3 2010 Emerging Insights Agilent Solutions for Metabolomics Agilent publication 5990 6048EN April 30 2012 Agilent Mass Profiler Professional Software Discover the Difference in your Data Agilent publication 5990 4164EN April 27 2012 Pathways to Insight Integrated Biology at Agilent Agilent publication 5991 0222EN March 30 2012 Confidently Better Bioinformatics Solutions Agilent publication 5990 9905EN February 2 2012 Integrated Biology from Agilent The Future is Emerging Agilent publication 5990 6047EN September 1 2010 Agilent Fiehn GC MS Metabolomics RTL Library Agilent publication 5989 8310EN December 5 2008 Agilent METLIN Personal Metabolite Database Agilent publication 5989 7712EN December 31 2007 Agilent Metabolomics Laboratory The breadth of tools you need for successful metabolomics research Agilent publication 5989 5472E
76. anism g Click OK if you receive a notification that the organism is already supported in Mass Profiler Professional see Figure 143 on page 107 106 Integrated Biology operations NLP Networks lt x Ber ie caninas X GeneSpring supports the organism with common name Human S67 Please go to Annotation gt Update Pathway Interactions to download the interaction database Taxonomy id e606 Scientific Name Homo sapiens Common Name Human Cancel Figure 143 Error indicating that the organism is already supported h Click Annotations gt Pathway Database Statistics to confirm that your organism is now included in Mass Profiler Professional x Organisms Entity Type System Entities Count User Entities Count Escherichia coli Mus musculus Mycobacterium tuberculosis Neurospora crassa Caenorhabditis elegans TestName Antilocapra americana Plasmodium falciparum Generic Drosophila melanogaster Saccharomyces cerevisiae Bacillus subtilis Figure 144 Pathway Database Statistics information Click Close This completes creating a pathway organism 107 Integrated Biology operations NLP Networks 108 Reference information This chapter consists of definitions and references The definitions section includes a list of terms and their definitions as used in this workflow The refer ences section includes citations t
77. ao 8 Single Experiment Pa Normalized Intensity Values A 23_P314100 2 40 EE is vify ralrutrs atest B N na p na s s N elalejoln a jej vfa lo BI NIN wv RIM BIMOLO WIR ie ed Dl Aiwiv1jo e 5 2 A 23_P45199 2 48 Fold change cut off fo ral toed Read ead ead ed od ead od fo VBS je A Vi co 0 VIR W Wo Treated Untreated Treatment cok ESS eh e Figure 25 Fold Change for the Agilent Expression Single Color Demo sample data a Review the GO Analysis results b Click and move the corrected p value cut off slider or type in the p value cut off value and press the Enter key The default value is 0 1 The results in the display window are automatically updated c Click Next A progress dialog box is displayed while the Single Experiment Path way Analysis is performed Al workflow Type Analysis Biological Significance Step 7 of 8 Steps GO Analysis The Gene Ontology GO classification scheme allows you to quickly categorize genes by biological process molecular Function and cellular component To determine if there is a significant representation of your entities identified From the previous step in a particular GO category a statistical test is performed and corrected p value is assigned to each category Entities corresponding to each category that satisfies the corrected p value cut off
78. apter Prepare for an experiment in the Agilent Metabolomics Work flow Discovery Workflow Guide and make sure your analysis includes a carefully thought out experimental design that includes the collection of replicate samples The ntegrated Biology with Mass Profiler Professional Workflow Guide is part of the collection of Agilent manuals help application notes and training videos The cur rent collection of manuals and help are valuable to users who understand the metabolomics workflow and who may require familiarization with the Agilent soft ware tools Training videos provide step by step instructions for using the software tools to reduce example GC MS and LC MS data but require a significant time investment and ability to extrapolate the example processes This workflow provides a step by step overview of performing metabolomics data analysis using Agilent MassHunter Qualitative Analysis and Agilent Mass Profiler Professional The following selection of publications provides materials related to metabolomics and Agilent MassHunter Mass Profiler Professional software e Manual Agilent Metabolomics Workflow Discovery Workflow Guide 5990 706EN Revision B October 2012 e Manual Agilent Metabolomics Workflow Discovery Workflow Overview 5990 7069EN Revision B October 2012 e Manual Agilent G38354A MassHunter Mass Profiler Professional Quick Start Guide G3835 90009 Revision A November 2012 e Manual Agilent G3835AA MassH
79. arch Criteria Database Peak Limits Positive lons Negative lons Scoring Search Mode and Search Results tabs similar to that shown in Figure 48 Figure 49 on page 50 and Table 2 on page 50 i compound Identification Wizard x Search Criteria Database Peak Limits Positive lons Compound Identification Browser Database selection Please set parameters for identification techniques Dane M Identification method atabase path Ic MassHunter PCDL Metlin_PCDL_A4M_11_1_12 C MassHunter Methods B 05 00 Default m H Search Criteria Positivelons 3 Identify Compounds Negative lons oing arch Mode earch Results Spectrum peak searches Bll Search Database Re ositive lons gt M seme d poka to search 5 eeqeaeaeoeaeaeedke amp Values to match a HLS spen graphically Generate Formulas r Molecular formula Search Criteria Database Peak Limits Positive lons _ Mass Charge carriers Neutral losses Mass and retention time retention time optional Mass and retention time retention time required M Match tolerance Mass 5 00 ppm x Retention time fo 100 minute fe JE si Pel Charge states if not known M Aggregates Charge stse rence _ Tl Dimers e g 2M H Help lt lt Back Ney Finish Cancel Tl Trimers e g 3M H Figure 48 Parameters for Search Database in the Compound Identification Wizard 49 Integrated Biology operations Results Interp
80. arch text Jmemory Back Next gt gt Finish Cancel Figure 136 Choose Type of Input Data page Extract Relations via NLP Step 1 of 4 Blextract Relations via NLP Step 1 of 4 f x Choose type of input data gt PubMed Search Provide a search term or a PubMed query phrase to run NLP on PubMed abstracts the number of abstracts Fetched can be modified via Tools gt Options gt Pathway gt NLP limits gt PubmedFetchLimit gt Extract From Local Files Run NLP on local Text Pdf Doc Html or Medline XML files Input source File r Next gt gt Finish Cancel Figure 137 Choose Type of Input Data page for File Input source Extract Relations via NLP Step 1 of 4 a Review the tagged content Target documents containing the search terms are identified Tagging is per formed using the entity dictionary All molecular and biological processes and function entities present in the Mass Profiler Professional Interaction Databases are tagged Matching entities are highlighted according to default color settings with the corresponding legend displayed below the searched content In the case of PubMed articles or Medline XML files the PubMed ID is shown in the left hand column In all other cases the name of the file is displayed b Click Next 103 Integrated Biology operations 5 Review the pathway in Extract Relations via NLP Step 3 of 4 ElExtract Relations via NLP Step 2 of 4 l x
81. as a dependent variable Metabo lites that do not show any change with respect to the independent variable may be valuable as control or reference signals The metabolites in a sample may be individually referred to as a compound feature element or entity during the various steps of the metabolomic data analysis When hundreds to thousands of dependent variables e g metabolites are available che mometric data analyses is employed to reveal accurate and statistically meaningful correlations between the attributes independent variables and the metabolic pro file dependent variables Meaningful information learned from the metabolite responses can be part of a larger process that is used to develop clinical diagnos tics for understanding the onset and progression of human diseases and for treat ment assessment Therefore metabolomic analyses are poised to answer questions related to causality and relationship as applied to chemically complex systems such as organisms You can use a metabolomics workflow as a road map for any analysis that requires the identification of statistically significant answers to questions presented to com plex data sets The metabolomics workflow may be used to perform the following analyses e Compare two or more biological groups e Find and identify potential biomarkers e Look for biomarkers of toxicology e Understand biological pathways e Discover new metabolites e Develop data mining and data p
82. ated pathways can be individually selected as sources for Pathway Analysis Note Single Experiment Analysis is referred to as Find Significant Pathways in prior versions of Mass Profiler Professional a Click Single Experiment Analysis in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data Is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The Single Experiment Analysis operation has four 4 steps as shown in Figure 69 The new SEA pathway list is placed in the Analysis folder within the Experiment Navigator Single Experiment Analysis Input Input Single Experiment Save Experiments Parameters Analysis Results Pathway List 1 of 4 2 of 4 3 of 4 4 of 4 Experiment Custom Information Save Organism Pathways Figure 69 Flow chart of the Single Experiment Analysis operation a Review the selected Experiment The default experiment is the active experiment in the open project If available Click Choose to change the Experiment b Review the Organism specified in the Experiment If the specified organism is not specified or incorrect you can change the Organism for the experiment in How to change the organism for an existing experiment You can change the Organism for an experiment 1 Right click on the experiment name in the Project Navigator 2 Cli
83. atinn Fynes chosen Rindinn Fxnression Member Metabolism Promoter Rindinn l Source NLP Network Discovery Number of Pathways E O Creation date Tue Apr 02 09 59 53 MDT 2013 SSS Last modified date Tue Apr 02 09 59 56 MDT 2013 S Owner user Pathways IDirect Interactions Homo sapiens Find Previous J Match Case lt lt Back Next gt gt Finish Cancel Figure 127 Analysis Results page NLP Network Discovery Step 5 of 5 MeSH Medical Subject Headings helps you create networks based on information in PubMed abstracts and identify interactions associated MeSH terms using NLP as an alternate way of creating pathways based on terms and concepts instead of enti ties Mass Profiler Professional obtains MeSH terms from the MeSH database see http www nlm nih gov mesh meshhome html a Click MeSH Network Builder in the Workflow Browser The MeSH Network Builder operation has four 4 steps as shown in Figure 128 The new pathway list is placed in the Analysis folder within the Experiment Navi gator The organism used is the same as specified in the active project MeSH Network Builder Save Pathway List 4 of 4 Input Select relevant MeSH Pathway Page MeSH terms 1 of 4 2 of 4 3 of 4 Figure 128 Flow chart of the MeSH Network Builder operation a Type an MeSH Term Type a concept or actual MeSH term The term does not have to be technical a simple phrase or phenome
84. ation number into the Keywords box you find the publication number and additional publica tions that reference the publication number Definitions on page 110 contains a list of terms and their definitions as used in this workflow Before you begin Required items Required items The Integrated Biology with Mass Profiler Professional workflow performs best when using the hardware and software described in the required sections below The required hardware and software is used to perform the data acquisition and analysis tasks shown in Figure 1 Agilent MassHunter Data Acquisition Software Data from an Agilent mass spectrometer Separate amp Feature Alignment amp PC running Windows Finding Statistics seua eis Agilent Mass Profiler Professional Qualitative ID Browser MPP Pathway Analysis ee Analysis Module Software Agilent MassHunter ID Browser Agilent MassHunter Qualitative Analysis Software sa Ag ilent MassHunter DA GC MSD Findby mp oe an Organize Identify compounds Integrated Biology Reprocessor GC 000 Saronio grai a Aae aa and add annotations with MPP GC QTOF Deconvolution Advanced Operations ee Feature ee g E LC Q TOF o 1 a ap Mass Profiler Professional LC 000 MFE Find by second Mn J Find by lon Figure 1 Agilent hardware and software used to acquire and analyze your sam ples following the Agilent Integrated Biology Workflow Sample separation to path way analysis typic
85. blocks of many different biological processes In order to distin 114 Reference information Metabolome Metabolomics Metabonomics NLP Normalization Null hypothesis Observation One hit wonder Organic compound Organism Definitions guish metabolites from lager biological molecules known as macromolecules such as proteins DNA and others metabolites are typically under 1000 Da A metabolite may be individually referred to as a compound molecular feature element or entity during the various steps of the metabolomic data analysis The complete set of small molecule metabolites that may be found within a biologi cal sample Small molecules are typically in the range of 50 to 600 Da The process of identification and quantification of all metabolites of an organism in a specified biological situation The study of the metabolites of an organism pres ents a chemical fingerprint of the organism under the specific situation See meta bonomics for the study of the change in the metabolites in response to externalities The metabolic response to externalities such as drugs environmental factors and disease The study of metabonomics by the medical community may lead to more efficient drug discovery and to individualized patient treatment Meaningful informa tion learned from the metabolite response can be used for clinical diagnostics or for understanding the onset and progression of human diseases See metabol
86. bolomics workflow analysis involves two types of variables that are associ ated with your samples independent variables One or more of the attributes of the state of the organism that are known to you in advance of sampling These attributes are referred to as an independent variable During the various steps of the data analysis the workflow refers to the known states of the organism or externalities to which the organism is subjected as parameter values conditions or attribute values The known states and external ities represent independent variables in the statistical analyses Dependent variables The observable biological response to changes in the inde pendent variables The response can manifest as a change in the metabolic pro file Each metabolite that undergoes a change in expressed concentration is referred to as a dependent variable The metabolites in a sample may be individually referred to as compounds fea tures elements or entities during the various steps of the metabolomic data analysis Metabolites represent dependent variables in the statistical analyses 14 Working with Mass Profiler Professional What is the metabolomics workflow The hypothesis Natural variability Replicate sampling System suitability Sampling methodology The first and most important step in your experiment is to formulate the question of correlation that is answered by the analysis the hypothesis This question Is a state
87. cal results The trade off loss of information about the biological variation that was formerly present in each unique sample is offset by a gain in sta tistical significance of the results Transformed data into axes or principal components so that the patterns between the axes most closely describe the relationships between the data The first princi pal component accounts for as much of the variability in the data as possible and each succeeding component accounts for as much of the remaining variability as possible The principal components often may be viewed and interpreted most readily in graphical axes with additional dimensions represented by color and or shape representing the key elements independent variables of the hypothesis This is part of the principal component analysis process employed by Agilent Mass Pro filer Professional The mathematical process by which data containing a number of potentially cor related variables is transformed into a data set in relation to a smaller number of variables called principal components which account for the most variability in the data The result of the data transformation leads to the identification of the best explanation of the variance in the data e g identification of the meaningful informa tion Also known as PCA Linear chain of amino acids whose amino acid order and three dimensional struc ture are essential to living organisms Also know as a polypeptide The study of t
88. cape in the Workflow Browser MPP immediately starts transferring your entity list and launches Cytoscape b Click Cancel in the Send Entities to Cytoscape progress box if you launched Con nect to Cytoscape with an active entity list that does not belong to the active experiment or if you want to stop Connect to Cytoscape for another reason Note f the Send Entities to Cytoscape progress box indicates that GeneSpring was unable to start Cytoscape or a message indicating that Access is denied stop and continue to step 5 Download Cytoscape 2 8 x to your computer on page 88 and then return to this step Figure 110 The normal Send Entities to Cytoscape progress box When Cytoscape is launched a splash screen is displayed with the version number as shown in Figure 111 on page 87 a Cytoscape Jnhearsity of California UCSD ee ae Systems amp RA Biology TORONTO AER NCIBI ol srt ga ote y At 2 Sis Institut Pasteu Agilent Technologies A noriai Steal J Figure 111 Cytoscape splash screen at startup a Begin using Cytoscape to perform your network visualization and analysis Cytos cape is used to visualize molecular interaction networks and provide you with a means to generate views of gene and protein associations 87 Integrated Biology operations 5 Download Cytoscape 2 8 x to your computer Pathway Analysis Cytoscape Desktop New Session Oo x File Edit view Selec
89. cell or whole organism Metabolomics studies let researchers view biological systems in a way that is different from but complementary to genomics transcriptomics and proteom ics studies Discovery metabolomics experiments involve examining an untargeted suite of metabolites finding the metabolites with statistically significant variations in abundance within a set of experimental versus control samples and answering questions related to causality and relationships Metabolomics is a powerful emerg ing discipline with a broad range of applications including basic research clinical research drug development environmental toxicology crop optimization and food science Metabolomics research leads to complex data sets involving hundreds to thousands of metabolites Comprehensive analysis of metabolomics data requires an analytical approach and data analysis strategy that are often unique and require specialized data analysis software that enables cheminformatics analysis bioinformatics and statistics Agilent provides you with tools to perform metabolomics research Experiment variables are derived from your experiment When one or more of the attributes of the state of the organism are manipulated those attributes are referred to as independent variables The biological response to the change in the attributes may manifest in a change in the metabolic profile Each metabolite that undergoes a change in expressed concentration is referred to
90. ck Inspect Technology 63 Integrated Biology operations Pathway Analysis Project Navigator Differentiation 1U TWo ariable Data Set Experiments il Differentiation by Infection and Treatment Hill One Variable Data Set amg One ariable Hur jee Upen Experiment Compound Close Experiment Inspect Experiment a JL Differentiation by Infection Inspect Technology o eT _ Samples Create New Experima Inspect Technology Interpretations 5 Analysis Create New Normalized Experiment E E All Entities Remove Experiment Filtered on Flag E F Filtered by E k 2way 4 Share Experiment Change owner Delete Experiment One Variable Data Set Refresh Experiment Samples P Figure 70 Inspect Technology for an experiment 3 Select the Organism from the Technology Inspector dialog box al Technology Inspector x Name Jone Variable Human_2013_Mar_12_15_29_14 Description Technology for MassHunterQual UNIDENTIFIED_COMPOUNDS One Variable Creation date Tue Mar 12 15 32 22 MDT 2013 Last modified date Tue Mar 12 15 32 22 MDT 2013 Owner gxmanager sss vendor Name MassHunterQual Type UNIDENTIFIED COMPOUNDS C SOSOSC S Version fioo oo E Organism Homo sapiens Number of entities 9257 Entities Alignment Value CompositeSpectrum 122 048 1 0158621 123 055176 93611 484
91. d folder Do not type a file name at this location b Select the folder or create a new folder for your CEF file in the Choose a file dia log box c Type the File name For example you can type Export for Recur sion cef d Click Save x Save in gr MPP Data x P pA E Recent Items File name Export for Recursion Save Files of type Compound Exchange Format cef v Cancel Figure 43 Choose a file dialog box e Click OK 46 Integrated Biology operations ID Browser Identification 1 Launch IDBrowser Identi fication in the Workflow Browser Results Interpretation ID Browser identifies and annotates the entities in your selected entity list using LC MS compound databases METLIN pesticides forensics GC MS libraries NIST and Agilent Fiehn Metabolomics and empirical formula calculations using Agilent s molecular formula generator MFG When entity identification is completed ID Browser saves and returns and an identi fied CEF file to Mass Profiler Professional This CEF is imported into the Mass Pro filer Professional experiment and annotations in the selected entity list are updated a Click IDBrowser Identification in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data Is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The ID B
92. dication while ID Browser is identifying your entities 5 Review your ID Browser When the identification is complete use the ID Browser interface review your results results and make adjustments before returning the identification results to Mass Profiler Professional a Review and make adjustments to the entity identifications as necessary The ID Browser interface is shown in Figure 52 on page 52 Additional informa tion regarding the use of ID Browser is obtained using Help found on the menu bar b Click Save and Return to export your identified entity list back to your experiment in Mass Profiler Professional Wlagilent MassHunter ID Browser B 05 00 oj x File Edit Yiew Identification Method Configuration Help w P Run ID Wizard NOAM Ca ah MS Spectrum Resuks bt ik MS Peaks One MFE Spectrum 0 305 min x amp Structure Viewer Prepacifenol epode x e tiQ f mz Abud Abund Nom z Sat Structure MOL Text gon p aor o 104 Cpd 20 Prepacifenol epoxide C15 H21 Br2 CI 03 0 305 MFE Spectrum ya F es aa O gee A e E a S 0 6 443 9655 M H a eae Pear See oe PSS ee Ser Se Se Se 443 443 25 443 5 443 75 444 444 25 4445 444 75 445 Counts vs Mass to Charge m z ILS Speotum Resuts Ga Compound List x i Cpd Y Label Vv Name Tif Formula VY Score Vv Mass Y lt AvgMass VW Std Dev VY Mass DB 2 13 Cpd 13 C4 H7
93. e entity list specified as the Entity List in step 2 on page 39 Since the conditions and search values you enter depends on the selected search field Table 1 provides you with an overview of the available parameters to build your entity list search query Table 1 List of available parameters to build your search query Applicable Condition Search Field Group Search Value Group A Creation Date A Enter value or select date Global List C gt Enter value or select date Mark B lt Enter value or select date Modified Date A Name B Group B Notes B equals Enter text Number of Entities A starts with Enter text System Object B ends with Enter text Technology B includes Enter text Type B Group C equals Select true a Select a Search Field 40 Integrated Biology operations 5 Review the search results in EntityList Search Wizard Step 2 of 2 Results Interpretation b Select a Condition The available conditions depend on the Search Field selec tion as shown in Table 1 c Enter a Search Value or select a date or condition depending on your Search Field selection as shown in Table 1 d Select the AND or OR operator in Combine search conditions by if your entity list filter includes criteria for more than one Search Field If your criteria has only a single Search Field or if this is your last combined Search Field row proceed to step f e Repeat step a through step c for each of your combined filter criteria f
94. e saved as a Pathway List with the Following details under the entity list selected as input For this analysis Pathways Pathway Matched Entities Differentiation by mey Entities of Experiment Typ Hs_TCR_signaling_wWP1927_45094 Name pEA Filtered by Frequency conditions 100 0 1 Notes Pathway List resulting from Single Experiment Analysis with the Following settings Selected Annotations for Experiment KEGG ID ae si Source SingleExpermentAnalysis ss lt is s i SO Number of Pathways 1278 Experiment Differentiation by Infection and Treatment ss s sSSSSS Experiment Type JMetabolomic Interpretation finfection Treatment Entity List Filtered by frequency conditions 100 0 1 Creation date Last modified date Owner fue Mar 12 13 51 17 MDT 2013 fue Mar 12 13 59 50 MDT 2013 user Homo sapiens Hs_Prostanoid_metabolism_wWP1891_ Homo sapiens Hs_Signaling_by_PDGF_WP1916_45212 Homo sapiens Hs_Signaling_by_Robo_receptor_wWP13 Homo sapiens Hs_Gene_Expression_wWP1821_42044 Hs_Sphingolipid_Metabolism_WP1923 Homo sapiens Hs_Intrinsic_Pathway_for_Apoptosis_W Homo sapiens Hs_Fatty_acid _triacyighcerol _and_ke Homo sapiens 1 0 0 1 0 Homo sapiens 2 0 2 0 Hs_Nucleosome_assembly_wWP1874_4 Find Q Find Next Find Previous Match Case Homo sapiens
95. e the slider p value cut off until the results displayed are satisfactory Rerun the p value adjustment several times to develop an understanding of how the p value cut off affects your results A larger p value passes a larger number of entity lists d Click Back make changes to prior parameters and click Next to return to the results until you are satisfied with your analysis e Click Finish All of the entity lists shown on the page whether they are or are not highlighted are saved in a folder named Similar Lists satisfying under the source Entity List in the Experiment Navigator ElrFind Similar Entity Lists Step 3 of 3 x Find Similar Entity lists Results Entity lists showing significant overlap with the entity list selected for analysis are displayed in the left hand spreadsheet To modify the level of significance click the Change cut off button and enter new p value cut off To save the significant entity lists select the lists and click the Custom Save button Entity Lists showing no overlap with the entity list selected For analysis are displayed in the right hand spreadsheet Displaying 38 Objects satisfying corrected p value cut off 0 05 To change use the control buttons below My Two Variable Experi 2 Way ANOVA p Corr 238 238 1 44E 43 a My Two Variable Experi Union 2Way ANOVA cut rar ge 2 1 0 0 9 My Two Variable Experi Union 2Way ANOVA cut 271 271 0
96. e types of entities to evaluate in Select entity type Click Next Elnr Network Discovery Step 3 of 5 a x Analysis Filters 4 specific entity or relation type can be omitted From the analysis by unchecking the appropriate check box Relations can also be filtered from the analysis by their score The score of a relation ranges from 1 to 10 10 being the highest score Entities can also be filtered From the analysis by their local connectivity to the starting set of entities Further the results of the analysis can be restricted to a manageable size by specifying the number of new entities to Fetch based on the local connectivity of the entities or on the local to global connectivity ratio of the entities Relation Filter Relation score gt E Select relation type J Member JV Transport JV Expression V Regulation JV Binding JV Promoter Binding J Metabolism V Protein Modification lt lt Back i Figure 123 Direct Interactions Analysis Filters page NLP Network Discovery Step 3 of 5 Elnr Network Discovery Step 3 of 5 i x Analysis Filters 4 specific entity or relation type can be omitted From the analysis by unchecking the appropriate check box Relations can also be filtered from the analysis by their score The score of a relation ranges From 1 to 10 10 being the highest score Entities can also be filtered from the analysis by their local connectivity to the starting set of entiti
97. ecreased to improve computation time and information quality For example an extracted ion chromato gram obtained from GC MS and LC MS data files Reduction provides smaller view able and interpretable data sets by employing feature selection and feature extraction Also know as dimension reduction and data reduction This is part of the principal component analysis process employed by Agilent Mass Profiler Profes sional Mathematical techniques for analyzing data to identify the relationship between dependent and independent variables present in the data Information is gained from the estimation regression or the sign and proportionality of the effects of the inde pendent variables on the dependent variables This is part of the principal compo nent analysis process employed by Agilent Mass Profiler Professional Also known as regression Collecting multiple identical samples from a population so that when the samples are evaluated a value is obtained that more closely approximates the true value A part piece or item that is taken from a specimen and understood as being repre sentative of the larger specimen e g blood sample cell culture body fluid aliquot or population An analysis may be derived from samples taken at a particular geo graphical location taken at a specific period of time during an experiment or taken before or after a specific treatment A small number of specimens used to represent a whole class or group
98. ed analysis operations 17 Working with Mass Profiler Professional Using Mass Profiler Professional Layout of the Mass Profiler The main functional areas of the Mass Profiler Professional screen are illustrated in Professional screen Bae The main Mass Profiler Professional window consists of four parts Menu Bar access to actions that are used for managing your projects experi ments pathways and display pane views Toolbar access to buttons for commonly used tasks grouped by project experi ment entity statistical plot and sidebar tasks Display Pane organized into functional areas that help you navigate through your project experiments analyses and available operations Status Bar information related to the current view cursor position entity and system memory ElMass Profiler Professional My Two Yariable Experiment 5 x Project Search View Tools Annotations Windows Help eae A Fe a WD NS A e Project Navigator My Two Variable Experiment Experiments periment Setup a ii My Two ariable Experiment Quick Start Guide Experiment Grouping pate Interpretation Workflow Browser ontral a F f n f h ontrol on Samples 1 1_Control_o00 ist of operations relevant for the Frequency 12 coea Project Navigator Sa Ee hl 13 Cntr 0 tarh mantion
99. ee O Tue Apr o2 15 12 50Mm0T 2013 SSS Tue Apr o2 15 12 51 MoT 2013 ka Rattus norvegicus Eind Find Next Find Previous 7 Match Case lt lt Back Ext gt gt Finish Cancel Figure 132 Save Pathway List page MeSH Network Builder Step 4 of 4 101 Integrated Biology operations NLP Networks Extract Relations via NLP You can use Natural Language Processing NLP to create new pathways directly from PubMed abstracts and other documents stored on your PC or network pdf doc and html files NLP first recognizes entities in sentences and then performs information extraction to identify relationships between these entities The entities that NLP can recognize are restricted to those packaged in the generic Interaction Database and the Interaction Databases for the organism of the currently active experiment 1 Check that the NLP limits a Click Tools gt Options on the menu bar to launch configuration options is greater than 1000 lmass Profiler Professional Agilent Single Color Demo Project Search View Annotations Windows Help i nE Script Editor a R Editor Project Navigator al Agilent Single Color Demo Experiments Import Pathways from WikiPathways Run R Script Remotely j Agilent Single Import Pathways From BioCyc ji IB One Variable Import Pathway From File Import from Pathway Architect AR hen te Import BROAD GSEA Gene sets
100. ered on Flags fa MassHunterQual LCMS_IDENTI Filtered on Flags accCalls P Created from Significance Test MassHunterQual LCMS_UNIDE Filtered by frequency conditio Created from Significance Test MassHunterQual UNIDENTIFIE Filtered on Flags accCalls P Created from Significance Test MassHunterQual IDENTIFIED_U T test p lt 0 05 Created from Significance Test MassHunterQual LCMS_UNIDE Filtered by frequency conditio Created from Significance Test MassHunterQual UNIDENTIFIE Oneway ANOVA p lt 0 05 Created from Significance Test MassHunterQual UNIDENTIFIE 4 Filtered by frequency conditio Created from Significance Test MassHunterQual LCMS_UNIDE Filtered by frequency conditio Created from Significance Test MassHunterQual IDENTIFIED_U 2Way ANOVA p Corr Infectio Created from Significance Test MassHunterQual LCMS_UNIDE 2Way ANOVA p Corr Infectio Created from Significance Test MassHunterQual LCMS_UNIDE T test p lt 0 05 Created from Significance Test MassHunterQual IDENTIFIED U T test p lt 0 05 Created from Significance Test MassHunter ual IDENTIFIED U Filtered by frequency conditio Created from Significance Test MassHunterQual IDENTIFED U Welch s t test p Corr lt
101. eriment Pa Normalized Intensity Values Treated Untreated Treatment lt lt Back Next gt gt Emish cancel Figure 23 Filter Probesets for the Agilent Expression Single Color Demo sample data a Review the Significance Analysis results b Click and move the Corrected p value cut off slider or type in the p value cut off value and press the Enter key The default value is 0 05 The results in the display window are automatically updated c Selected Treated for the Control Group d Click Next 30 Example experiments Creating an expression analysis using the sample array experiment Al workflow Type Analysis Biological Significance Step 4 of 8 Filter Probesets Steps 1 Summary Report 2 Experiment Grouping 3 QC on samples 5 Significance Analysis 6 Fold Change 7 GO Analysis 8 Single Experiment Pa Normalized Intensity Values If flag values are present entities are Filtered based on their flag values Otherwise entities are filtered based on their signal intensity values To change the filter criteria click Re run Filter Displaying 20187 out of 20227 entities where 1 out of 6 samples have flags in Detected Not Detected Treated Untreated Treatment lt lt Back Next gt gt Emish canc Figure 24 Significance Analysis for the Agilent Expression Single Color Demo sample data a Review the Fold Change res
102. es Further the results of the analysis can be restricted to a manageable size by specifying the number of new entities to Fetch based on the local connectivity of the entities or on the local to global connectivity ratio of the entities Relation Filter Entity Filter Relation score gt E Entity local connectivity gt 2 Select relation type Select entity type J Member JV Enzyme J Transport JV Function JV Expression IV Process V Regulation JV Family JV Binding JV Small Molecule V Promoter Binding V Complex J Metabolism JV Protein J Protein Modification Limit analysis results based on Local to Global Connectivity Ratio v Maximum number of new entities so een hee Finish Cancel Figure 124 Expanded Interactions Analysis Filters page NLP Network Discovery Step 3 of 5 97 Integrated Biology operations 5 Review the analysis results in NLP Network Discovery Step 4 of 5 6 Save the pathway list in NLP Network Discovery Step 5 of 5 NLP Networks ap Network Discovery Step 3 of 5 x Analysis Filters 4 specific entity or relation type can be omitted From the analysis by unchecking the appropriate check box Relations can also be filtered from the analysis by their score The score of a relation ranges From 1 to 10 10 being the highest score Entities can also be filtered from the analysis by their local connectivity to the starting set of entities Further
103. etab ii One Variable Data Set Hs_Integration_of_energy_metabolismn_ WwW 3 Fold change gt 2 0 Hs_Membrane_Trafficking_WP1846_448 B E Filtered on Flags accCalls P M fil Hs_Peroxisomal_lipid_metabolism_WP 18 E Filtered by frequency conditions HEM etabolismiol water solublesvitami E Oneway ANOVA p lt 0 05 ia Hs_GPCR_downstream_signaling_ WP 182 SEA Filtered by frequency cc Hs_Asparagine_N linked_glycosyation_ Hs_Metabolism_of_carbohydrates_wWP18 Hs_Eukaryotic_Translation_Termination_ i MOA Differentiation by Infection anc x JHs_Platelet_homeostasis_WP1885_42101 Analysis Hs_Fanconi_Anemia_pathway_WP1816_ 2 GRRE P Hs_Mitotic_G2 G2 M_phases_WP1859_ Filtered by frequency conditions 100 0 1 JHs_Protein_folding_WP1892_42108 i MOA Differentiation by Infection and Treatm Hs_Sernaphorin_interactions_WP1907_4 My Favorites Hs_Metabolism_of_RNA_WP1854_44901 Hs_Respiratory_electron_transport ATP Hs_Metabolism_of_nucleotides_WP185 1 gt Hs_Telomere_Maintenance_wWP1928_42 1 Hs_Signaling_by_Insulin_receptor_wWP191 pany ues z Hs_Phase_1_ _Functionalization_of_com My Lists Hs_Metabolism_of_steroic_hormones_an Hs_Metabolism_of_porphyrins_WP1852_ j ji _ One Variable Data Set normalized Conflicts 0 Differentiation by Infection and Treatment
104. eview analysis results in Multi Omic Analysis Step 3 of 4 Pathway Analysis d Click Next A progress status box is displayed while the pathways are searched based on the entities in the entity list ElMutti omic analysis Step 2 of 4 E x Input Parameters Select the desired Interpretation and Entity List in the fields provided Then select the annotation types From the lists that you want GeneSpring to use when matching the entities from the chosen entity list to the pathways From the organism you specified in the previous step The order in which they are displayed reflects the order in which GeneSpring is matching entities between the pathways and entity lists By default all available annotation types in the chosen entity list are selected Associated data with the matching entities is grouped by the conditions specified in the chosen Interpretation and displayed as heatstrips in the matching pathways at the end of the Multi Omic Analysis workflow Differentiation by Infection and Treatment One Variable Data Set Choose Interpretation Choose Interpretation ii Differentiation by Infection and Treatment uhl One Variable Data Set Sy Interpretations Interpretations Ql All Samples All Samples eM Infection Treatment Non averaged i Qu Numeric Parameter Non averaged infection Treatment Q Numeric Parameter Ql Numeric Parameter Non averaged Pull Numeric Parameter i m Parameter Non ave
105. f Cytoscape CytoPanels Rendering Engine The Menus Annotation Network Management eA E E E E E Linkout The Network Overview Window Acknowledgements z v 4 b 4 gt Figure 113 Cytoscape User Manual accessed from Help gt Contents c The connection process is now complete You can continue analyzing your data with Mass Profiler Professional at the same time your Cytoscape session Is run ning d Close Cytoscape before starting a new analysis Re launching Connect to Cytos cape while Cytoscape is still open with a prior entity list adds the new entity list to the prior analysis and experiment within Cytoscape There is no cost to register download and install Cytoscape on your computer a Close Mass Profiler Professional b Open http www cytoscape org in your Internet browser 88 Integrated Biology operations Install Cytoscape on your computer Pathway Analysis I Cytoscape An Open Source Platform for Complex Network Analysis and isualization Mozilla Firefox File Edit View History Bookmarks Tools Help Cytoscape An Open Source Platform for Co RK wow cytoscape org C cyctoscape P A O AN A q Cytoscape o m 6 4 lan 4 f PN Introduction Download Apps Documentation Community Sasson Appications Cytorcape_ 1 0 0 sampieData gall dnered cys 200 SBR He eet ww aqad G HHH ES a x 200
106. ference f Mark Save as Default if you would like this configuration to be saved as the default for future save entity list steps g Select the experiment type for your configuration to be applied h Click OK to exit the Select Annotation Columns dialog box Elselect Annotation Columns x rSelect Annotation Columns Available items Compound Name Ionization mode KEGG ID JV Save as default Default for MassHunterQual UNIDENTIFIED_COMPOUNDS Figure 54 Select Annotation Columns dialog box Click OK to complete the IDBrowser Identification operation At this time your entities in Mass Profiler Professional are identified as shown in Figure 55 53 Integrated Biology operations Results Interpretation mass Profiler Professional Differentiation by Infection and Treatment Eee Search View Tools Annotations Windows Help Bea SEEN EJA a e He a 2x E e Experiments Differentiation by Infection and Treatment F Gb Differentiation by infection and teach 936100 14 THI 0 FEN cori 9 893 1 100 94343 199 9361 0 FindByMolecu Z Mi
107. folder that contains the Cytoscape program Cytoscape exe in the Choose a File dialog box see Figure 61 on page 57 for a typical dialog box Click Open Click OK 86 Integrated Biology operations 3 Launch Connect to Cytoscape in the Workflow Browser 4 Perform your analysis with Cytoscape Pathway Analysis configuration Dialog xi Cytoscape Installation Path Class Prediction Algorithms Agilent Exon MS Cytoscape installation path c Cytoscape_v283 Browse J Miscellaneous fr Bape ca 36 Startup Dialog emory for Cytoscape MB Network Settings Search Results Custom Data Library Creation R Integration NCBI ftp URL e rray Website Settings Ingenuity Pathways Analysis IPA Desktop Agilent Flag Settings Translation Mapping Mode of Inheritance Haplotyping Parameters GEOLOADER Settings ArrayExpress Loader Settings Guided Workflow Color Settings Fold Change Settings Maximum Limit on the number of Entities Samples to show 3D PCA plots Region List Size gt E d Target Detection v Cancel Apply Defaults Figure 109 Cytoscape Installation Path in the Configuration Dialog When passing entities to Cytoscape the active entity list must belong to the active experiment An overview of the Project Navigator and Experiment Navigator func tional areas within Mass Profiler Professional are shown in Figure 29 on page 37 the active entity list and experiment are in bold font a Click Connect to Cytos
108. ganism A metabolite that may be individually referred to as a compound molecular feature element or entity during the various steps of the metabolomic data analysis The compounds that meet the requirements specified by each experiment performed on your data Entity lists are viewed in the Experiment Navigator Proteins acting as biocatalysts in a metabolomic reaction These entities are particu larly important in depicting a biochemical network 112 Reference information Experiment Externality Extraction Family Feature Feature extraction Feature selection Filter Filter by flag Function Hypothesis Definitions Data acquired in an attempt to understand causality where tests or analyses are defined and performed on an organism to discover something that is not yet known to demonstrate as proof of something that is known or to find out whether some thing Is effective A quality attribute or state that originates and or is established independently from the specimen under evaluation The process of retrieving a deliberate subset of data from a larger data set whereby the subset of the data preserves the meaningful information as opposed to the redundant and less meaningful information Also known as data extraction A group of proteins related by structure function or another biological parameter Independent distinct characteristic of a phenomena and data under observation Features are an im
109. ge Class Prediction Build and Test Model Description Launched on interpretation All Samples All Samples Displaying 4414 0 selected gt 110M of 138m Figure 29 The main functional areas of Mass Profiler Professional illustrated using the Two variable experiment data set 37 Integrated Biology operations Results Interpretation Results Interpretations z Find Similar Entity Lists Export For Recursion IDBrowser Identification Export For Identification Export Inclusion List Import Annotations Entity Lists Find Similar Entity Lists p value cut off 1 Launch Find Similar Entity Lists in the Workflow Browser Results Interpretation With the operations available in Results Interpretation you can analyze and refine the entities and entity lists that were created during your experimental analyses Results Interpretation consists of six operations e Find Similar Entity Lists on page 38 e Export for Recursion on page 45 e ID Browser Identification on page 47 e Export for Identification on page 54 e Export Inclusion List on page 55 e Import Annotations on page 59 Entity lists contain the compounds entities that meet the conditions specified in each experiment performed on your data Entity lists are displayed and accessed in the Experiment Navigator The Experiment Navigator makes it easy for you to view an entity list s relationship among your experiments and select it f
110. ge 24 The entity list created from the Agilent Expression Single Color Demo sample data is a gene probe based entity list rather than a compound based entity list created from mass spectrometry data Create an initial Advanced analysis operations Create project Ww and experiment pondan New ar aad Experiment Do Significance sathe Testing and Workflow Load data Fold Change Browser Startup dialog box Create New Project dialog box Experiment Selection Dialog dialog box New Experiment dialog box Figure 10 The steps to import and analyze the Agilent Expression Single Color Demo sample data 23 Example experiments Set up a project and an experiment 1 Create a new project in the Startup dialog box 2 Enter descriptive information in the Create New Project dialog box Creating an expression analysis using the sample array experiment Flow Chart of the Analysis Biological Significance Wizard el Step 4 Filter on Probesets Y Step 5 Significance Analysis Y Step 6 Step 1 Summary Report Step 7 GO Analysis Y Step 8 Single Experiment Pathway Analysis V Save your project and perform advanced analysis operations from the Workflow Browser Y Step 2 Experiment Grouping Y Step 3 QC on samples Fold Change Y Y Go to Step4 Go to Step7 Figure 11 Steps performed by the Analysis Biological Significance wizard A
111. gt pamere 1 Phosphati 0 566 0 114 3 452 7 933 493 01346 492 0059 0__FindByMolecu 13 E ERETT C21 H6 N2 7 117 3361 0 347 7 162 510 97165 509 9652 0 FindByMolecu 11 LB 2Way ANOVA p Corr Infe C16 H5 CIO 0 191 0 250 4 077 0 063 360 95758 359 95 15 0 _FindByMolecu 15 O 2Way ANOVA p Corr infer Prepacifenol 0 097 3 509 11 379 3 640 442 96072 441 9526 0 FindByMolecu 11 Fy 2Way ANOVA p Cort Trea 6 Mercaptop 0 223 0 103 3801 0 205 524 96313 523 9552 0_FindByMolecu 15 LIB Union 2Way ANOVA cut off C16 H18 N2 0 282 7 556 7 505 11541 606 967 1 1605 9604 0 FindByMolecu 3 ewes C23 H13 Cl 0 095 3 763 3 674 11556 688 9695 687 9609 0 FindByMolecu 11 C13 H C105 S 15 858 3 868 0 194 0 545 376 93088 275 9222 0 FindByMolecu 11 Griseofulvin 7 875 0 578 0 178 0 228 353 07797 252 0715 0 FindByMolecu 14 C22 H5 CIN 8 376 3 831 0 205 0 392 540 9373 539 9294 0_FindByMolecu 13 958 9567 0 715 254 0 453 7 577 3 776 959 9633 958 9567 0 FindByMolecu 9 785 9379 0 0 000 7 411 14 696 0 000 786 94586 785 9379 0 FindByMolecu 6 C23 H14 N4 3182 0 203 0 000 0 194 603 0487 602 0429 0 _FindByMolecu 15 C14 H20 N1 3395 0 173 0 108 0 029 52104614 520 038 0 FindByMolecu 15 C14 H16 N2 0 104 0 230 0 470 0 055 57 04056 356 0241 0 FindByMolecu 16 C20 H15 Cl 0 000 11 000 0 000 15 077 634 99
112. gure 59 Flow chart of the Export Inclusion List operation a Click Choose b Select the entity list to export Click an entity list that is at least Filtered on Flags from the entity lists in the Choose Entity List dialog box More significance in your analysis is obtained by selecting an entity list that has at least been filtered by flags to remove one hit wonders c Click OK xi My Two Variable Experiment H Analysis E All Entities Filtered List on Mass gt 125 Mass lt 200 gt Filtered List on Annotations equals C My Favorites Figure 60 Choose Entity List dialog box d Click Browse Do not type a file name at this location e Select the folder or create a new folder for your CEF file in the Choose a file dia log box f Type the File name For example you can type Export Inclusion List csv g Click Save 56 Integrated Biology operations 3 Enter filter parameters in Export Inclusion List Step 2 of 2 Results Interpretation x Save in rr MPP Data F E E Recent Items File name Export Inclusion List Save Files of type Comma separated file csv v Cancel Figure 61 Choose a file dialog box h Click Next ElExport Inclusion List Step 1 of 2 b x Entity List and File Path Chooser Select the Entity List Entity List Filtered on Flags accCalls P M filterCondi Choose Output File C MassHunter Data MPP Data Export
113. he range of entity lists you want to select Select discontinuous or individual entity list press Ctrl and click on additional entity lists Note If your entity list search results span more than one page and you want to make range and or individual entity list selections across multiple pages click 41 Integrated Biology operations 6 Choose entity lists in Find Similar Entity Lists Step 2 of 3 Results Interpretation Back and increase the value for Max results per page so that all of the results are on a single page Blentitytist Search Wizard Step 2 of 2 E Search Results The table below shows the search results Select the entity lists that you would like to use for further analysis and click Finish Per page only the max number of search results specified in the first page of wizard will be shown T test p lt 0 25 Created from Significance Test Technology MassHunterQual UNIDENTIFIE Filtered by frequency conditio Created from Significance Test MassHunterQual IDENTIFIED_U 2Way ANOVA p Corr Infectio Created from Significance Test MassHunterQual LCMS_UNIDE Filtered by frequency conditio Created from Significance Test MassHunterQual LCMS_UNIDE Filtered on Flags accCalls P Created from Significance Test MassHunterQual LCMS_UNIDE Filter By Frequency with cut off Entity List Filt
114. he structure and function of proteins occurring in living organisms Proteins are assemblies of amino acids polypeptides based on information encoded in the genes of an organism and are the main components of the physiolog ical metabolic pathways of the organism A feature attribute and or characteristic element whose presence absence or inability to be properly ascertained due to instrumental factors is factored into whether a sample is or is not representative of the larger specimen Reapplying the same algorithm to a subset of a previous result in order to generate an improved result A three step process in the metabolomics workflow that improves the accuracy of finding statistically significant features in sample data files Step 1 Find untargeted compounds by molecular feature in MassHunter Qualitative Analysis Step 2 Filter the molecular features in Mass Profiler Professional Step 3 Find targeted com pounds by formula in MassHunter Qualitative Analysis Importing the most signifi cant features identified using Mass Profiler Professional back into MassHunter Qualitative Analysis as targeted features improves the accuracy in finding these fea tures from the original sample data files 117 Reference information Reduction Regression analysis Replicate Sample Sample class prediction Specimen Spike Standard Standard deviation Definitions The process whereby the number of variables in a data set is d
115. her iterative analy sis of those genes Note You must have an account with Ingenuity Systems www ingenuity com in order to make use of the Launch IPA operation 1 Launch Launch IPA tn the a Click Launch IPA in the Workflow Browser Workflow Browser The Launch IPA operation has one 1 step as shown in Figure 89 on page 77 This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data Is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide 76 Integrated Biology operations Pathway Analysis Launch IPA Choose IPA p Create New Pathway s INGENUITY Analysis to run dialog box SYSTEMS or Perform Data Analysis Create New on Experiment Pathway dialog box or Perform Data Perform Data Analysis Analysis on on Entity List Experiment dialog box Perform Data Analysis on Entity List Figure 89 Flow chart of the Launch IPA operation 2 Select the IPA Analysis to a Select the Choose IPA Analysis to run run Create Pathway in IPA sends an Entity List from Mass Profiler Professional to IPA and uses those genes to create a pathway in IPA This pathway can then be subjected to further manipulation and analysis in IPA by growing a node remov ing nodes and interactions and interrogating a node or an interaction Perform Data Analysis on Experiment sends an entity list and the associated gene exp
116. hically Positive lons Charge carriers Mark H and Na Neutral losses Clear H20 Charge states if not known Type 1 for Charge state range Aggregates Clear Dimers and Trimers Negative lons Charge carriers Mark H HCOO and CH3C00 Neutral losses Clear H20 Charge states if not known Type 1 for Charge state range Aggregates Clear Dimers and Trimers Scoring Contribution to overall score Type 100 for Mass score Type 60 for Isotope abundance score Type 50 for Isotope spacing score Type 100 for Retention time score Expected data variation Type 2 0 mDa and 5 6 ppm for MS mass Type 7 5 for MS isotope abundance Type 5 0 mDa and 7 5 ppm for MS MS mass Type 0 115 min for Retention time Search Mode lon search mode Select Cation or anion entries Search Results Search Results Mark Limit to the best 50 Integrated Biology operations Results Interpretation 8 compound Identification Wizard i x Allowed spece E Charge State Scoring Compound Identification Limits on input masses Maximum neutral mass for which formulas should be 750 0000 calculated Limits on results I Minimum overall score 35 000 T Maximum MS mass error 7 5000 opm z I Require DBE from foo two foo 9 Identify Compounds Generate Formulas 0 0025 m z plus 7 0 ppm Common organic molecules 7 IV Limit assigned charge states to a maximum of 2 I Treat ions with unassigned charge as singly charged lt lt Back Nex
117. ilar conditions Overlaying data on a signaling path way can provide an understanding of the cause and effect relationships 61 Integrated Biology operations Pathway Analysis between the genes or proteins of interest and provide insight into the mecha nism of a specific condition under study e Which small molecules might interact with a gene or set of genes Getting started requirements Pathway Analysis requires the following 1 A valid license for the Pathway Architect module See the Agilent G3835AA MassHunter Mass Profiler Professional Quick Start Guide and the Mass Pro filer Professional User Manual for information about licenses in Mass Profiler Professional 2 A valid license for the GeneSpring GX module See the Agilent G3835AA MassHunter Mass Profiler Professional Quick Start Guide and the Mass Pro filer Professional User Manual for information about licenses in Mass Profiler Professional 3 Pathways from sources of interest for the organism under study See section 11 1 2 Importing Pathways into Mass Profiler Professional in the Mass Pro filer Professional User Manual for more information about pathway sources and creating pathways 4 Supporting databases to perform Pathway Analysis for a single organism or across different organisms Pathway Analysis is supported by organism spe cific interaction databases BridgeDb databases and HomoloGene annota tions See section 11 1 5 Supporting Databases for Pathway A
118. ilent publication January 2012 e Agilent MassHunter Workstation Software Qualitative Analysis Familiarization Guide Agilent publication G3336 90018 Revision A September 2011 e Agilent MassHunter Workstation Software Quantitative Analysis Familiarization Guide Agilent publication G3335 90108 First Edition June 2011 e Proteomics Biomarker Discovery and Validation Agilent publication 5990 5357EN February 11 2010 e Metabolomics Approaches Using Mass Spectrometry Agilent publication 5990 4314EN October 27 2009 e Multi omic Analysis with Agilent s GeneSpring 11 5 Analysis Platform Agilent publication 5990 7505EN March 25 2011 e An LC MS Metabolomics Discovery Workflow for Malaria Infected Red Blood Cells Using Mass Profiler Professional Software and LC Triple Quadrupole MRM Confirmation Agilent publication 5990 6790EN November 19 2010 e Profiling Approach for Biomarker Discovery using an Agilent HPLC Chip Coupled with an Accurate Mass Q TOF LC MS Agilent publication 5990 4404EN October 20 2009 e Metabolite Identification in Blood Plasma Using GC MS and the Agilent Fiehn GC MS Metabolomics RTL Library Agilent publication 5990 3638EN April 1 2009 e Metabolomic Profiling of Bacterial Leaf Blight in Rice Agilent publication 5989 6234EN February 14 2007 120 Reference information D Fa Yank aN s Ane Presentations Product Rrr P h irae procnures References Advances in Instrumentation
119. imum abundance in each isotope cluster 3 Charge State Preference filter If Prefer highest abundance charge state s is selected then the peaks per compound are listed in descending order of abun dance Otherwise if Specify charge state preference order is selected only those peaks whose charge states are specified in the Active window are passed and ordered as specified For example if you specified Charge states as 2 3 and gt 3 then peaks with charge states 1 are filtered out and the peaks with charge states 2 3 and gt 3 are passed The results are ordered with charge state 2 then all peaks with charge state 3 and finally those with a charge state gt 3 in descending order of abundance 4 Minimum lon abundance filter passes only the peak ions with an abundance greater than the specified value 5 Limit number of precursor ions passes only the top number of peaks com pounds as specified 58 Integrated Biology operations 4 Review the exported inclusion list Import Annotations 1 Launch Import Annota tions in the Workflow Browser 2 Select the entity list to import annotations Results Interpretation The results from Export Inclusion List are saved in a CSV file and include the m z charge state retention time and delta retention time You can review your results without the Mass Profiler Professional software a Open the CSV file using a text editor or spreadsheet program b Review the results
120. is Multi Omic Analysis MOA will be saved in a newly created MOA Experiment with the following details Name Moa Differentiation by Infection and Treatment One Variable Data Set Pathway List resulting From Multi Omic Analysis with the Following settings Selected Annotations For Experiment 1 KEGG ID oh Source Multi Omic Analysis Number of Pathways 1278 Experiment 1 Differentiation by Infection and Treatment Experiment 1 Type Metabolme n Interpretation 1 infection Treatment Entity List1 Filtered by frequency conditions 100 0 1 Experiment 2 One Variable Dataset S Experiment 2 Type JMetabolomic Interpretation 2 Numeric Parameter Entity List 2 Filtered by frequency conditions 100 0 1 Creation date Thu Mar 14 00 01 10 MDT 2013 Last modified date Thu Mar 14 00 09 59 MDT 2013 0 Owner user Matched Entities Differ Pathway Entities of Ex Matched Entities One Y Pathway Entities of Ex Hs_TCR signaling WP 19 1 10 0 10 Homo sapiens Hs_Prostanoid_metaboili Homo sapiens Hs_Signaling_by_PDGF_ Homa sapiens Hs Sinnalinn hy Rahn r 1 Hamn saniens Eind Find Next Find Previous 7 Match Case Notes Pathways lt lt Back Figure 87 Save Pathway List page Multi Omic Analysis Step 4 of 4 75 Integrated Biology operations Pathway Analysis mass Profiler Professional MOA Differentiation by Infection and Treatment O
121. is of variance which is a statistical method that simultane ously compares the means between two or more attributes or parameters of a data set ANOVA is used to determine if a statistical difference exists between the means of two or more data sets and thereby prove or disprove the hypothesis See also t Test Another term for an independent variable Referred to as a parameter and is assigned a parameter name during the various steps of the metabolomic data analy SIS Another term for one of several values within an attribute for which exist correlating samples Referred to as a condition or a parameter value and given an assigned value during the various steps of the metabolomic data analysis A technique used to view and compare data that involves converting the original data values to values that are expressed as changes relative to a calculated statisti cal value derived from the data The calculated statistical value is referred to as the baseline A term used to refer to statistical techniques named after the Reverend Thomas Bayes ca 1 02 1 61 The use of statistical reasoning instead of direct facts to calculate the probability that a hypothesis may be true Also known as Bayesian statistics The use of computers statistics and informational techniques to increase the understanding of biological processes 110 Reference information Biomarker Carbohydrate CEF file Cell Census Cheminformatics Chemo
122. is the same used by Mass Profiler Professional Select the Gene Identifier Column The gene identifier is used to map genes in the entity list to genes in the IPKB Optional Mark Save Pathway The new pathway is saved in IPA to the specified Project Folder within My Pathways under the specified Pathway Name Click OK Your default Internet browser is automatically launched and connected to the IPA server as specified in the IPA Server Address Entity List away ANOVA p Corr Infec IPA Server Address Janalysisingenuity com Pathway Name 2Way ANOVA p Corr Infection cut off p lt 0 05 Project Folder Two variableDataSet 000 Gene Identifier Column lntrezGeneID i t i OU Ci Help OK Cancel Figure 9 Create New Pathway dialog box Sign in to IPA as shown in Figure 93 on page 79 Note Information on how to use IPA is covered in section 11 6 1 Ingenuity Path ways Analysis IPA Connector in the Mass Profiler Professional User Manual and accessed from the Quick Start page of IPA as shown in Figure 94 on page 79 18 Integrated Biology operations 4 Enter the options for Perform Data Analysis on Experiment Quick Start Pathway Analysis Discover the Biology oa AS INGENUITY Welcome Please login Contact Customer Support Email Live Chat EK MELLEN Password Customer Support l Remember my password Phone 650 381 5111 Hours 6am Spm PST Relea Monday Frida
123. itional handwritten signa tures For more information see http www fda gov RegulatoryInformation Guidances ucm125067 htm MassHunter Data Acquisition Compliance Software includes the following features which support 21 CFR Part 11 compliance e Hash Signature for data files let you check the integrity of files during a compli ance audit e Roles that restrict actions to certain users e Method Audit Trail Viewer MassHunter Quantitative Analysis Compliance Software includes the following fea tures which support 21 CFR Part 11 compliance e Security measures ensuring the integrity of acquired data analysis and report results e Comprehensive audit trail features for quantitative analysis using a flexible and configurable audit trail map e Customizable user roles and groups let an administrator individualize user access to processing tasks Before you begin creating methods and submitting studies you may decide to install MassHunter Data Acquisition Compliance Software and MassHunter Quantitative Analysis Compliance Software The Quantitative Analysis Compliance program is installed separately from the Quantitative Analysis program See Agilent MassHunter Quantitative Analysis Com pliance Software Quick Start Guide Agilent publication G3335 90099 Revision A February 2011 for instructions on installing the Compliance program The Data Acquisition Compliance program is installed automatically with the MassHunter Data Acquisiti
124. ketone_body_metabolism_wWP1817_4 Hs_Nucleosome_assembly_wWP1874_42092 Hs_Signaling_by_EGFR_WP1910_45218 Hs_Interferon_Signaling_wWP183 7_46942 Hs_Nucleotide_Excision_Repair_WP1980_42219 Hs_Interleukin 1_signaling_WP1839_44873 Hs_Metabolism_of_amino_acids_and_derivatives_wWP1847_523 73 Custom Save Figure 77 page Single Experiment Analysis Step 3 of 4 UolololRlolNio niolBlolole lt lt Back Next gt gt Finish Cancel Pathways selection on the Single Experiment Analysis Results 3 Review the content and parameters in the Pathway List Inspector dialog box ElPathway List Inspector x Pathways Hs_TCR_signaling_w Homo sapiens Name SEA Filtered by Frequency conditions 100 0 1 Notes Pathway List resulting From Single Experiment Analysis with the Following settings Selected Annotations for Experiment KEGG ID ChFRT In xl Source Custom Save from Single Experiment Analysis Number of Pathways BO Experiment Differentiation by Infection and Treatment Experiment Type JMetabolomic SS C Interpretation finfection Treatment ss lt i SSC S Entity List Filtered by Frequency conditions 100 0 1 Creation date Tue mar 12 13 50 02 MoT 2013 2 Last modified date Tue Mar 12 13 50 14 MDT 2013 0 Owner fxuser i i s SC O
125. kipathways org and BioCyc http www biocyc com or import path ways from other sources in the BioPAX Level 2 and Level 3 GPML or Text format e Create your own interaction networks from a database of biological and chem ical entities relationships between entities and properties of these entities and relationships derived from a proprietary Natural Language Processing NLP algorithm e Determine which of the created or imported pathways have significant overlap with a specified list of entities from one experiment Single Experiment Anal ysis or two experiments of the same or differing experiment types Multi Omic Analysis e View and investigate pathways and interaction networks in an interactive pathway viewer and overlay your experimental data on these pathways e Export your data to other popular pathway analysis tools like Ingenuity Path ways Analysis IPA MetaCore and Cytoscape In the case of IPA you can also import entity lists resulting from pathway analysis into GeneSpring Pathway Analysis can help you answer questions such as e What biological pathways and processes are significantly represented by the experiment e What other entities and pathways reported in literature are affected by the results of the experiment e ls there a pattern in the expression of connected genes across different exper imental conditions or is there a pattern of different entity types as measured by experiments under sim
126. l User Manual After you have imported and organized your data and then created an initial dif ferential analysis MPP places you in the advanced workflow mode where you have access to all features available in Mass Profiler Professional through the Workflow Browser If you are using the two variable experiment data set or similar you see a display similar to that shown in Figure 29 on page 37 You are ready to perform the integrated biology operations 36 Integrated Biology operations Overview of operations mates Profiler Professional My Two Yariable Experiment Project Search View Tools Annotations Windows Help RETEST SS ME TEU CE MIEGISS Ps SIS ASICs CA C Display Pane Project Navigator List of the experiments within E ES S i the current project we wea iw aw y YS P annotations o ized Abundance Values S S x meer ch PNA AFAR MARA ARAL ATS VIN a J analyses and favorites within cabs 7 SS SSS ia the selected experiment _ Wi Export to MetaCore Connect to Cytoscape Color key related to the current view SERGE x NLP Network Discovery MeSH Network Builder Extract Relations via NLP garpage can to reaquce memory usage Utilities R 1 1_Contr 1 2_Con 1 3_Con 1 4_Con 3 1_Con 3 2_Con 3 3_Con 3 4_Con 2 1_Infe 2 2_Infe 4 4I Remove Entities with missing signal values Analysis Significance Testing and Fold Chan
127. l at the end of this process h Type your Experiment prefix The exported data is contained within an experi ment in MetaCore and this option sets a prefix string to name the experiment The default is a time stamped string Select the Gene Identifier Column This option sets the identifier of the data col umn that is exported to MetaCore Currently there is only one option Entrez Gene ID Note f the Entrez Gene ID annotation is not present for the technology of the chosen entity list you must update the technology with Entrez Gene ID annota tions before proceeding j Click OK ElExport to MetaCore x Parameters Entity List Fold change gt 2 0 Choose Interpretation Treatment Choose MetaCore Server Address lhttp portal genego com Experiment prefix fos_imp_2013 03 271115 Gene Identifier Column Entrez Gened i itsti SC s YS Help Cancel Figure 103 Export to MetaCore dialog box Parameters a Select the data column for the Choose data column b Type in an Experiment suffix to be added The column name is used if no charac ters are entered c Click OK Plexport to MetaCore x Column selection Choose data column Treated v Experiment suffix to be added leave empty to use column name Help Cancel Figure 104 Export to MetaCore dialog box Column selection a Click OK Your default browser is launched information x i The MetaCore page will now be opened i
128. lation table 1 Standard Popset E a Mitochondrial genetic code Translation table 2 Vertebrate Mitochondrial pst PubMed Central 35 35 Lineage full Taxonomy 2 1 cellular organisms Eukaryota Opisthokonta Metazoa Eumetazoa Bilateria Deuterostomia Chordata Craniata Vertebrata Gnathostomata Teleostomi Euteleostomi Sarcopterygii Tetrapoda Amniota Mammalia Theria Eutheria Laurasiatheria Cetartiodactyla Ruminantia Pecora Antilocapridae Antilocapra Genome Information Trace records raw single pass reads of DNA sequence Sequencing Center Name Record counts per type PCR ALL BAAR TIADA ARS TTS Maat Animal Recaarch Mm antar Figure 141 The Entrez Taxonomy database for a North American mammal c Type the value for the Taxonomy ID d Type the exact Scientific Name including capitalization and spaces e Type the Common Name using in your own style and spelling f Click OK A progress box is displayed while the organism is added to Mass pro filer Professional Elcreate Pathway Organism x Enter the following details Taxonomy id 694474 Scientific Name Antilocapra americana Common Name Pronghorn antelope 00 Cancel Progress Creating Organism Cancel Information x 0 Organism created successfully Figure 142 Create Pathway Organism dialog box organism creation progress and Information dialog box indicating the successful creation of your org
129. lid Pathway Architect module license See Getting started requirements on page 62 NLP Networks consists of three operations e NLP Network Discovery on page 93 e MeSH Network Builder on page 99 e Extract Relations via NLP on page 102 Useful supplemental task also documented e Create Pathway Organism on page 106 Create networks based on information in PubMed abstracts and identify interactions associated with Medical Subject Headings MeSH terms using NLP as an alternate way of creating pathways based on terms and concepts instead of entities Once you have created and saved such networks in Mass Profiler Professional you can overlay data from your experiments on these networks to help you find significant pathways and networks NLP uses a method that carefully parses your sentence structure to maximize accu racy and control different aspects of a sentence without compromising recall reli ability The NLP system operates on a sentence by sentence manner and extracts only those relations that are completely within a sentence NLP employs four main phases to ensure accuracy entity recognition syntax analysis semantic analysis and semantic inference See section 12 1 Natural Language Processing NLP in Mass Profiler Professional in the Mass Profiler Professional User Manual for more information The Pathway Analysis module is integrated with a database of relations between various biological molecules and
130. lp Script Editor R Editor ae al Agilent Single Color Demo Sy Experiments i IB One ariable 5l Samples 7 Interpretations amp Analysis 3 Agilent Single Color D Samples Interpretations Sy Analysis n R Script Remotely Import Pathways from WikiPathways Hj Agilent Single wil IB One Variable Import Pathways from BioCyc gt Import Pathway From File gt Import from Pathway Architect Import BROAD GSEA4 Gene sets Import NCBI GEO Experiment Export 4rrayExpress MAGE TAB Al Entities ar Prepare For GS Migration Migrate from GS Memory Monitor Edit Pathway Theme Create Custom Ref Configuration dialog Backup Repository Restore Backup Change Repository Figure 108 Launching the configuration options from the menu bar b Click Miscellaneous on the left hand pane in the Configuration Dialog dialog box Click Cytoscape Installation Path on the left hand pane in the Configuration Dia log dialog box Determine if you have a user entry field to type a Cytoscape installation path see Figure 109 on page 87 If the entry field is available continue to the next step Note f the entry field Cytoscape installation path not available stop at this step and contact Agilent support to activate this feature Click Browse to select the Cytoscape installation path in the Configuration Dia log Select the
131. lp Cancel Figure 13 Create New Project dialog box Specify whether the wizard guides you through creating a new experiment or whether the wizard opens an existing experiment a Click Create new experiment b Click OK lexperiment Selection Dialog x Choose whether you would like to be guided through the creation of a new experiment or if you would like to open an existing experiment from 4 previous project Open existing experiment Help Cancel Figure 14 Experiment Selection Dialog dialog box Available entry options for the New Experiment dialog box depend on your experi ment type and data sources a Type a descriptive name for the experiment in Experiment name Agilent Single Color Demo b Select Expression for the Analysis type Only your licensed analysis types are available c Select Agilent Expression Single Color for the Experiment type d Select Analysis Biological Significance for the Workflow type e Type descriptive notes for the experiment in the Experiment notes f Click OK x Experiment description Enter a name analysis type experiment type and a desired workflow type Analysis will guide you through a statistical significance test and fold change analysis Data Import will quide you through experiment creation only Class Prediction will guide you through the creation and testing of a prediction model using imported training data Experiment
132. ly if it occurs in Min Frequency number of Pubmed articles PMIDs associated with the chosen MeSH term Memory Episodic 3 3 Memory 6 588 9 598 IV Memory Short Term 947 947 C Amnesia nterograde 12 12 E Repression Psychology 2 6 a Immunologic Memory 149 149 ia CD ROM 1 1 Ci Memory Long Term 61 61 E Amnesia Retrograde 67 67 C Neuropsychological Tests 476 479 C Memory Disorders 2 539 3 226 Select Type Exact relations C All relevant relations Min Frequency fi lt lt Back Next gt gt Finish Cancel Figure 130 Select relevant MeSH terms page MeSH Network Builder Step 2 of 4 MeSH Pathway displays the created pathway The number of entities and the num ber of relations are displayed a Review the pathway b Edit the pathway Details for using the pathway view is described in section 11 1 3 Creating and Editing Pathways in the Mass Profiler Professional User Manual c Click Next 100 Integrated Biology operations 5 Save the pathway list in MeSH Network Builder Step 4 of 4 ElMesH Network Builder Step 3 of 4 x MeSH Pathway MeSH Pathway for given search term Displaying 819 entities and 1488 relations we Sty MA S ac y g oe AR Ms ae 7 emt oS eT eae a yee Wa i Cope K FaN B Ne Se ea v has s A gt ni z lt URATA AN a z ad R D gt aa NAS ARLEN ANa oe AE OER a Ea Sy E Mire eS a R LA gt tor 4 tg Fy Geaa SN
133. m Figure 126 Analysis Results page NLP Network Discovery Step 4 of 5 Analysis Result displays the created pathway The initial number of entities the number of new relations and the number of new entities are displayed a Review the pathway list b Type a descriptive Name that is stored with the saved pathway entity list 98 Integrated Biology operations MeSH Network Builder 1 2 Launch MeSH Network Builder in the Workflow Browser Input parameters in MeSH Network Builder Step 1 of 4 NLP Networks c Edit the Notes that are stored with the saved pathway entity list d Double click a row in the Pathways table to launch the Pathway Inspector to review the entities and relations contained in the new pathway e Click Finish Elnr Network Discovery Step 5 of 5 f x Save Pathway List This window displays the details of the Pathway List that will be created on clicking Finish You can change the default Name and edit Notes of the Pathway List here as required The new Pathway List contains the pathway listed in the table To change the name of the new pathway double click the corresponding row and change the name in the Multiple Objects Inspector that opens Name Direct Interactions Notes Pathway List resulting From NLP Network Discovery with the Following settings Input list DOWN FC Untreated vs Treated Pathway analysis Direct Interactions Relations score gt 9 Rel
134. m published litera ture abstracts using NLP a Click NLP Network Discovery in the Workflow Browser This operation is illustrated with Agilent Expression Single Color Demo sample data provided with your Mass Profiler Professional installation The data is ini tially imported and analyzed following the Creating an expression analysis using the sample array experiment on page 23 of this workflow guide The NLP Network Discovery operation has five 5 steps as shown in Figure 120 on page 94 The steps that you use depend on your selection for the Analysis Type in the first step of the wizard The new pathway list is placed in the Analysis folder within the Experiment Navigator 93 Integrated Biology operations NLP Networks NLP Network Discovery Input Parameters 1 of 5 Analysis Results Save Pathway List 4 of 5 5 of 5 Matching Statistics Analysis Filters Pathway Wizard order depends on Analysis Type selection Figure 120 Flow chart of the NLP Network Discovery operation 2 Input parameters in NLP a Select an Input List By default the active entity list is selected The active entity Network Discovery Step 1 list must belong to the active experiment otherwise an error indicating No rela of 5 tions found may be encountered b Select an Analysis Type from the two choices Your selection determines the available option for Algorithm and the steps through the wizard as shown in Figure 120 Simple
135. ment that proposes a possible correlation for example a cause and effect between a set of independent variables and the resulting metabolic profile The workflow is used to prove or disprove the hypothesis Before your begin collecting your samples it is important to understand how any one sample represents the population as a whole Because of natural variability and the uncertainties associated with both the measurement and the population no assur ance exists that any single sample from a population represents the mean of the population Thus increasing the sample size greatly improves the accuracy of the sample set in describing the characteristics of the population Sampling the entire population is not typically feasible because of constraints imposed by time resources and finances On the other hand fewer samples increase the probability of concluding a false positive or false negative correlation At a minimum it is recommended that your analysis include ten 10 or more repli cate samples for each attribute value for each condition in your study System suitability involves collecting data to provide you with a means to evaluate and compensate for drift and instrumental variations to assure quality results The techniques that produce the highest quality results include 1 retention time align ment 2 intensity normalization 3 chromatographic deconvolution and 4 base lining However even the best analysis techniques cannot
136. metrics Child Co elution Complex Composite spectrum Definitions An organic molecule whose presence and concentration in a biological sample indi cates a normal or altered function of higher level biological activity An organic molecule consisting entirely of carbon hydrogen and oxygen that is important to living organisms A binary file format called a compound exchange file CEF that is used to exchange data between Agilent software In the metabolomics workflow CEF files are used to share molecular features between MassHunter Qualitative Analysis and Mass Pro filer Professional The fundamental unit of an organism consisting of several sets of biochemical func tions within an enclosing membrane Animals and plants are made of one or more cells that combine to form tissues and perform living functions Collection of a sample from every member of a population The use of computers and informational techniques such as analysis classification manipulation storage and retrieval to analyze and solve problems in the field of chemistry A science employing mathematical and analytical processes to extract information from chemical data sets The processes involve interactive applications of tech niques employed in disciplines such as multivariate statistics applied mathematics and computer science to obtain meaningful information from complex data sets Chemometrics is typically used to obtain meaningful information fr
137. mics proteomics and metabolomics experiments Entity lists may con tain genes proteins or metabolites Multi Omics Analysis helps you determine in which biological pathways there exists a significant enrichment of compounds of interest based on the input entity list You can choose an organism for pathway analysis that differs from the organism associated with your experiment Curated pathways such as WikiPathways BioCyc pathways and BioPAX pathways as well as NLP and MeSH created pathways can be individually selected as sources for Pathway Analysis a Click Multi Omics Analysis in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The Multi Omics Analysis operation has four 4 steps as shown in Figure 82 The MOA results are assigned a new project in the Project navigator and the MOA pathway lists are placed in the Analysis folder within the Experiment Navigator Multi Omics Analysis Input Input Multi Omics Save Experiments Parameters Analysis Results Pathway List 1 of 4 2 of 4 3 of 4 4 of 4 Experiment Custom Information Save Organism Pathways Figure 82 Flow chart of the Multi Omics Analysis operation a Review the selected Experiment 1 The default experiment is the active experi ment in the open
138. mple Definitions The probability of obtaining a statistical result that is comparable to or greater in magnitude than the result that was actually observed assuming that the null hypothesis is true The null hypothesis is stated that no correlation exists between the independent variables and the measurements taken from the samples Rejection of the null hypothesis is typically made when the p value is less than 0 05 or 0 01 A p value of 0 05 or 0 01 may be restated as a 5 or 1 chance of rejecting the null hypothesis when it is true When the null hypothesis is rejected the result is said to be statistically significant meaning that a correlation exists between the indepen dent variables and the measurements as specified in the hypothesis Another term for an independent variable Referred to as a parameter or parameter name and is assigned a parameter name during the various steps of the metabolo mic data analysis See also condition and attribute Another term for one of several values within a parameter for which exist correlating samples Parameter value may also be referred to as a condition during the various steps of the metabolomic data analysis See also attribute value The original set of information that is processed by an algorithm to create one or more subsets of information A subset entity list is referred to as the child of a par ent entity list Linear chain of amino acids that is shorter than a protein The length of
139. mpound Identification Wizard is automatically started to help you identify your entities This is the first of two dialog boxes related to this wizard a Select Identify all compounds for Compound selection b Mark Database search for Compound identification methods c Mark Molecular Formula Generator MFG d Select Generate formulas only for unidentified compounds Generate formulas for the compounds that are not identified by the database search or the spectral library search if marked e Click Next 8 compound Identification Wizard E x Compound Identification Browser Please select the identification methods you wish to apply to the compounds in this CEF file Compound selection C Identify only highlighted compounds C Identify only unidentified compounds Identify all compounds M Compound identification methods M Database search CSV PCD METLIN I Spectral library search M Molecular formula generator MFG Generate formulas for all compounds Generate formulas only for unidentified compounds Help Back Next gt gt Finish Cancel Figure 47 Parameters related to compound selection and compound identification methods in the Compound Identification Wizard The parameters that control the compound identification technique are entered in this second dialog box of the Compound Identification Wizard a Select Search Database under Identify Compounds b Enter parameters for the Se
140. n a browser window Figure 105 Information dialog box indicating that a browser window is required 84 Integrated Biology operations 5 Log into MetaCore Connect to Cytoscape Pathway Analysis a Review the progress of your browser window A submittal notice is displayed by your browser as shown in Figure 106 before you are directed to the MetaCore in x File Edit view History Bookmarks Tools Help Sfile C Pragram 2 pp tmp MetaCore html i files C Program Files Agilent MassHunter Workstation MPP12 5 app tmp MetaCore html gt Google P A Submitting experiment info please wait Internet Explorer Users your browser s security settings may require you to allow Active Content on this page Please click at the information bar at the top of the page if nothing happens in 3 seconds or click Submit Query ifno information bar is displayed Waiting For portal genego com Figure 106 MetaCore submittal notice in your browser b Enter your Username and Password to log into your MetaCore account I MetaCore Login Thomson Reuters Mozilla Firefox loj x File Edit view History Bookmarks Tools Help MetaCore Login Thomson Reuters partal genego com caifimport_genes cai B gt Google p A E 3 THOMSON REUTERS THOMSON REUTERS SYSTEMS BIOLOGY SOLUTIONS LOGIN Username YOUR GPS IN PATHWAY ANALYSIS a Whether you want to reduce the
141. n page 106 to add a new organism NLP Network Discovery is performed on entity lists and selected entities in a path way viewer To perform NLP Network Discovery on custom lists of entities you can create a Pathway experiment The queried database corresponds to the organism specified in the technology of the current experiment Mass Profiler Professional uses Entrez Gene ID Swiss Prot and Gene Symbol from the technology for this query to map to available Entrez Gene IDs and available entries in the Protein field and the Symbol field of the Interaction Database respectively It is important that both the technology and the Interaction Database contain at least one of these annotations The NLP Network Discovery operation has two options for exploring the most com mon functionalities of network discovery Simple Analysis Provides you with a selection of the most common functional ities of network discovery The default settings for guiding you through the simple network discovery workflow include e matching the selected entities to entities in the database e retrieving relevant relations between the set of matched entities e displaying the results in a network graphical view Advanced Analysis Enables you to change and specify the details at every step of the network discovery process Organism specific Interaction Databases are available as updates to Mass Profiler Professional The relations in the database are mainly derived fro
142. nalysis in the Mass Profiler Professional User Manual for more information What is BridgeDb Pathways acquired from different sources may refer to the same entity using synon ymous names and or identifiers from different biological databases Incorporating data from multiple databases leads to variations in annotations BridgeDb http www bridgedb org is an identifier mapping framework for bioinformatics applica tions and provides mapping for the same entity across different biological data bases Single Experiment Analysis and Multi Omics Analysis use BridgeDb to search for pathways that match the entities in the your entity list s Click Annotations gt Update BridgeDb gt From Agilent Server to update BridgeDb See section 11 5 2 BridgeDb ID Mapping and section 11 1 5 Supporting Databases for Pathway Analysis in the Mass Profiler Professional User Manual for more information Improving your Pathway The results from performing a Pathway Analysis are dependent upon 1 the number Analysis results of annotated entities in your experiment and 2 the quality of the annotations Enti ties from proteomics and genomics experiments typically provide greater pathway analysis accuracy less ambiguity because these entities are more highly anno tated When you are using entities from metabolomics experiments you can improve your analysis accuracy by 1 using GCMS data that has been identified in a spectral library search 2
143. nalysis E amp all Entities 3 Filtered on Flags Detected Not Detected E T test p lt 0 05 3 UP FC Untreated vs Treated 3 DOWN FC Untreated vs Treated o ij GO Analysis p lt 0 05 SIs P S UUS E molecular _function 2 biological_process a cellular_component Figure 101 Choose Entity List dialog box c Click OK d Review the Interpretation The active interpretation list is selected e Click Choose to select a different Interpretation The interpretation allows you to control which type of data is sent to MetaCore sample wise or condition wise average or non averaged If averaged data is selected the intensity values are averaged across samples in that condition If a non averaged interpretation is chosen then you can send data one sample at a time Choose Interpretation x i5 Agilent Single Color Demo El QY Interpretations Ge All Samples Qi Treatment Non averaged Pag Treatment i Cancel Figure 102 Choose Interpretation dialog box 83 Integrated Biology operations 3 Enter the column selection in the Export to MetaCore dialog box 4 Approve opening a browser window to log into MetaCore Pathway Analysis f Click OK g Type the MetaCore Server Address The default address is http portal genego com The address can be changed to point to your organization s installation You must have a valid account on the server in order to be able to login to the porta
144. name Agilent Single Color Demo Analysis type Expression v Experiment type Agilent Expression Single Coor 0 i workflow type Janalysis Biological Significance o n Experiment notes Demo Agilent Data Help Cancel Figure 15 Experiment description in the New Experiment dialog box 25 Example experiments Import the sample data 1 Load data from the New Experiment dialog box 2 Select the sample files in the Open dialog box 3 Review the sample data in the New Experiment dialog box Creating an expression analysis using the sample array experiment a Click Choose Files x Load Data Click to choose either data files or samples to be used in this experiment Click Finish when all data files or samples have been added Selected files and samples Choose Files Choose Samples Reorder Remove oc ee Figure 16 Load Data from the New Experiment dialog box a Select the sample data files to open b Click Open x Look in lu Agilent Expression Single Color Demo nd pA 522502705 251209747382 Recent Items My Documents Ly Computer ial File name jintreated txt US22502705_251209747404_Treated txt Open e Jii Files of type txt files txt z Cancel Figure 17 Open dialog box a Review the selected sample files b Click OK A progress dialog box is shown while importing the sample files x Load Data Click to choose either data files
145. ne ariable Data Set R 0 x Project Search View Tools Annotations Windows Help BBae Aa Project Navigator Q Differentiation by Infection and Treatment One ariable Data Set 3 MOA Differentiation by Infection and Treatment One Yariable Data Set l Workflow A yl Two Yariable Data Set Xe E Experiments HEak s GH m ii t SER a ea E E A E TA x li Differentiation by Infection and Treatment f Hs etabolism_of_water soluble_vitamins_and_cofactors_WP1857_44904 Quick Start Guide Halil One Variable Data Set Homo sapiens ik One Variable Human WikiPathways All Pathways Reactome http www reactome org Utilities p i MOA Differentiation by Infection and Filter on Entity List gt Export to e rray iil Differentiation by Infection and Treatment Filtered by frequency condition B 2way ANOVA Hs_TCR_signaling_WP1927_45094 E 2Way ANOVA p Corr li Hs_Signaling_by_Robo_receptor_WP191 E 2Way ANOVA p Corr Ir Hs_Sphingolipid_Metabolism_WP1923_4 2Way ANOVA p Corr T ji E Union 2Way ANOVA cut i 5EA Filtered by frequency cc SE4 Filtered by Frequency cc My Favorites Hs_Fatty_acid triacyighcerol and_keto Hs_Signaling_by_EGFR_wWP1910_45218 Hs_Metabolism_of_amino_acids_and_der acam Hs_Factors_involved_in_megakaryocyte_ Hs_L1CAM _interactions_WP1843_44884 Hs_Neurotransmitter_uptake_and_M
146. nloads Cyto 9 Google Index of downloads Cyto Name Last modified Size Description o Parent Directory i Cytoscape Patch n Plugins zip 03 Jul 2012 01 02 15M C Crtoscape Patch n Plugins 03 Jul 2012 01 01 Apache 2 2 14 Ubuntu Server at basil strandis com Port 80 Opening Cytoscape_Patch_n_Plugins zip x You have chosen to open oD Cytoscape_Patch_n_Plugins zip which is a Compressed zipped Folder 15 0 MB from http basil strandls com m What should Firefox do with this file C Open with winzip default A i I Do this automatically For files like this from now on Cancel Figure 117 Cytoscape_Patch_n_Plugins zip file location and Save File e Open Cytoscape_Patch_n_Plugins zip The files included in the zip file are AdaptiveJavaHelp jar CriteriaMapper jar CytoscapeConnector 1 0 SNAPSHOT jar GeneSpringConnector 1 0 SNAPSHOT jar GOElitePlugin jar gpml jar HeatMapViewer 2 2 1 jar HeatStripPlugin jar PathwaySearchPluginWithLibs jar 90 Integrated Biology operations SendGenesAndEnrichmentFilesToCytoscapeSpy class SendMetabolitesAndInterpretationToCytoscapeSpy class README txt Pathway Analysis f Copy the nine 9 jar files to the p ugins folder in your Cytoscape installation directory BR c Cytoscape_ 283 plugins 0 x A ae z acan ea Search pugs eye js m Computer KDH Win 64bit C Cytoscape_v283 plugins X Search plugins 2
147. non of interest is sufficient for example memory b Click Next 99 Integrated Biology operations 3 Select terms in MeSH Network Builder Step 2 of 4 4 Review the MeSH pathway in MeSH Network Builder Step 3 of 4 NLP Networks ElMesH Network Builder Step 1 of 4 E x Input Page Enter the MeSH term MeSH Term memory Figure 129 Input Page MeSH Network Builder Step 1 of 4 a Mark the relevant MeSH headings the contain your input term s b Select the filtering option for the relevant MeSH terms under Select Type Exact Relations includes only those interactions that contain the exact MeSH headings that were selected All Relevant Relations includes all interactions that contain either the exact MeSH heading or the child MeSH heading terms c Type in the value for Min Frequency Min Frequency is the minimum number of PubMed articles PMIDs associated with the MeSH term that an interaction should have For example if the Min Fre quency is set to 5 the pathway includes only those interactions which have at least 5 PMIDs that contain the relevant MeSH term The default setting value is 1 d Click Next iMesh Network Builder Step 2 of 4 x Select relevant MeSH terms Exact relations include only those relations which contain the exact MeSH terms All relevant relations include relations which contain even child MeSH terms 4 relation will be considered on
148. noparticles in Morris water maze experiment The crossing number of the mice with memory deficits recovered after treatment with N n NAB in a dose dependent manner Similar results were also observed in AChE and ChAT activity No morphological damage and no detectable Abeta plaques were found in mice hippocampus and cortex treated with GN Ne AEP we modified nanoparticles could be a promising drug delivery system for peptide and protein drug such as to enter the brain and play the therapeutic role Enzyme Function Process Family SmallMolecule Complex Protein lt lt Back Next gt gt Finish Cancel Figure 138 Two target documents in the View Tagged Content page Extract Rela tions via NLP Step 2 of 4 for memory as the search text The Pathway View displays the created pathway The number of relations are dis played above the pathway a Review the pathway b Edit the pathway Details for using the pathway view is described in section 11 1 3 Creating and Editing Pathways in the Mass Profiler Professional User Manual c Click Next 104 Integrated Biology operations NLP Networks Blextract Relations via NLP Step 3 of 4 x Pathway view Pathway view for entities and relations Found Displaying 307 relations muscarinic Te scary gt tagenist d _kinase family neuro P genas mela p apoptotic process Ei
149. normalized Conflicts 1 Legend a lutathione oxidized E 36 Variation cN Show Differentiation by Infec s IV Show One Variable Data Set Phosphoric acid Il 37 Variation CN 7 i Pantothenic Acid Hl 38 Variation CN Min of matches h Min of matches 1 lacinamide Il 39 Variation CN Pyridoxine vitamin B6 Hl 40 Variation cn AcenosineS monopho Show pathways that pass either filter M C Show pathways that pass both filters b ay Description Pathway List view 295M of 493M iy Figure 88 Pathway View after a Multi Omics Analysis Launch IPA Launch IPA enables pathway information exchange between Mass Profiler Profes sional and Ingenuity Pathways Analysis IPA Ingenuity Systems www ingenu ity com Genes of interest identified using Mass Profiler Professional can be assessed in IPA using Its various analysis tools An IPA account is required to use this operation Launch IPA sends gene lists and associated expression data directly to IPA IPA pro vides an interface for you to perform network analyses build pathways view rele vant canonical pathways and obtain proprietary information on protein interactions and pathways The IPA interface can send a list of genes back to Mass Profiler Pro fessional only available for some experiment types allowing furt
150. nt variables while the metabolite profile presents a host of independent small molecule products that make up the dependent variables of a study An independent variable may be referred to as a parameter and is assigned a parameter name during the various steps of the metabolomic data analysis Non carbon and non biological origin compounds such as minerals and salts Expression of your data in entity lists after grouping your samples applying filters and performing statistical correlation methods When you open an experiment the All Samples interpretation is active You can click on another interpretation to acti vate it Identification and quantification of cellular lipids from an organism in a specified bio logical situation The study of lipids is a subset of metabolomics Using the mass to charge m z resolution to improve compound identification Compounds with nearly identical and identical chromatographic behavior are decon voluted by adjusting the m z range for extracting ion chromatograms The numerical result of dividing the sum of the data values by the number of individ ual data observations The chemical reactions and physical processes whereby living organisms convert ingested compounds into other compounds structures energy and waste Small organic molecules that are intermediate compounds and products produced as part of metabolism Metabolites are important modulators substrates byprod ucts and building
151. nts 20 Features of the example array experiment 22 Creating an expression analysis using the sample array experiment 23 4 Integrated Biology operations 35 Overview of operations 36 Results Interpretation 38 Pathway Analysis 61 NLP Networks 92 5 Reference information 109 Definitions 110 References 120 What s new in Revision A e This workflow guide is a complementary guide to the Agilent Metabolomics Workflow Discovery Workflow Guide Agilent publication 5990 7067EN Revision B The Metabolomics Workflow presents steps that precede the operations used in the Integrated Biology Workflow e The Mass Profiler Professional wizard and workflow images are based on version 12 05 e Formatting of text that appears in the left hand margin helps guide you through the operations e Operations are illustrated with flow charts that show you how the wizards are navigated based on your experiment and selections Before you begin Make sure you read and understand the information in this chapter and have the necessary computer equipment software experiment design and data before you start your analysis Optional 3 Advanced Operations Acquire data is not covered in the Metabolomics or Integrated Biology Workflow Guides Introduction Required items Compliance oh Agilent Technologies Before you begin Introduction More information Introduction An Integrated Biology IB workflow typically combines
152. o Agilent publications that help you use Agi lent products and perform your metabolomics analyses Optional Acquire data Advanced Operations Acquire data is not covered in the Metabolomics or Integrated Biology Workflow Guides Definitions References r Agilent Technologies Reference information Definitions Alignment AMDIS Amino acid ANOVA Attribute Attribute value Baselining Bayesian Bayesian inference Bioinformatics Definitions This section contains a list of terms and their definitions as used in this workflow Review of the terms and definitions presented in this section helps you understand the Agilent software wizards and the metabolomics workflow Adjustment of the chromatographic retention time of eluting components to improve the correlation among data sets based on the elution of specific component s that are 1 naturally present in each sample or 2 deliberately added to the sample through spiking the sample with a known compound or set of compounds that does not interfere with the sample Acronym for automated mass spectral deconvolution and identification system developed by NIST http www amdis net Biologically significant molecules that contain a core carbon positioned between a carboxyl and amine group in addition to an organic substituent Dual carboxyl and amine functionalities facilitate the formation of peptides and proteins Abbreviation for analys
153. o the features as experiment compounds Order and Save your group the project and data files experiment s Filter align and normalize Export your the sample project data Figure 5 Overview of the wizards that help you use Mass Profiler Professional A series of guides are available from the Agilent Literature Library http www chem agilent com en US Search Library Pages detault aspx to help you become familiar with using Mass Profiler Professional and preparing for your experi ment The Agilent G38354A MassHunter Mass Profiler Professional Quick Start Guide Agilent publication G3835 90009 helps you launch MPP activate your license review the MPP user interface and create a project and an experiment that you import preloaded data into and then use to begin a sample analysis The Agilent G38354A MassHunter Mass Profiler Professional Familiarization Guide Agilent publication G3835 90010 provides a familiarization tutorial that helps you create your first project and experiment using MPP The Agilent G38354A MassHunter Mass Profiler Professional Application Guide Agilent publication G3835 90011 helps you prepare for your experiment and guide you through an untargeted differential analysis of your data The Agilent Metabolomics Workflow Discovery Workflow Guide Agilent publica tion 5990 7067EN provides you with additional detail techniques and explanations to improve your experiment design and perform advanc
154. of 4 on page 68 for the steps involved in saving a custom pathways list c Click Next ElMulti omic Analysis Step 3 of 4 x Multi Omic Analysis Results All pathways associated with the chosen organism and the selected pathway sources in Step 1 are listed along with p values the number of Matched Entities and the number of Pathway Entities of Experiment Type for each experiment no p values are computed for Metabolomics experiments To save a subset of the Pathways select the corresponding rows and click Custom Save Click Next to proceed and save all pathways Experiment 1 Differentiation by Infection and Treatment Entity List Filtered by frequency conditions 100 0 1 Interpretation Infection Treatment Selected annotations that did not result in any matches ChEBI ID Experiment 2 One Variable Data Set Entity List Filtered by frequency conditions 100 0 1 Interpretation Numeric Parameter Selected annotations that did not result in any matches ChEBI ID 278 pathways will be saved Matched Entities iffe Pathway Entities of Ex Matched Entities One Pathway Entities of Ex 10 Hs_TCR_signaling_wWP1927_45094 1 Hs_Prostanoid_metabolism_wWP1891_42176 Hs_Signaling_by_PDGF_wWP1916_45212 Hs_Signaling_by_Robo_receptor_WP1918_45203 Hs_Gene_Expression_wWP1821_42044 Hs_Sphingolipid_Metabolism_wWP1923_46972 Hs_Intrinsic_Pathway_for_Apoptosis_WP1841_44875 Hs_Fatty_acid triac glycerol
155. of Software is subject to Agilent Tech nologies standard commercial license terms and non DOD Departments and Agencies of the U S Government will receive no greater than Restricted Rights as defined in FAR 52 227 19 c 1 2 June 1987 U S Govern ment users will receive no greater than Limited Rights as defined in FAR 52 227 14 June 1987 or DFAR 252 227 7015 b 2 November 1995 as applicable in any tech nical data Safety Notices CAUTION A CAUTION notice denotes a hazard It calls attention to an operating procedure practice or the like that if not correctly performed or adhered to could result in damage to the product or loss of important data Do not proceed beyond a CAUTION notice until the indicated conditions are fully understood and met A WARNING notice denotes a hazard It calls attention to an operating procedure practice or the like that if not correctly performed or adhered to could result in personal injury or death Do not proceed beyond a WARNING notice until the indicated conditions are fully understood and met Contents 1 Before you begin 5 Introduction 6 Required items 8 Compliance 10 2 Working with Mass Profiler Professional 11 Where is MPP used in your experiment 12 What is the metabolomics workflow 13 Advanced operations covered in the MPP workflow guides 16 Using Mass Profiler Professional 17 3 Example experiments 19 Features of the example mass spectrometry experime
156. ofessional are illus trated in this workflow with experiments that contain either 1 a single independent variable or 2 two independent variables Each of the advanced operations available in the Workflow Browser use a wizard to guide you through the operation The steps and wizard pages may change each time you perform the operation depending on the number of variables in your experiment and analysis features selected The two experiments described below allow this workflow to guide you through the options available for your analysis Terms and definitions used in metabolomics and metabolomic analyses vary It is recommended that you refer to the Definitions on page 110 for a list of terms and their definitions as used in Mass Profiler Professional and in this workflow The one variable experiment presents an analysis of a metabolomic response to changes in a single independent variable also referred to as a parameter The data was acquired using four 4 parameter values for the independent variable The parameter values consist of a single control data set that represents the organism without perturbation and data sets from three variations where the organism Is sub ject to one of three conditions established by the experiment design In summary the one variable experiment contains a single parameter with four parameter values and ten replicate samples for each parameter value Based on the discussion presented in the Prepare for an
157. om data derived from chemistry biochemistry and chemical engineering Agilent Mass Profiler Pro fessional is designed to employ chemometrics processes to GC MS and LC MS data sets to obtain useful information A subset of information that is created by an algorithm from an original set of infor mation An entity list created using Mass Profiler Professional is a child An original entity list is referred to as the parent of one or more child entity lists When compounds elute from a chromatographic column at nominally the same time making the assignment of the observed ions to each compound difficult Class of compounds consisting of more than one protein physically which physically bind each other and are biologically active and stable in their combined form A compound spectrum generated to represent the molecular feature that includes more than one ion isotope or adduct not just M H and is used by Mass Profiler Professional for recursive analysis and ID Browser 111 Reference information Compound Condition Data Data processing Data reduction Deconvolution Dependent variable Determinate Element Endogenous Entity Entity List Enzyme Definitions A metabolite that may be individually referred to as a compound molecular feature element or entity during the various steps of the metabolomic data analysis Another term for one of several values within a parameter for which exist correlating sam
158. omic for the identification and quantitation of metabolites Natural Language Processing NLP algorithm that extracts information from pub lished literature A technique used to adjust the ion intensity of mass spectral data from an absolute value based on the signal measured at the detector to a relative intensity of 0 to 100 percent based on the signal of either 1 the ion of the greatest intensity or 2 a spe cific ion in the mass spectrum The default position taken by the hypothesis that no effect or correlation of the inde pendent variables exists with respect to the measurements taken from the samples Data acquired in an attempt to understand causality where no ability exists to 1 control how subjects are sampled and or 2 control the exposure each sample group receives An entity that appears in only one sample is absent from the replicate samples and does not provide any utility for statistical analysis Entities that are one hit wonders may be filtered using Filter by Flags Carbon based compounds often with biological origin A group of biochemical systems that function together as a whole thereby creating an individual living entity such as an animal plant or microorganism Individual liv ing entities may be multicellular or unicellular See also specimen 115 Reference information p value Parameter Parameter value Parent Peptide Peptide bond Permutation Polarity Polymer Pooled sa
159. omics data in Mass Profiler Professional typically results in a list of entities that are significantly different in the experimental conditions of interest Pathway analysis provides the necessary biological context for a functional analysis of these entities to better understand their role in a biological process Pathway Analysis supports analysis on well studied curated pathways while the NLP Network Discovery component drives discovery by creating networks around the entities of interest using a powerful Natural Language Processing NLP algo rithm that extracts information from published literature Note The Pathway Analysis features in Mass Profiler Professional are licensed sep arately and can only be accessed with a valid Pathway Architect module license See Getting started requirements on page 62 Pathway Analysis consists of five operations These operations can only be per formed on entity lists that have been annotated and in some cases on entity lists with Entrez Gene ID annotation Annotation of your entity list can be done for exam ple using ID Browser and SimLipid e Single Experiment Analysis on page 63 e Multi Omic Analysis on page 71 e Launch IPA on page 76 e Export to MetaCore on page 82 e Connect to Cytoscape on page 85 The following features in Pathway Analysis help you interpret your experiment e Import curated pathways directly from WikiPathways portal http www wi
160. on Single Color experiment No of sample s 6 thresholded to 1 shifted to 75 0 percentile baseline to median of all sample s 3 QC on samples 4 Filter Probesets 5 Significance Analysis 6 Fold Change 7 GO Analysis 8 Single Experiment Pa Normalized Intensity Values US22502705_251209747382_Untr US22502705_25120974738 US22502705_2512097473 US22502705_2512097473 US22502705_25120974739 US22502705_251209747404 All Samples Figure 19 Summary Report plot of the Agilent Expression Single Color Demo sam ple data 2 Enter the experiment In this step you enter your experiment grouping An independent variable is referred grouping parameters to as a parameter name The attribute values within an independent variable are referred to as parameter values Samples with the same parameter values within a associated with the parameter name are treated as replicates independent variables and their attribute values in the Analysis Biological Sig nificance Step 2 of 8 Note When entering Parameter Names and parameter Assign Values it is very wizard important that the entries use identical letters numbers punctuation and case in order for the Experiment Grouping to function properly Click Back or Experiment Setup gt Experiment Grouping to return to Experiment Grouping if an error is identi fied later in the wizard or while performing operations in the Workflow Browser respectively Note n
161. on software See Agilent MassHunter Data Acquisition Compliance Software Quick Start Guide Agilent publication G3335 90098 Revision A February 2011 for instructions on enabling and using the MassHunter Compli ance Software When Compliance ts enabled only certain users can perform certain actions For example the user that logs on to the system to submit a study needs to have certain Quantitative Analysis privileges to automatically build the quantitative analysis method 10 This chapter helps you understand where Mass Profiler Professional is used in 8 Working with Mass Profiler Professional a typical metabolomics analysis and directs you to additional documentation 8 that covers using Mass Profiler Professional e Optional ae Acquire data Advanced Operations Acquire data is not covered in the Metabolomics or Integrated Biology Workflow Guides Where is MPP used in your experiment What is the metabolomics workflow Advanced operations covered in the MPP workflow guides Using Mass Profiler Professional Bee Agilent Technologies Working with Mass Profiler Professional Where ts MPP used in your experiment Where is MPP used in your experiment Figure 2 1 Prepare for your experiment 2 Acquire your data 3 Find the spectral features 4 Import and organize your data 5 Create your initial analysis 6 Identify the features 7 Save your project 8 Perform
162. one or more pathways See Review the search results in EntityList Search Wizard Step 2 of 2 on page 41 for selecting multiple rows 2 Click Custom Save This option is only available if one or more pathways are selected a selected pathway row is highlighted lsingle Experiment Analysis Step 3 of 4 x Single Experiment Analysis Results All pathways associated with the chosen organism and the selected pathway sources in Step 1 are listed along with p values the number of Matched Entities and the number of Pathway Entities of Experiment Type for each experiment no p values are computed for Metabolomics experiments To save a subset of the Pathways select the corresponding rows and click Custom Save Click Next to proceed and save all pathways Experiment Differentiation by Infection and Treatment Entity List Filtered by frequency conditions 100 0 1 Interpretation Infection Treatment Selected annotations that did not result in any matches ChEBI ID 278 pathways will be saved Matched Entities Differentiation by Inf Pathway Entities of Experiment Typed Hs_TCR_signaling_WP1927_45094 Hs_Prostanoid_metabolism_wWP1891_42176 Hs_Signaling_by_PDGF_wWP1916_45212 Hs_Signaling_by_Robo_receptor_wWP1918_45209 Hs_Gene_Expression_wWP1821_42044 Hs_Sphingolipid_Metabolism_wWP1923_46972 Hs_Intrinsic_Pathway_for_Apoptosis_WP1841_44875 Hs_Fatty_acid _triacyiglycerol _and_
163. oose a file dialog box Figure 41 Flow chart of the Export for Recursion operation a Click Choose in the Export dialog box b Select the entity list to export Click an entity list that is at least Filtered on Flags from the entity lists in the Choose Entity List dialog box More significance in your analysis is obtained by 45 Integrated Biology operations Results Interpretation selecting an entity list that has at least been filtered by flags to remove one hit wonders A one hit wonder is a compound that appears in only one sample and is absent from the replicate samples Therefore a one hit wonder compound does not provide any utility for statistical analysis and you want to filter such compounds from your analysis c Click OK x Export Exports compound information in Compound Exchange Format CEF file Entity List Filtered on Flags f acc Choose Output File PP Data Export for Recursion cef in Browseni Ook cancer My Two Yariable Experiment Analysis S F all Entities SoS itered on Flags accCalls P M filterCondition samples 1 Filtered List on Mass gt 125 Mass lt 200 Filtered List on Annotations equals C amp My Favorites ee Cancel Figure 42 Export and Choose Entity List dialog boxes 3 Enter the export file name a Click Browse in the Export dialog box an
164. or reviewing Throughout this workflow the entities in an entity list may be individually referred to as a metabolite compound feature element or entity during the various opera tions Similar entity lists are the entity lists in your experiment navigator that contain a sig nificant number of entities in common with a specified source entity list Similarity among entity lists can also be based on filter criteria such as technology organism project and experiment The entity lists that meet your filter parameters are compared to the source entity list to determine if any of the target entity lists contain a significant number of enti ties in common with the source entity list Significance is adjusted using a p value cut off For any particular test of significance a p value may be thought of as the probability of rejecting the null hypothesis when it is in fact true For a p value of 0 05 approxi mately one out of every twenty comparisons results in a false positive analysis rejection of the null hypothesis when in fact it is true Thus if your experiment involves performing 100 comparisons with a p value of 0 05 we expect five of the comparisons to be false positives A proper statistical treatment therefore controls the false positive rate for the entire comparison set A smaller p value cut off reduces the rate of obtaining a false positive or false nega tive result and therefore reduces the number of comparisons that meet you
165. ore Color key related to mem it prin the current view Status Bar NRR A egend B NLP Network Discovery e Information related to the current view re Network Bulder olor By 1 1_Control_000 Log2 and a memory monitor Click the cca Utilities a 19 0 18 1 1_Contr 1 2_Con 1 3_Con 1 4_Con 3 1_Con 3 2_Con 3 3_Con 3 4_Con 2 1_Infe 2 2_infe garbage can to reduce memory usage Remove Entities with missing signal values Description Analysis Significance Testing and Fold Change Launched on interpretation All Samples All Samples i P Class Prediction Build and Test Model v Displaying 4414 0 selected 110m oF 13am fl Figure 6 The main functional areas of Mass Profiler Professional 18 Acquire data Example experiments The experiments described in this chapter allow the workflow to guide you through the options available for your analysis Optional Advanced Operations Acquire data is not covered in the Metabolomics or Integrated Biology Workflow Guides Features of the example mass spectrometry experiments Features of the example array experiment Creating an expression analysis using the sample array experiment Bee Agilent Technologies Example experiments Features of the example mass spectrometry experiments Definitions One variable experiment Features of the example mass spectrometry experiments The mass spectrometry analysis capabilities of Mass Profiler Pr
166. ose Choose Parameters Entity Lists gry lt gt 1 of 2 2 of 2 o Figure 30 Flow chart of the Find Similar Entity Lists wizard a Click Choose to select the Entity list that you want to use as the source for find ing similar entity lists By default the active entity list is selected b Select a filter for Target entity lists to compare to the Entity list The available filter selections for Target entity lists are Same project Same Experiment All entity list and Custom c Select an additional filter Type of targets if available as an option You can change the default value from All Types to either Same Technology or Same Organism when your selection for the Target entity lists is Same Project or All entity lists d Click Next If you select Custom for the Target entity lists see Figure 32 on page 40 proceed to step 3 Begin entity list search from Find Similar Entity Lists Step 2 of 3 on page 40 otherwise proceed to step 7 Select and save entity lists based on sig nificance in Find Similar Entity Lists Step 3 of 3 on page 43 ElrFind Similar Entity Lists Step 1 of 3 x Input Parameters Select From the Following options Options Entity list Filtered on Flags accCalls P M FilterCondit f Target entity lists Same Project RA Type of targets Same Technology ish Cancel Figure 31 Input Parameters page Find Similar Entity Lists Step 1 of 3 39 Integra
167. ot remain associated with promoters H3K4mez2 is lost and the rate of transcriptional reactivation is reduced These results suggest that Nup1 oops binding to recently expressed promoters plays a conserved role in promoting epigenetic transcriptional memory Enzyme Function Process Family lt lt Back Next gt gt Finish Cancel ElExtract Relations via NLP Step 2 of 4 x iew Tagged Content view tagged content For the selected item Title Brain Delivery of NAP with PEG PLGA Nanoparticles Modified with Phage Display Peptides Journal Pharm Res Authors LiJ Zhang C LiJ Fan L Jiang X Chen J Pang Z Zhang Q PMID 23549751 Abstract Brain Delivery of SAB with PBG PDGA Nanoparticles Modified with Phage Display Peptides PURPOSE A phage displayed peptide was used as a targeting motif to help the delivery oo ses nanoparticles across the blood brain barrier BBB which sets an obstacle for brain delivery of in vivo METHODS Intracerebroventricular injection of Abetal 40 into mice was used to construct in vivo model of Alzheimer s disease The water maze task was performed to evaluate the effects of the formulations on learning and memory deficits in mice The neuroprotective effect was tested by detecting AChE and and conducting histological assays RESULTS Intravenous administration of loaded modified nanoparticles PGN NP NAB has shown better improvement in spatial learning than NAP solution and WAB toaded na
168. pH nutrition geogra phy stress disease and controlled exposure The mathematical process employed in manipulating numerical data from scientific experiments to derive meaningful information This is part of the principal compo nent analysis t test and ANOVA processes employed by Agilent Mass Profiler Pro fessional A chemical or biological sample taken from a specimen or a whole specimen that undergoes a treatment experiment or an analysis for the purposes of further under standing Collection of samples from less than the entire population in order to estimate the population attributes A statistical test to determine whether the mean of the data differs significantly from that expected if the samples followed a normal distribution in the population The test may also be used to assess statistical significance between the means of two normally distributed data sets See also ANOVA Chromatographic components that are only uniquely denoted by their mass and retention times and which have not been assigned an exact identity such as com pound name and molecular formula Unidentified compounds are typically produced by feature finding and deconvolution algorithms See also Identified Compound An element in a data set that assumes changing values e g values that are not con stant over the entire data set The two types of variables are independent and dependent The area of the extracted compound chromatogram ECC The ECC
169. ples Condition may also be referred to as a parameter value during the various steps of the metabolomic data analysis See also attribute value Information in a form suitable for storing and processing by a computer that repre sent the qualitative or quantitative attributes of a subject Examples include GC MS and LC MS data consisting fundamentally of time ion m z and ion abundance from a chemical sample Conversion of data into meaningful information Computers are employed to enable rapid recording and handling of large amounts of data i e Agilent MassHunter Workstation and Agilent Mass Profiler Professional See reduction The technique of reconstructing individual mass and mass spectral data from co eluting compounds An element in a data set that can only be observed as a result of the influence from the variation of an independent variable For example a pharmaceutical compound structure and quantity may be controlled as two independent variables while the metabolite profile presents a host of small molecule products that make up the dependent variables of a study Having exact and definite limits on an analytical result that provide a conclusive degree of correlation of the subject to the specimen A metabolite that may be individually referred to as a compound molecular feature element or entity during the various steps of the metabolomic data analysis Pertaining to cause development or origination from within an or
170. portant part of the identification of patterns pattern recognition within data whether processed by a human or by artificial intelligence such as Agi lent MassHunter Workstation and Agilent Mass Profiler Professional In metabolom ics analysis a feature is a metabolite and may be individually referred to as a compound molecular feature element or entity during the various steps of the metabolomic data analysis The reduction of data size and complexity through the removal of redundant and non specific data by using the important variables features associated with the data Careful feature extraction yields a smaller data set that is more easily pro cessed without any compromise in the information quality This is part of the princi pal component analysis process employed by Agilent Mass Profiler Professional The identification of important or non important variables and the variable relation ships in a data set using both analytical and a priori knowledge about the data This is part of the principal component analysis process employed by Agilent Mass Pro filer Professional The process of establishing criteria by which entities are removed filtered from fur ther analysis during the metabolomics workflow A flag is a term used to denote a quality of an entity within a sample A flag indi cates if the entity was detected in each sample as follows Present means the entity was detected Absent means the entity was not detected
171. project is a container for a collection of experiments A project can have multiple experiments on different sample types and organisms You are guided through four steps to create a new project and experiment to receive your imported data Startup Select creation of a new project e Create New Project Type descriptive information about the project e Experiment Selection Dialog Select create a new experiment as part of the project e New Experiment Type and select custom information to store with the exper iment a Click Create new project b Click OK Elstartup i x Welcome to MassProfiler Pro Select what you would like to do from the options below then click on OK to continue Options Open existing project Open recent project Select recent project ntegrated Biolo I Do not show this dialog again Help Figure 12 Welcome to Mass Profiler Professional startup dialog box a Type a descriptive Name for the project Agilent Single Color Demo b Type descriptive Notes for the project c Click OK 24 Example experiments 3 Select your experiment Origin in the Experiment Selection Dialog dialog box 4 Type and select information that guides the experiment creation in the New Experiment dialog box Creating an expression analysis using the sample array experiment Elcreate New Project x New Project Details J Name Agilent Single Color Demo Notes MPP Sample File Set He
172. r criteria A larger p value cut off increases the rate of obtaining a false positive or false nega tive result and therefore increases the number of comparisons that meet your crite ria a Click Find Similar Entity Lists in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide 38 Integrated Biology operations 2 Select the input parameters in Find Similar Entity Lists Step 1 of 3 Results Interpretation The Find Similar Entities Lists wizard has three 3 steps plus additional steps involved in choosing your entity list using the EntityList Search Wizard The steps that you use depending on how you select the target entity lists in the first step of the wizard see Figure 30 When Custom is selected as the Target entity lists the EntityList Search Wizard is used to input the additional target criteria The new entity list is placed in the Analysis folder within the Experiment Naviga tor More than one entity list may be created from your analysis Find Similar Entity Lists Input Find Similar Entity Parameters Lists Results 1 of 3 3 of 3 Same Project Same Experiment All entity lists EntityList Search Wizard c e O Oo nD oO o Dv TD oL Us oc o oo N O s o g Advanced Search Cho
173. r p x Name Fitered by frequency conditions 100 0 1 Created From Significance Testing and Fold Change workflow step Filter By Frequency Entity List Filtered on Flags accCalls P M FilterCondition samples 2 Interpretation All Samples Fyneriment Differentiatinn hy Infection and Treatment xl Creation date Thu Mar 07 15 18 35 MST 2013 0 Last modified date Thu Mar 07 15 18 36 MST 2013 0 Owner use t C i SCSCSCSCSSSS Technology MassHunterQual UNIDENTIFIED _COMPOUNDS Differentiation by Number of entities ler 4 Experiments m by Infection and Treatment Entities attributes _Compou Number Alignme Annotati CAS Nu ChEBI ID Composi as 100 94 238 01 142 95 233 98 61 029 173 96 71 000 Notes H Find Next Find Previous Match Case Configure Columns Cancel Figure 53 EntityList Inspector dialog box b Add or edit descriptive information that is stored with the saved entity list in the Name Notes and Experiments fields see Figure 53 on page 53 c Click Configure Columns to add remove and reorder the columns in the tabular presentation of the entities This opens the Select Annotation Columns dialog box see Figure 54 d Select column items to add or to remove from the saved entity list e Reorder the selected columns to your pre
174. raction techniques where the first parameter value represents the current state of the art extraction process and the second parameter value rep resent the addition of a step designed to improve metabolite extraction In summary the two variable experiment contains two parameters with two parameter values for a total of four permutations and four replicate samples were obtained for each permutation Based on the discussion presented in the Prepare for an experiment chapter in the Agilent Metabolomics Workflow Discovery Workflow Guide an ideal experiment involves at least ten 10 replicates for each parameter value Thus an ideal experi ment with two parameters each with two parameter values has a data sample size of forty 40 samples The ideal sample size is calculated by multiplying 2 parameters by 2 parameter values for each parameter and then multiplying by 10 replicates for an ideal minimum sample size of forty 2 x 2 x 10 40 samples In this example the minimum sampling conditions are not met four replicates exist for each permutation for a total of sixteen 16 samples While the sampling falls short of the minimum sampling recommendation the strong correlation of cause and effect in this experi ment overcomes the sampling deficiency and provides support for further invest ment in the metabolomics question being studied In the experiment sample list shown in Figure 8 the parameter values for the inde pendent variables a
175. raged i Parameter Choose Entity List Choose Entity List J Differentiation by Infection and Treatment a J One Variable Data Set a B Analysis 2 8 Analysis S E all Entities all Entities E Filtered on Flags accCalls P M filterCondition samples 2 E F Filtered on Flags accCalls P M filterCondition samples 2 er tete conditions 100 0 1 SAEF tered by frequency conditions 100 0 1 B 2way ANOVA B Oneway ANOVA p lt 0 05 E 2Way ANOVA p Corr Infection cut off p lt 0 05 i E Fold change gt 2 0 E 2Way ANOVA p Corr Infection Treatment cut off p lt 0 05 E F Filtered on Flags accCalls P M filterCondition samples 2 2Way ANOVA p Corr Treatment cut off p lt 0 05 EB Filtered by frequency conditions 100 0 1 Union 2Way ANOVA cut off p lt 0 05 i if Oneway ANOVA p lt 0 05 a My Favorites z H E Filtered nn Flans faceCalls P M filterConditinn lsamnles 121 zi nnotations Annotations JV KEGG ID JV KEGG ID JV ChEBI ID JV ChEBI ID V HMP ID JV HMP ID J CAS Number JV CAS Number lt lt Back Next gt gt finish Cancel Figure 85 Input Parameters page Multi Omic Analysis Step 2 of 4 a Review your pathway results b Optional Select one or more pathways to save them as a custom pathways list See Review analysis results in Single Experiment Analysis Step 3
176. rameter value Thus an ideal experi ment with a single parameter and two parameter values has a data sample size of at least twenty 2 x 10 20 samples In this example the minimum sampling condi tions are not met three replicates exist for each permutation for a total of six 6 samples While the sampling falls short of the minimum sampling recommendation the strong correlation of cause and effect in this experiment overcomes the sam pling deficiency and provides support for further investment in the question being studied In the experiment sample list shown in Figure 9 the parameter values for the inde pendent variable are listed in the Treatment column Since sample names are derived from your actual data file names text files in this example it is recom mended to develop a concise meaningful file naming convention for your experi ment eI US22502705_251209747382_Untreated txt US22502705_251209747382_Untreated txt Untreated E Us22502705_251209747387_Untreated txt US22502705_251209747387_Untreated txt Untreated E US22502705 25 12097 47397 Treate dit US22502705_251209747392_Treated txt Treated E US22502705 25 12097 47393 Treate ditit US22502705_251209747393_Treated txt Treated E us22509705 25 190974739 ve Ed US22502705_ 251209747394 _ Untreated txt Untreated F z US22502705_251209747404 Treated txt US22502705_251209747404_Treated txt Treated Figure 9 One variable array experiment sample list and file
177. re listed in the Infection and Treatment columns Since sample names are derived from your actual data file names CEF files in this example it is recommended to develop a concise meaningful file naming convention for your experiment samples Infection Treatment 1 1_Control_000 Control 0 1 2 Control_000 Control 0 1 3_Control_000 Control 0 E 1 1 control_000 cef 1 4 Control_000 Control 0 1 2_Control_000 cef Ej 1 3_Control_000 cef 2 1 Infected 000 Infected 0 E 1 4_Control_000 cef 2 2 Infected 000 Infected 0 E 2 1_Infected_000 cef 2 3 Infected_000 Infected 0 E 2 2_Infected_000 cef 2 4 Infected _000 Infected 0 3 2 3_Infected_000 cef 7 E 2 4_Infected_000 cef 3 1_Control_260 Control 260 Eo 3 2_Co ntral_250 Control 25 0 E 3 3 Control_250 cef 3 3_Control_250 Control 250 l 3 4 Control 250 cef 3 4 Control 250 Control 250 E 4 1_Infected_250 cef 3 4 2_Infected_250 cef 4 1 Infected 250 Infected 250 4 3_Infected_250 cef 4 2 Infected 250 Infected 250 E 4 4_Infected_250 cef 4 3 Infected 250 Infected 250 4 4 nfected 250 Infected 250 Figure 8 Two variable experiment sample list and file list 21 Example experiments Features of the example array experiment Definitions One variable array experiment Features of the example array experiment Some pathway analysis capabilities of Mass Profiler Professional are illustrated in this workflow with an array experiments that contains a single independent variable E
178. rename and import these pathways or overwrite the existing pathways IF there are no duplicate pathways click OK to complete the import Analysis pathways Non Analysis pathways Duplicate Analysis pathways Duplicate Non Analysis pathways 46 277 0 0 Homo sapiens Help Resolye Duplicates Figure 74 Import Statistics dialog box 4 Click OK If BridgeDb databases have not yet been downloaded for the chosen organism you are prompted with the option to download the corresponding database End of the process to import a pathway f Click Next A progress status box is displayed while the pathways are searched based on the organism Elsingle Experiment Analysis Step 1 of 4 x Input Experiments The active experiment in the open project is set as Experiment 1 by default Choose a Pathway Organism and the sources from which you want to match pathways For the selected organism Any pathway organism can be selected from the drop down regardless of the organisms associated with the chosen experiment for example if you know that your research area is more extensively described in another organism Experiment Chooser Experiment Experiment Differentiation by Infection and Treatment Organism Home sapiens Choose Pathway Organism Homo sapiens Literature Derived Networks only Both Curated pathways Literature Derived Networks IV wikiPathways Analysis 0 pathways NLP 0 pathway
179. ression data to IPA to perform data analysis in IPA Genes in the entity list that are also found in Ingenuity Pathways Knowledge Base IPKB are used as Focus Genes to build networks The networks can be subjected to further manip ulation and analysis in IPA by growing a node removing nodes and interactions interrogating a node or an interaction and performing Function Canonical Path ways My Pathways Gene Summary and Overlapping Networks analyses You can create gene lists from the generated networks and send the gene lists back to Mass Profiler Professional Perform Data Analysis on Entity List sends an entity list with or without list associated values to IPA to perform data analysis in IPA Genes in the entity list that are also found in IPKB are used as Focus Genes to build networks The net works can be subjected to further manipulation and analysis in IPA by growing a node removing nodes and interactions interrogating a node or an interaction and perform Function Canonical Pathways My Pathways Gene Summary and Overlapping Networks analyses You can create gene lists from the generated networks and send the gene lists back to Mass Profiler Professional b Click OK The next step depends on your selection go to Enter the options for Create New Pathway on page 78 Enter the options for Perform Data Analysis on Experiment on page 79 or Enter the options for Perform Data Analysis on Entity List on page 81
180. retation i compound Identification Wizard k x Negative lons Cn Search Mode l Search Results Compound Identification Browser 5 Please set parameters for identification techniques Identification method C MassHunter Methods B 05 00 Default m ee eee ees Generate Formulas Charge states if not known Aggregates Which database entries should be examined when searching Charge state range T Dimers e g 2M H masses from simple ions D Trimers e g 3M H iana enes Cation or anion entries This choice is not applicable to CSV databases Negative lons Scoring Search Mode E Results _C Search Results _ lt lt Back Next gt gt Finish Cancel IV Limit to the best 10 hits Figure 49 Parameters for Search Database in the Compound Identification Wizard c Select Generate Formulas under Identify Compounds d Enter parameters for the Allowed Species Limits Charge State and Scoring tabs similar to that shown in Figure 50 on page 51 and Table 3 on page 51 Table 2 Search Database Parameters in the Compound Identification Wizard Search Database Parameters Parameter Set Search Criteria Values to match Select Mass Match tolerance Type 5 and select ppm for Mass Database Database selection Browse and select the MS database Peak Limits Spectrum peak searches Type 5 for Maximum number of peaks to search when peaks are not specified grap
181. rinsic_Pathway for_Apoptosis WP1841 44875 td Hs_13 signaling Pathway WP5 24 4230 l A A oa Hs Signaling by EGFR WP1910_45218 HsSpinalCord_Injury WP2431 59080 0022931345 He omg ecnBnton WPLBE 36986 pg gs Hs_Vitamin 812_Metabolism WP1533_49537 0 24596424 Hs_Folate Metabolism WP176 48247 006027045 8B Hs_Crosstalk_between_ER_and_GF_signaling WP1459 44997 ff A Hs Steroids WP2383 57878 df o Hs_SRF_and_miRs_in_Smooth Muscle_Differentiation_and_Proliferation WP1 A O Hs_RANKL RANK Signaling Pathway WP2018 44630 0 Oa O YO Hs_Membrane_Trafficking WP1846_44893 2 T M A Hs_Osteoblast_Signaling WP322 48244 ft Hs_Glucose_Homeostasis WP661 45308 Pf A Hs_Heme_BiosynthesisWP561 45350 0 M A 9 Hs_MAPK targets Nuclear_events mediated by MAP kinases WP1845 44 00000000 ee 4 Filter Probesets 5 Significance Analysis 6 Fold Change 7 Hs_TGF_ peta signaling Pathway WP366 47976 fee Hs_Integrated_Breast_Cancer Pathway WP1984_59204 o OTIZ o A y A He meteron induced apoptosis WP2113 58459 gop ir Hs_Musclecontraction WP1864 44927 SSS d y A a Hs Serotonin Receptor and FLK SRF GATA4 signaling W732 45038 E o e E E Hs_KinesinsWP1842 44882 0 Hs_Asparagine_N linked_g ycosyiation WP 1785 44964 Hs_sGC EMT_WP2334 57870 _ gt SSS d y A A HE Ganglio_Sphingolipid_Metabolism_WP1423_41140 o y A Ce A E E e E E T He Sinaing Pa
182. risk in your OMICs analysis realize the Ps potential of your biomarkers or establish a target s mechanism of action Password Thomson Reuters has the right solution for you PHARMA VISION 2013 j j INFORMATION UNLEASHED IJ Remeimbar M mi METADRUG PARR me MARCH 4 6 2013 METACORE METAMINER PRINCETON NJ USA lity biological PARTNERSHIPS A leading systems ntent ii pharmacola solution LOGIN FOR MORE INFORMATION gt aici that incorporates CLICK HERE 1g g you essential data and 5 extensive manually an alytic al tools to common h uman curated morm atio n on Something that do with accelerate your diseases and stern biological effects of MetaCore in one Scientific research cells led by Thomson small molecule afternoon now would have Reuters compounds taken a week before a DR CHARLES LECELLIER PRINCIPAL INVESTIGATOR IGMM g Figure 107 MetaCore login page c The export process is now complete Cytoscape is a biological network visualization and analysis tool Cytoscape is used to visualize molecular interaction networks and provide you with a means to gener ate views of gene and protein associations Cytoscape is built on an open source platform and no cost to download and use with Mass Profiler Professional The Agi lent Cytoscape plug in files enable the feature to send entity lists from your active MPP experiment to Cytoscape Note The Connect to Cytoscape features in Mass Profiler Professional are part of GeneSpring GX
183. rk An empty string indicates to search all available Knowledge Bases and to incorporate information from all sources during the analysis Select whether to Include My Pathways in Enrichment Score If you select Yes all pathways saved under My Pathways in IPA are included in the scoring process Select whether to Review Settings and ID Mapping before Running Analysis If you select Yes you can review and modify settings before running your IPA analysis If you select No IPA data analysis is automatically performed using the settings defined in this dialog box Select the Gene Identifier Column The gene identifier is used to map genes in the entity list to genes in the IPKB Click OK Your default Internet browser is automatically launched and connected to the IPA server as specified in the IPA Server Address See Figure 93 on page 9 lperform Data Analysis on Experiment l aef Perform Data Analysis on Experiment Entity List way ANOVA p Corr Infection cut off p lt 0 05 Experiment Interpretation Infection Treatment H H IPA Server Address Janalysis ingenuity com Project Name Two Variable Data Set Use both Direct and Indirect relationships res o Knowledge Base content CT Include My Pathways in Enrichment Score ves Review Settings and ID Mapping before running analysis Noo Gene Identifier Column lntrezGeneID o Help OK Cancel Figure 96 Perform Data Analysis on Experiment dialog box 80
184. rocessing procedures that produce character istic markers for a set of samples e Construct statistical models for sample classification 13 Working with Mass Profiler Professional What is the metabolomics workflow Typical metabolomics workflow Variables A typical Agilent metabolomics workflow is illustrated in Figure 3 starting with data acquisition through to analysis involving both untargeted discovery LC MS and targeted confirmation LC MS MS analyses Molecular feature extraction MFE and Find by Formula FbF are two different algorithms used by MassHunter Qualita tive Analysis for finding compounds All results files generated by Agilent analytical platforms can be imported into Mass Profiler Professional for quality control statis tical analysis visualization and interpretation Separate amp Feature Alignment amp i r age Identi Pathways Qualitative ID Browser MPP Pathway Analysis ae oe Analysis Module GC MSD Find by Import and Organize GC 000 Chromatogram Initial Analysis and GC OTOF Deconvolution Advanced Operations Identify compounds Integrated Biology and add annotations with MPP m i Feature Finding J E Ses jj LC 0 TOF a 1 Finding Discover ew Ie a Mass Profiler Professional Figure 3 An Agilent metabolomics workflow from separation to pathway analysis typically involves either or both GC MS and LC MS analyses LC 000 MFE Find by Confirm Formula or Find by lon A meta
185. rowser Identification operation has one 1 step within Mass Profiler Pro fessional and three 3 additional steps within ID Browser as shown in Figure 44 ID Browser for Identification Save and Return Choose the Entity List to be identified 1 of 1 EntityList Inspector dialog box MassHunter ID Browser Py a o Choose Entity List dialog box a lt gt lt lt lt lt a o of oe e a a Compound Identification Wizard Parameters for Parameters for Parameters for Identification Identification Identification Compound Search Database Generate Formulas Compound Search Criteria Negative lons selection Species Compound identification Database Scoring Limits methods Peak Limits Search Mode Charge State Positive lons Search Results Figure 44 Flow chart of the ID Browser Identification operation Your entities are initially unidentified as shown in Figure 45 on page 48 When you complete IDBrowser Identification your entities appear as shown in Figure 55 on page 54 47 Integrated Biology operations 2 Select the entity list to identity Results Interpretation mass Profiler Professional Differentiation by Infection and Treatment Project Search View Tools Annotations Windows Help EEEE GJA at wm ARRANT e
186. ry Workflow Guide The Import Annotations operation has one 1 step as shown in Figure 65 Import Annotations Import Annotations 1 of 1 Choose a file dialog box Figure 65 Flow chart of the Import Annotations operation a Click Browse 59 Integrated Biology operations Results Interpretation import Annotations x Import Annotations Updates technology annotation from the input file Entities are matched using mppid in input file Annotation File Ji Browse Help Cancel Figure 66 Import Annotations dialog box b Select the folder containing your CEF file in the Choose a file dialog box c Type or click the File name d Click Open xi Look in fd MPP Data 2 eaa E Recent Items Desktop Export for Identification cef My Documents Computer A File name Export for Identification cef Open e Network Files of type Compound Exchange Format ceF X Cancel Figure 67 Choose a file dialog box e Click OK A progress box is displayed while the annotations are updated Progress Updating Annotations Gancel Figure 68 Progress indication while annotations are updated 60 Integrated Biology operations Pathway Analysis Pathway Analysis Single Experiment Analysis Multi Ormic Analysis Launch IFA Export to MebaCore Connect to Cytoscape Features of Pathway Analysis Pathway Analysis Analysis of
187. s JV WikiPathways Reactome 121 pathways J MeSH term 0 pathways JV wikiPathways GenMAPP 0 pathways JV WikiPathways Other 157 pathways 7 BioCyc 0 pathways 7 BioPAX Imported 0 pathways 7 GPML Imported 0 pathways 7 Hand created 0 pathways Legacy 0 pathways Next gt gt Finish Cancel Figure 75 Input Experiments page Single Experiment Analysis Step 1 of 4 a Select an interpretation for Choose Interpretation An interpretation specifies how the samples are grouped based on your experimental conditions b Select an entity list for Choose Entity List 66 Integrated Biology operations Pathway Analysis c Mark the Annotations to use in your analysis At least one annotation must be marked Table 4 presents the annotations used by Mass Profiler Professional If an entity does not have the specified annotation it is not matched Table 4 Annotations used by Mass Profiler Professional Available Annotations by Omic Type Genomics Transcriptomics Proteomics Metabolomics RefSeq Protein ID Swiss Prot KEGG ID RefSeq Transcript ID NCBI GI number ChEBI ID Entrez Gene ID HMP ID Ensembl Gene ID CAS Number GenBank Accession UniGene ID TIGR ID Entities from the selected entity list and pathways from the selected organism are matched based on their annotation identifiers If the selected pathway organism differs from the experiment organisms matching is accomplished b
188. s accCalls P M filter Hs_Fanconi_Anemia_pathway_W 1 Launch IPA gt Filtered by frequency conditions 1 Hs_Mitotic_G2 G2 M_phases Export to MetaCore amp 5 Oneway ANOVA p lt 0 05 E Fold change gt 2 0 Filtered on Flags accCalls P M Filter Hs_Protein_folding_WP1892_42 Hs_Semaphorin_interactions_ WP Connect to Cytascape Hs_Metabolism_of_RNA_WP185 8 NLP Networks E E Filtered on Flags accCalls P M fiter Hs_Respiratory_electron_transp 13 My Favorites Hs_Metabolism_of_nucleotides_ 81 p gt Hs Signaling_by Insulin recepto 11 ees v moo Hs Telomere Maintenance wP 10 Hs_Phase_1_ _Functionalization 102 a My Lists Hs_Metabolism_of_porphyrins_ 31 Hs Eukaryotic_ Translation Elon 2 ix Eind Find Next Find Previous p MoCo sulfurated MoCo oxidized glutathione Orthophosphate Ortho Pantothenate Nicotinamide Pyridoxine gt AMP Legend q Heatstrip Heatmap Color Settings Differentiation by Infection and Treatment WB infected 0 I infected 250 E Not Infected 0 B Not Infected 250 Glutathione oxidized Phosphoric acid Pantothenic Acid Niacinamide Pyridoxine vitamin B6 AdenosineS rmonopho IV Show Differentiation by Infec Min of matches 1 End QO Find Next Find previous J Match Case ee
189. saved Similar Lists under the source Entity List in the Experiment Navigator 43 Integrated Biology operations Results Interpretation Pl significant Entitylists x Filtered by frequent Union 2Way ANOVA Filtered on Flags ar Z2Way ANOVA p Co 2Way ANOVA p Co iterCondition samples nterpretation Experiment My Two Variable Experiment Flag Yalue conditions 100 0 1 y where at least 100 0 percent of samples in any 1 out of 4 conditions has flag P Creation date Thu Nov 01 17 19 02 MDT 2012 S Last modified date Thu Nov 01 17 19 02 MOT 2012 0 Owner Joxuser Technology MPOUNDS My Two Variable Experiment_2011_Dec_19_15 56 01 Number of entities fizo Experiments j Two Variable Experiment Entities Attributes Number Pas Annotations Mass Retention T CAS Number 1 99 9361 0 131 C13 H3 N 04 1 C13 H3 N 237 0059 0 147 141 9465 1 141 9465 0 153 232 9753 2 232 9753 0 149 60 0223 0 2 60 0223 0 144 172 9538 1 172 9538 0 151 69 9923 0 1 69 9923 0 274 Find Q Find Next Find Previous I Match Case Configure Columns Cancel Figure 39 Saving custom significant entity lists End of the optional procedure to select one or more entity lists to save them as a custom entity list c Move the slider or type in the p value cut off value The default value is 0 05 Mov
190. sen entity list are selected Associated data with the matching entities is grouped by the conditions specified in the chosen Interpretation and displayed as Heatstrips in the matching pathways at the end of the Single Experiment Analysis workflow Differentiation by Infection and Treatment Choose Interpretation Differentiation by Infection and Treatment Sy Interpretations H Q All Samples E Infection Treatment Non averaged mari infection Treatment Choose Entity List i Differentiation by Infection and Treatment Analysis S E All Entities B E Filtered on Flags accCalls P M FilterCondition samples 2 Filtered by frequency conditions 100 0 1 5 2way ANOVA E 2Way ANOVA p Corr Infection cut off p lt 0 05 E 2Way ANOVA p Corr Infection Treatment cut off p lt 0 05 E 2Way ANOVA p Corr Treatment cut off p lt 0 05 E Union 2Way ANOVA cut off p lt 0 05 My Favorites Annotations lt lt Back Next gt gt Finish Cancel Figure 76 Input Parameters page Single Experiment Analysis Step 2 of 4 67 Integrated Biology operations 4 Review analysis results in Single Experiment Analysis Step 3 of 4 Save a custom pathway list a Review your pathway results Pathway Analysis b Optional Select one or more pathways to save them as a custom pathway list 1 Click
191. sion list creation Retention time window t 0 0 10 0 min P Limit number of precursor ions per compound to 1 ionts Minimum ion abundance 2000 counts Exported m z value Export monoisotopic m z Export highest abundance m z Positive ions Charge state preference Iv H Prefer highest abundance charge state s M Na rm k Specify charge state preference order Bs Inactive Active 2 1 3 Q gt 3 Negative ions Unknown LE FH J cl Br Hcoo J CH3c00 CF3c00 lt lt Back Next gt gt Finish Cancel Figure 63 Filtering Parameters for Inclusion List page Export Inclusion List Step 2 of 2 Inclusion filter application The inclusion filters are applied in following order order 1 Positive lons and Negative lons filters Peaks which contain the selected ions are passed e g if only H and Na ions are marked then peaks with ion spe cies similar to M H M 2H M Na M 2Na M H Na are selected for fur ther filtering 2 Peaks with same charge state and same ion species are grouped in one iso tope cluster e g M H M H 1 M H 2 in one cluster From this cluster only one peak is exported depending upon the selection for the Exported m z value filter If Export monoisotopic m z is selected then the peak similar to the M H or M Na M 2H etc is selected from isotope cluster Otherwise if the filter Export highest abundance m z is selected then the peak is exported which has the max
192. t gt gt Finish Cancel Figure 50 Parameters for Generate Formula in the Compound Identification Wiz ard Table 3 Generate Formula Parameters in the Compound Identification Wizard Generate Formulas Parameters Parameter Set Action Allowed Species Charge carriers to be assumed if not Select H for Positive ions known Select H for Negative ions Select even electron for MS ion electron state Elements and limits Type C 3 60 Type H 0 120 Type O 0 30 Type N 0 30 Type S0 5 Type ClO 3 Limits Limits on input masses Type 750 for Maximum neutral mass for which formulas should be calculated Limits on results Mark Minimum overall score and type 35 Charge State Isotope grouping Type 0 0025 m z and 7 0 ppm for Peak spacing tolerance Select Common organic molecules for Isotope model Scoring Contribution to overall score Type 100 for Mass score Type 60 for Isotope abundance score Type 50 for Isotope spacing score Type 100 for Retention time score Expected data variation Type 2 0 mDa and 5 6 ppm for MS mass Type 7 5 for MS isotope abundance Type 5 0 mDa and 7 5 ppm for MS MS mass Type 0 115 min for Retention time 51 Integrated Biology operations Results Interpretation e Click Finish when you have the method set up for your experiment ID Browser automatically begins identifying your entities and shows a progress bar Operation in Progress 18 Cpd 235 4 161 Starting Cancel Figure 51 Progress in
193. t molecular features consist of mass and retention time instead of molecular formula FbF calculates reasonable isotope patterns and uses these patterns with retention time tolerances to find the target features in the sample data files When the input molecular features are filtered from a find process that was previously untargeted the molecular features found using this repeated process of finding molecular fea tures is referred to as recursive finding Recursive finding consists of three steps 1 Untargeted Find Compounds by Molecular Feature in MassHunter Qualitative Analysis to find your initial entities 2 Filtering by Significance Testing and Fold Change using abundance retention time sample variability flags frequency and statistical significance in Mass Profiler Professional to find your most significant entities 3 Targeted Find Compounds by Formula in MassHunter Qualitative Analysis to improve the reliability of finding your features and subsequently improve your statistical analysis accuracy a Click Export for Recursion in the Workflow Browser This operation is illustrated with data from the Two variable experiment to pro vide an overview of the wizard options The data is initially imported and analyzed following the Agilent Metabolomics Workflow Discovery Workflow Guide The Export for Recursion operation has one 1 step as shown in Figure 41 Export for Recursion Choose Entity List dialog box Ch
194. t Layout Plugins Help EE a Sy By Bae Results Panel Sal Control Panel SEA Folic Acid Network O x All Entities Tg Network vizMapper Editor Filters WikiPathways Search Results Heme Biosynthesis a e Selenium 2 0 One Carbon Metabolism 1 0 Node Attribute Browser Edge Attribute Browser Network Attribute Browser Heat Map Viewer Welcome to Cytoscape 2 8 3 Right click drag to ZOOM Middle click drag to PAN Figure 112 Cytoscape Desktop b Click Help gt Contents or press F1 to access the Cytoscape User Manual for information on how to use Cytoscape Figure 113 ioi x gt lels A o Cytoscape User Manual a Cytoscape User Manual i Cytoscape User Manual Introduction Launching Cytoscape Table of Contents Quick Tour of Cytoscape Command Line Arguments Cytoscape User Manual i ee Cytoscape Preferences Introduction Creating Networks Supported Network File Fc Development Attribute Functions and Ec What s New in 2 7 H E H E Node and Edge Attributes License Loading Gene Expression Dica te ine Importing Networks and amp pe Navigation and Layout Launching Cytoscape visual Styles Finding and Filtering Node System requirements Editing Networks Getting Started Nested Networks Plugins and the Plugin Mar Quick Tour o
195. t explanation of the variance in the data e g identification of the components in the data that contain the meaningful informa tion providing differentiation Principal component Transformed data into axes principal components so that the patterns between the axes most closely describe the relationships between the data The first principal component accounts for as much of the variability in the data as possible and each succeeding component accounts for as much of the remaining variability as possible The principal components are viewed and interpreted in 3D graphical axes with additional dimensions represented by differ ent colors and or shapes representing the parameter names a Review the QC on samples results b Click Next Steps QC on samples 1 Summary Report 2 Experiment Grouping 4 Filter Probesets Sample quality can be assessed by examining the values in the PCA plot and other experiment specific quality plots displayed here To remove a sample from your experiment select the sample from any of the views and click Add Remove Samples IF a sample is removed the remaining samples are re summarized US22502705_25 1209747382_Untreated txt Untreated Displaying 6 out of 6 samples retained in the analysis To change use the Add Remove Samples button below 5 Significance Analysis j522502705_251209747387_Untreated txt Untreated US22502705_251209747392_Treated txt Treated 6 Fold Change
196. ted Biology operations 3 Begin entity list search from Find Similar Entity Lists Step 2 of 3 4 Enter entity list filter criteria in EntityList Search Wizard Step 1 of 2 Results Interpretation ElFind Similar Entity Lists Step 1 of 3 Input Parameters Select From the Following options Options Entity list Fitered on Flags accCalls P M FilterCondit Choose Target entity lists Type of targets All Types jack Next gt gt Finish Cancel Figure 32 Input Parameters page with Custom selected for the Target entity lists Find Similar Entity Lists Step 1 of 3 a Click Choose EntityList s to begin the EntityList Search Wizard This step is only performed if you select Custom for the Target entity lists see Figure 32 The entity list table is empty until you complete the EntityList Search Wizard in the following steps OG Similar Entity Lists Step 2 of 3 x Choose Entity Lists Choose Entity Lists First search for the entity lists of interest by clicking on the Choose Entity List s button Choose Entity List s that you wish to use to find similar Entity Lists and click Next Selected Entity Lists tes Choose EntityList s i Einish Cancel Figure 33 Input Parameters page Find Similar Entity Lists Step 1 of 3 Build the entity list filter criteria referred to as a search query to find the entity lists to compare to the sourc
197. terpretation for each experiment An inter pretation specifies how the samples are grouped based on your experimental conditions b Select an entity list for Choose Entity List for each experiment c Select Annotations for each experiment to use in your analysis At least one annotation must be specified Table 4 on page 67 presents the annotations used by Mass Profiler Professional Entities from the selected entity list and pathways from the selected organism are matched based on their annotation identifiers If the selected pathway organism differs from the experiment organisms matching is accomplished by identifying homologous genes based on Entrez Gene IDs using HomoloGene Translation for Gene Protein Identifiers http www ncbi nim nih gov homologene When the pathway and experiment organism are the same annotation identifiers are matched using BridgeDb ID Mapping Mass Profiler Professional first tries to find direct matches between the pathway entities and the entities in the selected entity list A direct match occurs when entities from both the pathways and entity list have identifiers from the same annotation When identifiers from differing annotations are matched the BridgeDb algorithm looks for a match in the order in which the annotations are displayed on this wizard page The first matching annotation and corresponding identifier are displayed in the Heatmap of the Pathway View 73 Integrated Biology operations 4 R
198. that are regulating the transport of other compounds Metabolism Regulators Find compounds that are regulating the metabo lism of biomolecules 94 Integrated Biology operations NLP Networks Small Molecules Find all small molecules drugs regulators and targets of two or more entities from the original list of selected entities Biological Processes Finds all biological process entities connected to two or more entities from the original list of selected entities Shortest Connect Finds the smallest set of relations that connects all enti ties in a given list into a single network In addition to the Algorithm selection above Simple also sets the following parameters Algorithms type local global Connectivity Connectivity relevance 50 and Connectivity lt 2 Entity filter All entities are selected Quality filter gt 9 Relation filter All relation types are selected 2 If you selected Advanced for the Analysis Type your options are Direct Interactions Find relations that connect the selected entities Expand Interactions Expand the existing network to include the first degree neighbors of the selected entities Shortest Connect Find the smallest set of relations that connects the set of selected entities into a single network Some intermediate entities may be introduced in this process d Click Next Go to step 5 Review the analysis results in NLP Network Discovery Step 4 of 5 on page 98 if your
199. the Metabolomics Discovery Workflow in the Integrated Biology Workflow L TT S N N Quick Start ually Statistical Find Similar single NLP Network i Control on ar Experiment Guide Analysis Entity Lists Discovery Samples Analysis Experiment Filter by Filter on Export for Multi Omic peo i Network Grouping Frequency Volcano Plot Recursion Analysis Builder Create er on ID Browser a ese IntoRBretation Sample Fold Change IdeANFCALICH Launch IPA Relations via P Variability NLP Create Export for Export to Filter by Flags Clustering identification MataCore Pathway Organism Filter by Find Similar Export Connect to Abundance Entities Inclusion List Cytoscape Filter by Filter on Import Annotations Parameters Annotations Principal Component Analysis Find Minimal Acquire data is not part of the material Entities covered in the workflow guides Figure4 Summary of the Metabolomics Discovery Workflow and MPP advanced operations covered in the Metabolomics and Integrated Biology workflows Working with Mass Profiler Professional Using Mass Profiler Professional Using Mass Profiler Mass Profiler Professional helps you analyze your data through the use of sequen Professional tial dialog boxes and wizards as shown in Figure 5 Import and Create an initial Advanced organize data analysis operations Set up a Do Significance Use the project and an Testing and Workflow experiment Fold Change Browser Import data Identify files int
200. the results generated by multi omics analyses into a new experiment The aim is to find important correla tions and validation through statistical analysis ultimately leading to further insight into a biological system This Workflow Guide is complementary to the Agilent Metabolomics Workflow Dis covery Workflow Guide Agilent publication 5990 7067EN and covers advanced operations available in Mass Profiler Professional MPP that help you perform inte grated pathway level analysis of the primary data from any Agilent omics platform while also enabling incorporation of prior knowledge existing datasets pathway maps and interaction maps for greater analytical power in your multi omics exper iments Metabolomic studies involve the process of identification and quantification of the endogenous components that form a chemical fingerprint of an organism or situa tion under study and may involve the process of identifying correlations related to changes in the fingerprint as affected by external parameters metabonomics Mass Profiler Professional may be used in the study of metabolomics and metabonomics for small molecule studies proteomics for protein biomarker studies and general differential analysis Regardless of the specific study and molecular class the pro cess is referred to as metabolomics throughout this workflow To increase your confidence in obtaining reliable and statistically significant results review the ch
201. the results of the analysis can be restricted to a manageable size by specifying the number of new entities to Fetch based on the local connectivity of the entities or on the local to global connectivity ratio of the entities Relation Filter Entity Filter Relation score gt E Entity global connectivity lt 500 Select relation type Select entity type J Member IV Enzyme JV Transport JV Function JV Expression IV Process JV Regulation V Family IV Binding JV Small Molecule JV Promoter Binding IV Complex V Metabolism IV Protein JV Protein Modification epee DE Finish PE Figure 125 Shortest Connect Analysis Filters page NLP Network Discovery Step 3 of 5 Analysis Result displays the created pathway The initial number of entities the number of new relations and the number of new entities are displayed a Review the pathway b Edit the pathway Details for using the pathway view is described in section 11 1 3 Creating and Editing Pathways in the Mass Profiler Professional User Manual c Click Next Elnr Network Discovery Step 4 of 5 x Analysis Result Pathway view for entities and relations that resulted From the analysis Displaying pathway with 130 initial entities 146 new relations and 19 new entities EA E E EG SLG36AL gt gp TEO CFBP2 gt gp ll24 gt pe HPGD a 55 R X K f e a ee ea mul 5 vy 2 B m
202. tment Selected annotations that did not result in any matches ChEBI ID 278 pathways will be saved Hs_TCR_signaling_WP1927_45094 1 Lola Hs_Prostanoid_metabolism_wWP1891_42176 0 17 Hs_Signaling_by_PDGF_wWP1916_45212 0 9 Hs_Signaling_by_Robo_receptor_wWP1918_452093 1 5 Hs_Gene_Expression_wWP1821_42044 0 0 Hs_Sphingolipid_Metabolism_wWP1923_46972 2 29 Hs_Intrinsic_Pathway_ for Apoptosis _WwP1841_44875 o 2 Hs_Fatty_acid _triacyiglycerol and_ketone_body_ metabolism _WP1817_4 2 81 Hs_Nucleosome_assembly_wWP1874_42092 0 0 Hs_Signaling_by_EGFR_WP1910_45218 1 8 Hs_Interferon_Signaling_wWP1837_46942 0 0 Hs_Nucleotide_Excision_Repair_WP1980_42219 o 0 Hs_Interleukin 1_signaling_WP1839_44873 2 Hs_Metabolism_of_amino_acids_and_derivatives_WP1847_52373 5 135 7 Gustom Saye Figure 79 Single Experiment Analysis Results page Single Experiment Analysis Step 3 of 4 5 Enter save pathway list a Review your pathway list results parameters in Single b Add or edit descriptive information that is stored with the saved pathway list in Experiment Analysis Step the Name and Notes fields 4 of 4 ee c Click Next The new SEA pathway list is placed in the Analysis folder within the Experiment Navigator 69 Integrated Biology operations Pathway Analysis Elsingle Experiment Analysis Step 4 of 4 Save Pathway List The pathways listed in the table below will b
203. ults b Click and move the Fold change p value cut off slider or type in the p value cut off value and press the Enter key The default value is 2 0 The results in the dis play window are automatically updated c Click Next 31 Example experiments Creating an expression analysis using the sample array experiment workflow Type Analysis Biological Significance Step 6 of 8 Steps Fold Change Entities that satisfy a Fold change cut off of 2 0 in at least one condition pair are displayed by default To change the Fold change cut off use the slider at the bottom of the view or enter the required cut off value in the Field and confirm the entry by pressing Enter 1 Summary Report 2 Experiment Grouping Note Condition 1 vs Condition 2 is calculated as Condition 1 Condition 2 A negative value indicates down regulation in Condition 1 IF shown the Absolute Fold Change column ignores the directionality of regulation and the Regulation column is labeled with respect to expression in Condition 1 3 QC on samples 4 Filter Probesets Displaying 630 out of 4264 entities with Fold change cut off of 2 0 with Untreated as the control condition 5 Significance Analysis Pr obe jame cmoa epangE A23_P501193 2 63 7 GO Analysis A 23_P111402 A23_P115943 2 2 AL23 PS 7007 A 23_P252413 5 56 A 23_P1331 22 1 A 23_P60933 EE A23 P4271139 A23 P145644
204. unter Mass Profiler Professional Familiar ization Guide G3835 90010 Revision A November 2012 e Manual Agilent G3835AA MassHunter Mass Profiler Professional Applica tion Guide G3835 90011 Revision A November 2012 Before you begin Introduction Presentation Advances in Instrumentation and Software for Metabolomics Research Advances in Instrumentation and Software for Metabolomics pdf September 18 2012 Brochure Agilent Solutions for Metabolomics 5990 6048EN April 30 2012 Brochure Agilent Mass Profiler Professional Software 5990 4164EN April 27 2012 Application Mass Profiler Professional and Personal Compound Database and Library Software Facilitate Compound Identification for Profiling of the Yeast Metabolome 5990 9858EN April 25 2012 Brochure Pathways to Insight Integrated Biology at Agilent 5991 0222EN March 30 2012 A complete list of references may be found in References on page 120 This manual gives links to most references If you have an electronic copy of this manual you can easily download the documents from the Agilent literature library Look for and click the blue hypertext for example you can click the Agilent literature library link in the previous sentence If you have a printed copy go to the Agilent literature library at www agilent com chem library and type the publication num ber in the Keywords or Part Number box Then click Search Note If you type the public
205. using data from targeted QQQ experiments and 3 using ID Browser to annotate data from high resolution LCMS experiments By comparing the pathway results from filtered entity lists i e fold change K means clustering and unfiltered entity lists not filtered for fold change or significance lists can also improve your results 62 Integrated Biology operations Single Experiment Analysis 1 Launch Single Experiment Analysis in the Workflow Browser 2 Select the experiment parameters in Single Experiment Analysis Step 1 of 4 How to change the organism for an existing experiment Pathway Analysis Single Experiment Analysis SEA identifies pathways that contain entities in com mon to the entities in the selected entity list for one experiment The matched enti ties are highlighted on the pathway Commonality between a pathway and an entity is determined via the presence of a shared identifier The operation works with genomics transcriptomics proteomics and metabolomics experiments Entity lists may contain genes proteins or metabolites Single Experiment Analysis helps you determine in which biological pathways there exists a significant enrichment of compounds of interest based on the input entity list You can choose an organism for pathway analysis that differs from the organism associated with your experiment Curated pathways such as WikiPathways BioCyc pathways and BioPAX pathways as well as NLP and MeSH cre
206. way E2203 A082 anaa a a a H o_a Hs EGF EGFR Signaling Pathway WP 437 44600 LELI WN ACASA m lt lt Back Next gt gt _Einish cancel Figure 27 Single Experiment Pathway Analysis for the Agilent Expression Single Color Demo sample data You are now in the advanced workflow mode and have access to all features avail able in Mass Profiler Professional through the Workflow Browser The imported and analyzed Agilent Expression Single Color Demo sample data is displayed in MPP similar to Figure 28 on page 34 33 Example experiments Creating an expression analysis using the sample array experiment ElMass Profiler Professional Agilent Single Color Demo Project Search View Tools Annotations Windows Help Ala fa Me AAN Quick Start Guide Experiment Grouping Create Interpretation Create New Gene level Exp Oi Treatment Analysis All Entities a Filtered on Flags Detect EB T test p lt 0 05 H 8 Fold change gt BJ UP FC unt be Down Fc Normalized Intensity Values BH extra i SEA Fold char Legend Profile Plat Color By Treated 1 5 0 Description Launched on interpretation Treatment 4 5 Treated Untreated All Samples a ce Figure 28 The Agilent Expression Single Color Demo sample data
207. will be saved as entity lists To modify the corrected p value cut off use the slider below 1 Summary Report 2 Experiment Grouping Please note that if any entity has less than two values in either of the conditions then that entity will get filtered out 3 QC on samples 4 Filter Probesets Displaying 11 GO terms satisfying corrected p value cut off 0 1 To change use the slider below IGO 0046870 cadmium ion binding BFF 8 072 G0 0002526 acute inflammatory resp OR BOF 8 285 IGO 0005576 extracellular region a 9G 11 071 GO 0006811 fiontranseon PRB 35S BN 5 Significance Analysis 6 Fold Change 8 Single Experiment Pa GO 0005507 copper ion binding en oosit 8B 0 411 GO 0044421 extracellular region pat 9 0 078 o 2A 12 308 ro SRG GO 0051239 regulation of multicellular BE oP 06 IGO 0004035 alkaline phosphatase acti BBD IGO 0006953 acute phase response BES HB 202 IGO 0044699 i single organismprocess_ BE OS 5B 37 685 IGO 0034220 fiontransmembrane trans 00g gs G4 804 E GO Tree corrected p value cut off c fur lt lt Back Next gt gt Einish Cancel Figure 26 GO Analysis for the Agilent Expression Single Color Demo sample data 32 Example experiments Creating an expression analysis using the sample array experiment a Review the Single Experiment Pathway Analysis results b
208. y excluding holidays support ingenuity com Si pi got Passwor i iri Sign Up Forgot Password For Product and Sales related inquiries contact 650 381 5056 sales ingenuity com Contact Us Press Site Map Privacy Policy 2013 Ingenuity Systems Inc All rights reserved Figure 93 IPA sign in page Start Here Learn IPA through the easy tutorials below Each will be checked as you complete it Learning IPA Shortcuts View Pathways Explore the Pathway Library Search for Genes See an Analysis Example Upload amp Analyze Example Data Upload amp Analyze Your Own Expression Data More Signaling Pathways gt More Metabolic Pathways Visualize Connections Among Genes For further exploration How do I open the pathways ee Short Videos about Pathways Learn about Specialized Features What do the symbols and shapes What do the connections mean mean How can export or save a copy How do visually relate this to of these images my own data Do Not Show at Startup Tutorials and Help Training Schedule Customer Support How to Cite IPA Figure 94 IPA Quick Start page Review the Entity List The active entity list is selected To use a different entity list cancel the operation select a different entity list in the Experiment Navigator and relaunch the operation Click Choose to select the Experiment Interpretation By default the active inter pretation is already selected Figure 95
209. y identifying homologous genes based on Entrez Gene IDs using HomoloGene Translation for Gene Protein Identifiers http www ncbi nim nih gov homologene When the pathway and experiment organism are the same annotation identifiers are matched using BridgeDb ID Mapping Mass Profiler Professional first tries to find direct matches between the pathway entities and the entities in the selected entity list A direct match occurs when entities from both the pathways and entity list have identifiers from the same annotation When identifiers from differing annotations are matched the BridgeDb algorithm looks for a match in the order in which the annotations are displayed on this wizard page The first matching annotation and corresponding identifier are displayed in the Heatmap of the Pathway View d Click Next A progress status box is displayed while the pathways are searched based on the entities in the entity list BAlsingle Experiment Analysis Step 2 of 4 x Input Parameters Select the desired Interpretation and Entity List in the fields provided Then select the annotation types from the lists that you want GeneSpring to use when matching the entities From the chosen entity list to the pathways From the organism you specified in the previous step The order in which they are displayed reflects the order in which GeneSpring is matching entities between the pathways and entity lists By default all available annotation types in the cho
210. yc includes the pathways that you downloaded from the Agi lent Server using Tools gt Import Pathways from BioCyc BioPAX Imported e GPML Imported e Hand created e Legacy The following pathway sources are available for Literature Derived Networks only e NLP e MeSH term e The pathway sources include interaction networks you imported or created using the NLP Network Discovery MeSH Network Builder or Extract Rela tions via NLP operations in the Workflow Browser If you select Both then all of the Curated pathways and Literature Derived Net works pathway sources are available f Mark the Curated pathways and or Literature Derived Networks to include in your analysis The number of pathways previously imported into Mass Profiler Professional for each of the sources for the selected pathway organism Is dis played in parentheses next to the source name The number of pathways auto matically updates when you choose a different pathway organism for your analysis If the number of pathways previously imported into Mass Profiler Professional is reported as zero 0 for your organism among the sources click Cancel and 72 Integrated Biology operations 3 Select the interpretation and entity list in Multi Omic Analysis Step 2 of 4 Pathway Analysis import pathways for your organism To import pathways for an organism from WikiPathways follow steps in How to import pathways from WikiPathways on page 65 and
211. your pathway analysis When an entity list is selected the row ts highlighted 95 Integrated Biology operations 4 Select and enter filter parameters in NLP Network Discovery Step 3 of 5 NLP Networks Select a continuous range of entity lists click on the first file and press Shift and click on the last entity list that includes the range of entity lists you want to select Select discontinuous or individual entity list press Ctrl and click on additional entity lists c Click Next api Network Discovery Step 2 of 5 E x Matching Statistics The table shows statistics of matching the entities in the chosen list with the pathway interaction database Entities which match a corresponding entity in the database are marked as Matched while those which do not match are marked as Not Matched IF multiple entities in the list match to the same entity in the database they are marked as Redundant The matched entities are selected in the table and are used by default in the pathway analysis algorithm To choose a subset of the matched entities select the appropriate rows in the table Displaying 130 matched and 10 unmatched entities of the entity list 10 entities matched more than once ProbeName Name Type Global Connectivity _ amp Match Result A_23_P17064 i 613 Matched Jal A_23_P153920 AAP 2 Protein 287 Matched A_23_P89589 i 182 Matched 343 Matched 1074 Matched A_23_P3

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