Package `GSCA`
Contents
1. Quantile or Exprs If cutoff type is Norm then the fourth column should be specified as p value between O and 1 where the geneset expression cutoff will correspond to the specified p value one sided based on a fitted normal distribution If cutoff type is Quantile then the fourth column should be specified as a desired quantile between 0 and 1 where the geneset expression cutoff will correspond to the specified quantile Finally if cutoff type is Exprs the geneset expression cutoff will be equal to the value given in the fourth column Fourth column numeric value of cutoff value based on different cutoff types specified in the third column chipdata A character value of hgul33a hgul133A2 hgu133Plus2 or moe4302 GSCAeda 11 This argument specifies which compendium to use Requires the correspond ing data package scaledata logical value indicating whether expression data for each gene should be scaled across samples to have mean 0 and variance 1 SearchOutput Output of the tabSearch function More specifically a data frame where the Ist column is the ExperimentIDs GSE ids the 2nd column is the SampleTypes and the 3rd column is the sample count for each SampleType Pval co A numeric value specifying the adjusted p value cutoff Only the biological contexts with significant enrichment above the adjusted p value cutoff will be reported in the final ranked table output Ordering A character value of2. ters like cutoff types using pull down menus Users can also check instantly how many genes they inputted are recorded in a given compendium and decide what geneset to be used in further analysis process GSCAui also provides users more flexible and direct means to specify geneset activity pat terns For different number of geneset GSCAui will automatically generate control panels which are most suitable for users to interactively choose the geneset activity patterns Users can not only specify geneset activity patterns using traditional GSCA options but they can also choose gene set activity pattern on histograms scatterplots and heatmaps by point and click which makes the process easier and more explicit In addition GSCAui offers more powerful analysis and plotting options Both p value and foldchange cutoffs can be given interactively to select the enriched bio logical contexts Besides displaying top ranked enriched biological contexts users can also select specific biological contexts to be displayed on the plot Finally users can specify plotting details like x axis range and titles of the plots if they want to keep the plots for future use Thanks to the shiny server users can type in the URL http spark rstudio com jzc 19900805 GSCA to directly launch the UI in their web browser This does not require any dependent R packages or even R itself installed on users computer All required is a web browser and the URL Please check the user
3. Author s Zhicheng Ji Hongkai Ji 14 GSCAui References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 Examples Constructing genedata and pattern Example of mouse gene G1i1 Gli2 and Gli3 all members of GLI Kruppel family Their corresponding Entrez GeneID gligenedata lt data frame gsname c G1li1 G1i2 G1i3 gene c 14632 14633 14634 weight 1 stringsAsFactors F glipattern lt data frame gsname c G1i1 G1i2 G1i3 acttype High cotype Norm cutoff 0 1 stringsAsFactor Case of one geneset a set of histograms Note that for N too large sometimes there is figure margins too large error Decrease N or try to enlarge the plotting area in R console oneout lt GSCA gligenedata 1 1 glipattern 1 moe4302 GSCAplot oneout N 2 Case of two genesets a scatterplot twoout lt GSCA gligenedata 3 glipattern 3 moe4302 GSCAplot twoout Case of three genesets two heatmaps press Enter to switch to the second heatmap May take some time be patient threeout lt GSCA gligenedata glipattern moe4302 GSCAplot threeout Same plots in designated file path FILE which is a pdf file If you want to further customize output plots for example changing range of x axis changing titles or altering display of enriched biological contexts please check out the inte
4. Statgenenum lt length STAT1_TG Statgenedata lt data frame gsname c GS1 rep GS2 Statgenenum gene c 6772 STAT1_TG weight 1 stringsAsFact Statpattern lt data frame gsname c GS1 GS2 acttype High cotype Norm cutoff 0 1 stringsAsFactors FALSE Find all contexts in human compendium from GSE7123 GSE71230ut lt tabSearch GSE7123 hgul33a Run GSCAeda GSCAeda Statgenedata Statpattern hgul33a GSE71230ut Pval co 0 05 Ordering Average Title NULL outputdir NUL To save the results instead of displaying in R console specifiy an outputdir argument GSCAeda Statgenedata Statpattern hgu133a GSE71230ut Pval co 0 05 Ordering Average Title NULL outputdir tem GSCAplot Visualize GSCA output Description GSCAplot visualizes the output from GSCA For one geneset GSCAplot makes histograms of geneset activities for all samples and samples in each of most significantly enriched biological contexts For two genesets GSCAplot makes a scatter plot of sample activities of first geneset versus sample activities of second geneset plotting and the most significantly enriched biological contexts are highlighted in the plot For more than two genesets GSCAplot produces two heatmap according to geneset activities The first heatmap shows the geneset activities of all samples and indicates which samples belong to enriched biological contexts The second heatmap shows the geneset activ
5. interest The p values are also adjusted by the Bonferroni correction If directory is not null then GSCA will peform detail analyses for all contexts in each of the ex perimental IDs in the final GSCA results table For each of the experiment IDs tabSearch will be run to locate all contexts in the compendium for that experiment ID and then GSCAeda will be run using the same genedata and pattern as input specific to the contexts recovered by tabSearch See GSCAeda for more details Note this automated process could be time consuming and produce a lot of files and directories Value Returns a list with Ranking Score Pattern Cutoff SelectedSample Totalgene Missinggene Species Data frame of ranked table of biological contexts significantly enriched with the specified geneset activity pattern It includes information of Ranking number of samples exhibiting the given activity pattern total number of samples fold change values adjusted p values name of biological context and corresponding experiment ID Numeric matrix of geneset expression values for each sample in the compendium Each row stands for a certain geneset and each column stands for a certain sam ple Data frame of geneset activity pattern The same as the input value Numeric vector of cutoff values calculated for each geneset based on the input pattern Numeric vector of all samples that exhibits the given geneset activity pattern Numeric vec
6. In the first heatmap geneset activities of all samples in the compendium will be shown A color legend will be drawn on the left upper corner of the heatmap so that users will know the corresponding activity value each color represents Above the heatmap there is a color bar of light and dark blue indicating which samples exhibit the specific geneset activity pattern In the second heatmap geneset activity of all samples which exhibit the specific geneset activity pattern will be shown A color bar above the heatmap uses different colors to indicate top N most significantly enriched biological contexts A color legend will also appear in the left upper corner of the heatmap If plotfile is not null then instead of showing the plots directly in the R console GSCAplot will save the plots to the designated filepath as a pdf file Note that because there are a lot of samples in both human and mouse compendiums drawing the first heatmap and sometimes the second heatmap could take a lot of time especially a large number of genesets are given GSCAplot only supports a predefined geneset activity pattern and basic plotting options Users are encouraged to use the interactive UI if they want to interactively determine the geneset activity pattern gain more powerful plotting options and further customize their plots Value A plot consisting of several histograms a scatterplot or two heatmaps will be returned depending on numbers of genesets users give
7. either one geneset name or Average If Ordering is one geneset name the plot of geneset activity values and heatmap of the t statistics pvalues will be ordered from the highest to lowest according the Or dering geneset activity value If Ordering is Average the plots and heatmap will be organized by the average rank across all geneset activity values Title Title of the plot will appear on the top of the plot outputdir Either null or a character value giving the directory in which GSCAeda will save the output files Details GSCAeda is designed to be used in combination with tabSearch after an initial GSCA analysis GSCAeda is used to further study each predicted biological context in more detail by comparing the functional activity across related contexts through the geneset activities To do so GSCAeda re quires users to specific genedata pattern species pval cutoff and the search results from tabSearch containing the list of biological contexts of interest Then GSCAeda will calculate the mean and standard deviation of each geneset activity value for each inputted context and will perform t tests comparing the mean geneset activity values for all pair wise combinations of inputted contexts and test for enrichment of the geneset activity pattern of interest The results will be shown in several plots and tables number of files varying with number of given genesets along with the raw geneset activity values for further fo
8. if desired Usage GSCAeda genedata pattern chipdata SearchOutput scaledata F Pval co 0 05 Ordering Average Title NU Arguments genedata A data frame with three columns specifying the input genesets Each row spec ifies an activated or repressed gene in a geneset First column character value of geneset name specified by the user could be any name easy to remember e g GS1 GS2 Second column numeric value of Entrez GeneID of the gene Third column numeric value of single gene weight when calculating the activ ity level of the whole geneset Positive values for activated gene and negative values for repressed gene Here activated gene means that increases in expres sion of the gene also increases the overall activity of the whole geneset while increases in expression of the repressed genes will decrease the overall activity of the whole geneset pattern A data frame with four columns indicating the activity patterns corresponding to the given genedata Each row specifies activity pattern for one geneset First col umn character value of the same geneset name used in genedata each geneset name in genedata should appear exactly once in this column Second column character value of whether high or low activity of the whole geneset is inter ested High stands for high activity and Low stands for low activity Third column character value of which cutoff type is going to be used 3 cutoff types can be specified Norm
9. 18 Index 20 GSCA package GSCA Gene Set Context Analysis Description GSCA analyzes biological contexts enriched within given patterns of geneset expression activity GSCA takes as input several lists of activated and repressed genes Though the input genesets could contain any gene which interest users they are usually dervied from ChIP chip or ChIP seq ChIPx and gene expression data in one or more biological systems for example TF target genes genes that are both TF bound in the ChIPx data and differentially expressed in the gene expression data Then GSCA uses the given genesets to scan through a compendium of gene expression profiles constructed from publicly available gene expression data to search for patterns of geneset expression activity specified by the users The final output of GSCA is a ranked table of biological contexts that are significantly enriched with the specified pattern of geneset expression activity After the initial GSCA analysis users can further study the predicted biological contexts and related contexts in more detail using the tabSearch function to search for contexts of interest in the human or mouse compendium and the GSCAeda function to visualize and test for differences in geneset expression activities of the recovered contexts Further functions to help annotate peaks and construct TF target genes are also provided if users are interested in exploring enriched biological contexts in given TF expression and target
10. Package GSCA December 15 2015 Type Package Title GSCA Gene Set Context Analysis Version 1 8 0 Date 2015 1 6 Author Zhicheng Ji Hongkai Ji Maintainer Zhicheng Ji lt zji4 jhu edu gt Description GSCA takes as input several lists of activated and repressed genes GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression GSCA provides both traditional R functions and interactive user friendly user interface License GPL gt 2 LazyLoad yes Imports graphics Depends shiny sp gplots ggplot2 reshape2 RColorBrewer rhdf5 R gt 2 10 0 Suggests Affyhgul33aExpr Affymoe4302Expr Affyhgul33A2Expr Affyhgu133Plus2Expr biocViews GeneExpression Visualization GUI NeedsCompilation no R topics documented GSCAspackace ala yin BOS oo eS A yids amp Ane ay 2 ek annotateP aks 2 26 e424 8 ea a be ce RA OMS A a os ConstructlG xs Be ee ed ee ee dk OR A tt g nelDdata viera Se Sea he Se EGER ERE RES ee ee eS ENS gee eee O ga ep eas ee Gy ee De eee os GSCAeda dee a SO EKA DS e ERS A ee wd S GSCADPIOL eo sc id Slaw ae Se PRE RE SR ee 8 2 GSCA package GSCAUL 64 4 BRD Rew a ea DERE Ee Rhee Bebe ee Cea as 14 OCIFESC TG uo a eB dle a Bae RAC EGR eK Ce ee 16 STATI TG gos of Gos Se Go deny Pina ta es Got Pty amp Gh Sos Pe he a 17 tabSeatch 22 5522 08 64h eh dae bebe eed eee he Ew ES
11. also requires activity patterns of the genesets Users can choose either high or low level of activity for each geneset Cutoffs are given by the users to determine what activity level should be considered high or low There are three types of cutoffs available normal quantile and expression value For normal cutoff type a specified p value one sided based on a fitted normal distribution will be used as cutoff and all samples having p value larger smaller than this p value will be considered having high low expression activity in a certain geneset Likewise for quantile cutoff type a quantile will be used as cutoff As for cutoff type of expression level a numeric value will be directly used as cutoff GSCA then searches through the compendium for all samples that exhibit the specified activity pattern of interest For example if activity patterns of all genesets are set to be high then GSCA will find all samples in the compendium that have greater geneset expression levels than the respective cutoffs Since each of the samples correspond to different biological contexts the Fisher s exact test will then be used to test the association between each biological context and the geneset activity pattern of interest based on the number of samples in each biological context that exhibits the specified geneset activity pattern of interest GSCA The final output is a ranked table of biological contexts enriched with the geneset activity pattern of
12. eneID of the gene Third column numeric value of single gene weight when calculating the activ ity level of the whole geneset Positive values for activated gene and negative values for repressed gene Here activated gene means that increases in expres sion of the gene also increases the overall activity of the whole geneset while increases in expression of the repressed genes will decrease the overall activity of the whole geneset A data frame with four columns indicating the activity patterns corresponding to the given genedata Each row specifies activity pattern for one geneset First col umn character value of the same geneset name used in genedata each geneset name in genedata should appear exactly once in this column Second column GSCA 7 character value of whether high or low activity of the whole geneset is inter ested High stands for high activity and Low stands for low activity Third column character value of which cutoff type is going to be used 3 cutoff types can be specified Norm Quantile or Exprs If cutoff type is Norm then the fourth column should be specified as p value between O and 1 where the geneset expression cutoff will correspond to the specified p value one sided based on a fitted normal distribution If cutoff type is Quantile then the fourth column should be specified as a desired quantile between 0 and 1 where the geneset expression cutoff will correspond to the specified quantile Fi
13. gene activity Besides traditional R functions GSCA also provides a user friendly interactive user interface based on R shiny Users can run GSCAui function to run the UI in the web browser on their own computer need to install shiny and GSCAdata package or go to http spark rstudio com jzc 19900805 GSCA to run the UI on shiny server only a web browser is required do not need to install GSCA GSCAdata or R Details Package GSCA Type Package Version 0 99 1 Date 2014 2 9 License GPL 2 Author s Author Zhicheng Ji Hongkai Ji Maintainer Zhicheng Ji lt zji4 jhu edu gt annotatePeaks References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 annotatePeaks Annotate ChIPx peaks with genes by Entrez GenelDs Description This function finds all genes that overlap with each peak detected from TF ChIP chip or ChIP seq data Assigned genes are assumed to be genes bound by the TF Usage annotatePeaks inputfile genome up NULL down NULL Arguments inputfile genome up down Details A data frame where each row corresponds to a peak The first column is the chromosome on which the peak is found e g chr1 and the second and third columns are the peak starting and ending sites Should be one of hg19 hg18 mm9 or mm8 genome More genomes may be supported in future versions of GSCA Region upstrea
14. hat match the keywords where AND requires all recovered contexts to satisfiy all keywords and OR requires all recovered contexts to match at least one keyword Usage tabSearch keyword chipdata option OR Arguments keyword A character vector of biological context words or experiment IDs e g liver or GSE7123 chipdata A character value of hgul33a hgul33A2 hgul33Plus2 or moe4302 This argument specifies which compendium to use Requires the correspond ing data package option Either AND or OR to specify whether the recovered contexts need to be found by ALL keywords AND or found by at least one keyword OR Details If the users want to search for a specific list of contexts simply input the contexts as a character vector where each element is a different context Alternatively the contexts can also be a series of keywords in short hand The AND option is primarly used when users want to search for contexts from a specific experiment In most cases OR should be used After tabSearch finishes running it will return a list of contexts that match the inputted keywords and parameters Users can then further study these contexts for activity of given gensets using the function GSCAeda Value A data frame consisting of three columns The Ist column is the experiment ID the 2nd column is the biological context label and the 3rd column is the number of samples for each b
15. iological context Author s Zhicheng Ji Hongkai Ji tabSearch 19 References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 Examples library GSCA Search for all contexts in GSE7123 in hgul33a tabSearch GSE7123 hgul33a gt Search for all contexts labeled fetal or liver in moe4302 tabSearch c Fetal Liver moe4302 HH Search for all contexts labeled fetal liver AND in GSE13044 in moe4302 tabSearch c Fetal GSE13044 moe4302 AND Index Topic GSCAeda GSCAeda 9 Topic GSCAplot GSCAplot 12 Topic GSCAui GSCAui 14 Topic GSCA GSCA 6 Topic annotate annotatePeaks 3 Topic datasets genelDdata 6 Oct4ESC_TG 16 STAT1_TG 17 Topic package GSCA GSCA package 2 Topic peaks annotatePeaks 3 Topic plot GSCAplot 12 Topic search tabSearch 18 Topic target genes ConstructTG 4 annotatePeaks 3 ConstructTG 4 genelDdata 6 GSCA 6 GSCA package 2 GSCAeda 9 GSCAplot 12 GSCAui 14 Oct4ESC_TG 16 STAT1_TG 17 tabSearch 18 20
16. ities of samples exhibiting given geneset activity pattern and the most significantly enriched biological contexts are highlighted GSCAplot 13 Usage GSCAplot GSCAoutput N 5 plotfile NULL Title NULL gt Arguments GSCAoutput Exact output from GSCA N N is a numeric value ranging from 1 to 5 It specifies the number of top ranked biological contexts to plot from the GSCA analysis plotfile A character value specifying the path to save the GSCA plot If plotfile is null the plot will not be saved and will appear directly in R console Title A character value specifying the title of the plot Details GSCAplot is a plotting function that acts as an easy to use tool to visualize the GSCA output For one geneset GSCAplot uses histogram to first plot a histogram of geneset activities for all samples in the compendium then plot N histograms of geneset activities for samples in each of top N most significantly enriched biological contexts For two genesets GSCAplots uses plot to make a scatterplot of all samples in the compendium where x axis is the activity of the first geneset and y axis is the activity of the second geneset Then it highlights the top N most significantly enriched biological contexts in different colors and types Cutoff of the two genesets will also be represented on the scatterplot as one vertical and one horizontal dotted line For more than two genesets GSCAplots uses heatmap 2 from gplots package to plot two heatmaps
17. lDs that match the microar ray probeset IDs ConstructTG 5 Details This function is designed as one method to allow users to construct target genes after obtaining a list of significant peaks from ChIP chip or ChIP seq data and differential expression results from using limma to anaylze their microarray data It is not designed to be flexible to account for all methods to obtain TF bound and or differentially expressed genes Users can choose to manually intersect their own TF bound and differentially expressed genes by classifying activated genes as genes whose expression increases when the TF expression increases and repressed genes as genes who expression decreases when the TF expression increases Note that significant cutoffs for peaks and differentially expressed genes need to be already applied prior to input Value Returns a list with two items PosTG Activated TF target genes NegTG Repressed TF target genes Author s Zhicheng Ji Hongkai Ji References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 Examples HH Read in example ChIP seq analyzed data output from GSE11431 for Oct4 in ESCs directly downloaded from NCBI GEO path lt system file extdata package GSCA chipxfile lt read delim paste path GSM288346_ES_Oct4 txt sep header FALSE stringsAsFactors FALSE annotate each peak with the corresponding ge
18. llowup statistical analyses Check the value part of this help file to see how GSCAeda saves the outputs For information on the GSCA parameters see the GSCA help file which explains in more detail how functional enrichment of a geneset activity pattern of interest is tested Value If outputdir is specified GSCAeda will first produce a boxplot depicting the distribution of all geneset activities in different biological contexts of interest Then for each geneset GSCAeda will produce tow heatmaps showing respectively the t statistics and p values obtained from the t tests testing the mean of geneset activity for each pair wise combination of the input biological contexts Finally GSCAeda will output two csv files The first one contains the raw geneset activity values for each input context and the second one contains the mean and standard deviation of the geneset activity values for each context the GSCA enrichment test results and the p values t statistics of the t tests If outputdir is NULL all plots and the result table will be directly displayed in the R console 12 GSCAplot Author s Zhicheng Ji Hongkai Ji References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 See Also GSCA Examples library GSCA Load example STAT1 target genes defined ChIP seq and literature data STAT1_TG Construct genedata and pattern using the same way as GSCA
19. m of the TSS A gene will be annotated to a peak if the region upstream to downstream of each gene TSS as defined by the up and down argu ments overlap with the peak Region downstream of the TSS A gene will be annotated to a peak if the re gion upstream to downstream of each gene TSS as defined by the up and down arguments overlap with the peak A gene will be annotated to a peak if the region upstream to downstream of each gene TSS as defined by the up and down arguments overlap with the peak Value Returns a data frame with the same columns as the input data frame and an additional column containing the Enterz GenelDs for all genes that overlap with the peak Multiple genes will be separated with and 9 will be reported if no genes are found Author s Zhicheng Ji Hongkai Ji 4 ConstructTG References Chen X Xu H Yuan P Fang F et al Integration of external signaling pathways with the core transcriptional network in embryonic stem cells Cell 2008 Jun 13 133 6 1106 17 George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 Examples Read in example ChIP seq analyzed data output from GSE11431 for Oct4 in ESCs directly downloaded from NCBI GEO path lt system file extdata package GSCA inputfile lt read delim paste path GSM288346_ES_Oct4 txt sep header FALSE stringsAsFactors FALSE Note that 1s
20. manual in the Ul for more complete explanations Value A user interface will be shown in users default web browser R console will start listenting to a random port Author s Zhicheng Ji Hongkai Ji References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 See Also GSCA Examples Running this will launch the UI in users default web browser Not run GSCAui End Not run 16 Oct4ESC_TG Oct4ESC_TG Oct4 activated and repressed target genes in embryonic stem cells Description List of Oct4 target genes derived from ChIP seq and gene expression data from embryonic stem cells ESCs Activated target genes are the first item in the list and repressed target genes are the second item in the list Usage data Oct4ESC_TG Format The format is List of 2 chr 1 519 100678 106298 14609 12468 chr 1 337 246703 15441 70579 20333 Details Oct4 target genes are defined as genes that are both predicted to be TF bound in E14 ESCs and differentially expressed after Oct4 knockdown via RNAi in E14TG2a ESCs Source Chen X Xu H Yuan P Fang F et al Integration of external signaling pathways with the core transcriptional network in embryonic stem cells Cell 2008 Jun 13 133 6 1106 17 Loh YH Wu Q Chew JL Vega VB et al The Oct4 and Nanog transcription network regulates pluripotency i
21. n mouse embryonic stem cells Nat Genet 2006 Apr 38 4 431 40 References http www ncbi nlm nih gow geo Examples data Oct4ESC_TG STATI_TG 17 STAT1_TG STATI activated target genes defined from experimental ChIP seq data and literature survey Description List of STAT1 target genes derived from ChIP seq data in Hela cells and further refined by making sure each target gene was further supported by experiments in literature as described in GSE15353 No represed target genes were defined Usage data STAT1_TG Format The format is chr 1 23 9636 2537 2633 1435 103 3433 3434 Details STATI target genes are defined as TF bound from Hela ChIP seq data and then further verified as target genes through literature survey This procedure is described in GSE15353 Source Robertson G Hirst M Bainbridge M Bilenky M et al Genome wide profiles of STAT DNA association using chromatin immunoprecipitation and massively parallel sequencing Nat Methods 2007 Aug 4 8 651 7 References http www ncbi nlm nih gov geo Examples data STAT1_TG 18 tabSearch tabSearch Searches through GPL96 GPL1261 GPL570 or GPL571 com pendium data for biological contexts of interest Description tabSearch requires users to provide keyword s the species and either AND or OR Then the function uses grep and the keywords to iteratively search for biological contexts or experiment IDs t
22. nally if cutoff type is Exprs the geneset expression cutoff will be equal to the value given in the fourth column Fourth column numeric value of cutoff value based on different cutoff types specified in the third column chipdata A character value of hgul33a hgul33A2 hgu133Plus2 or moe4302 This argument specifies which compendium to use Requires the correspond ing data package scaledata logical value indicating whether expression data for each gene should be scaled across samples to have mean 0 and variance 1 Pval co A numeric value specifying the adjusted p value cutoff Only the biological contexts with significant enrichment above the adjusted p value cutoff will be reported in the final ranked table output directory Either null or a character value giving a directory path If directory is not null then additional follow up GSCA analyses will be performed and stored in the folder specified by directory If directory is null then no additional follow up GSCA analyses will be performed Details GSCA requires as input user specified genesets together with their corresponding activity patterns Each geneset contained the Entrez GeneID of activied and repressed genes Activated gene means that increases in expression of the gene also increases the overall activity of the whole geneset while repressed gene means that increases in expression of the gene decreases the overall activity of the whole geneset GSCA
23. ne target annon out lt annotatePeaks chipxfile mm8 10000 5000 Read in example limma output from gene expression data obtained by analyzing Oct4 RNAi knockdown gene with RMA then limma from the raw CEL files in GSE4189 The first column contains the Entrez GeneID for each probeset ID annotated using the mouse4302 db package in Bioconductor gp out lt read delim paste path Pou5f1_E14TG2a_GSE4189_Limma txt sep stringsAsFactors FALSE ConstructTG annon out gp out GSCA geneIDdata Homologene data Description Homologene data to support conversion of ENTREZ gene ID and gene name between human and mouse species References http www ncbi nlm nih gov homologene Examples data geneIDdata GSCA GSCA Description The function takes as input several lists of activated and repressed genes It then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression Usage GSCA genedata pattern chipdata scaledata F Pval co 0 05 directory NULL Arguments genedata pattern A data frame with three columns specifying the input genesets Each row spec ifies an activated or repressed gene in a geneset First column character value of geneset name specified by the user could be any name easy to remember e g GS1 GS2 Second column numeric value of Entrez G
24. ractive user interface GSCAplot oneout plotfile tempfile plot fileext pdf N 2 Title Demo of one geneset plot GSCAplot twoout plotfile tempfile plot fileext pdf Title Demo of two genesets plot GSCAplot threeout plotfile tempfile plot fileext pdf Title Demo of three genesets plot GSCAui Launch GSCA interactive User Interface Description GSCAui initiates in the web browser an interactive user interface of GSCA built using R shiny This user interface enables users to easily perform nearly all standard GSCA functions in GSCA pack age and provides more powerful and useful options to specify geneset activity patterns investigate interested biological contexts and customize output plots and tables For a complete user manual of GSCAui please refer to the user manual included in the user interface Usage GSCAui GSCAui 15 Details The purpose of building GSCA interactive user interface is to provide an easy way for all users to perform analysis tools offered by GSCA even though the users do not have any prior knowledge in computer programming or statistics GSCAui provides users handy ways to input their original dataset into the program Users who do not have much experience using R may find themselves having difficulties building genedata and pattern datasets required by standard GSCA functions In comparison GSCAui offers more covenient ways to directly type in gene IDs and specify parame
25. s Octoutput lt GSCA Octgenedata Octpattern moe4302 Pval co 0 05 directory tempdir All output will be stored in the specified directory This process may be time consuming and generate a lot of files Alternatively see GSCAeda for more info on manual alternatives GSCAeda GSCA follow up exploratory data analysis 10 GSCAeda Description GSCAeda is used to further study GSCA significant predictions in more detail to obtain additional insight into biological function GSCAeda requires users to first run the tabSearch function to identify the biological contexts of interest By default GSCAeda will run automatically after an initial GSCA analysis by searching for all contexts related to the experimentID for each significant GSCA prediction Alternatively users can use GSCAeda by itself to further study any geneset or biological contexts of interest that are found in the compendium The output of GSCAeda are multiple plots displaying the geneset activity values and genes of interest in the input biological contexts Also included are the usual GSCA analysis results table showing the enrichment of each contexts for the geneset activity pattern of interest t test results t statistics and p values for all pair wise combinations of inputted contexts in each geneset and a summary of raw geneset activity values for each context of interest Users can then use the raw geneset activity values for further statistical analyses
26. sname c GS1 rep GS2 activenumtrepressnum gene c 18999 Oct4ESC_TGL 1 Oct4ESC_ We are interested in the pattern that TF and its target genes are all highly expressed We also need to define how high the cutoffs should be such that each cutoff corresponds to the p value of 0 1 based on fitted normal distributions Constructing pattern required by GSCA all geneset names in genedata should appear exactly once in the first column Octpattern lt data frame gsname c GS1 GS2 acttype High cotype Norm cutoff 0 1 stringsAsFactors FALSE Lastly we specify the chipdata to be moe4302 and the significance of enriched biological contexts must be at least 0 05 to be reported Octoutput lt GSCA Octgenedata Octpattern moe4302 Pval co 0 5 The first item in the list Octoutput 1 contains the ranked table which can then be saved Additionally we may be interested in plotting the results to visualize the enriched biological contexts within given geneset activity Here N specifies the top 5 significant biological contexts HH Since plotfile is NULL the plot directly shows up in R Check GSCAplot for more details GSCAplot Octoutput N 5 plotfile NULL Title GSCA plot of Oct4 in ESC If you would like detailed follow up analyses to be automatically performed for the Oct4 analyses in ESCs just specify a file directory Check GSCAeda for more detail
27. t column is chr 2nd and 3rd columns are starting and ending sites of peaks Remaining columns are other output from the peak detection algorithm head inputfile annotatePeaks only requires the first 3 columns annon out lt annotatePeaks inputfile mm8 10000 5000 head annon out ConstructTG Construct target genes for a TF using TF bound genes and differen tially expressed genes from ChIP chip or ChIP seq and TF perturba tion gene expression data Description This function requires users to first analyze their own ChIP chip and ChIP seq data to detect signif icant peaks and then annotate the peaks with their corresponding regulated target genes using the annotatePeaks function in the GSCA package Users must also use the limma package to detect differentially expressed genes in their gene expression data preprocessing and noramzliation can be done with any algorithm the user desires then the resulting output needs to be annotated into Entrez GenelDs Finally with both inputs ConstructTG will identify the activated and repressed TF target genes Usage ConstructTG annonPeaksOut limma0ut Arguments annonPeaksOut Output from the annotatePeaks function in the GSCA package Contains the genes that correspond to the significant peaks detected from TF ChIP chip or ChIP seq data limmaOut Differential expression output from the limma package and requires the first column of the data frame to contain the EntrezGene
28. tor of total number of genes use to calculate the geneset activity in each geneset Numeric vector of number of genes that do not have corresponding expression measuremnts on the platform Character value of the species analyzed If directory is not null then pdf and csv files containing the GSCAeda follow up analysis results and plots in the directory folder will also be returned Author s Zhicheng Ji Hongkai Ji References George Wu et al ChIP PED enhances the analysis of ChIP seq and ChIP chip data Bioinformatics 2013 Apr 23 29 9 1182 1189 GSCAeda 9 Examples First load the TF target genes derived from Oct4 ChIPx data in embryonic stem cells The data is in the form of a list where the first item contains the activated target genes in Entrez GeneID format and the second item contains the repressed target genes in Entrez GeneID format data Oct4ESC_TG We want to analyze Oct4 so we need to specify the EntrezGeneID for Oct4 and input the activated and repressed target genes of Oct4 Constucting the input genedata required by GSCA There are two genesets one is the TF and another is the TF target genes Note that constructing genedata with many genesets could be laborious so using the interactive UI is recommended to easily start up the analysis activenum lt length Oct4ESC_TGL 1 repressnum lt length Oct4ESC_TG L 2 Octgenedata lt data frame g
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