egsea {EGSEA} | R Documentation

## Core functions to perform ensemble of gene set enrichment analysis (EGSEA)

### Description

`egsea` is the main function to carry out gene set enrichment analysis using the EGSEA algorithm. This function is aimed to extend the limma-voom pipeline of RNA-seq analysis.

`egsea.cnt` is the main function to carry out gene set enrichment analysis using the EGSEA algorithm. This function is aimed to use raw RNASeq count matrix to perform the EGSEA analysis.

`egsea.ora` is the main function to carry out over-representation analysis (ORA) on gene set collections using a list of genes.

`egsea.ma` is the main function to carry out gene set enrichment analysis using the EGSEA algorithm. This function is aimed to use a microarray expression matrix to perform the EGSEA analysis.

### Usage

    egsea(voom.results, contrasts = NULL, logFC = NULL, gs.annots,
      symbolsMap = NULL, baseGSEAs = egsea.base(), minSize = 2,
      display.top = 20, combineMethod = "wilkinson", combineWeights = NULL,
      sort.by = "p.adj", report.dir = NULL, kegg.dir = NULL,
      logFC.cutoff = 0, fdr.cutoff = 0.05, sum.plot.axis = "p.adj",
      sum.plot.cutoff = NULL, vote.bin.width = 5, num.threads = 4,
      report = TRUE, interactive = FALSE, keep.base = FALSE,
      verbose = FALSE, keep.limma = TRUE, keep.set.scores = FALSE)

    egsea.cnt(counts, group, design = NULL, contrasts = NULL, logFC = NULL,
      gs.annots, symbolsMap = NULL, baseGSEAs = egsea.base(), minSize = 2,
      display.top = 20, combineMethod = "wilkinson", combineWeights = NULL,
      sort.by = "p.adj", report.dir = NULL, kegg.dir = NULL,
      logFC.cutoff = 0, fdr.cutoff = 0.05, sum.plot.axis = "p.adj",
      sum.plot.cutoff = NULL, vote.bin.width = 5, num.threads = 4,
      report = TRUE, interactive = FALSE, keep.base = FALSE,
      verbose = FALSE, keep.limma = TRUE, keep.set.scores = FALSE)

    egsea.ora(geneIDs, universe = NULL, logFC = NULL, title = NULL, gs.annots,
      symbolsMap = NULL, minSize = 2, display.top = 20, sort.by = "p.adj",
      report.dir = NULL, kegg.dir = NULL, sum.plot.axis = "p.adj",
      sum.plot.cutoff = NULL, num.threads = 4, report = TRUE,
      interactive = FALSE, verbose = FALSE)

    egsea.ma(expr, group, probe.annot, probeMap.method = "avg", design = NULL,
      contrasts = NULL, logFC = NULL, gs.annots, baseGSEAs = egsea.base(),
      minSize = 2, display.top = 20, combineMethod = "wilkinson",
      combineWeights = NULL, sort.by = "p.adj", report.dir = NULL,
      kegg.dir = NULL, logFC.cutoff = 0, fdr.cutoff = 0.05,
      sum.plot.axis = "p.adj", sum.plot.cutoff = NULL, vote.bin.width = 5,
      num.threads = 4, report = TRUE, interactive = FALSE,
      keep.base = FALSE, verbose = FALSE, keep.limma = TRUE,
      keep.set.scores = FALSE)

### Arguments

`voom.results` | list, an EList object generated using the `voom` function, which it has at least three elements: `E` log2 normalized expression values, `design` design matrix, `targets` sample information, which must have a column named group. If a matrix `weights` is provided, it will be used in `limma`-based methods. Entrez Gene IDs should be used as row names.
`contrasts` | double, an N x L matrix indicates the contrasts of the linear model coefficients for which the test is required if the design matrix does not have an intercept. N is number of columns of the design matrix and L is number of contrasts. This matrix should be based on the primary factor of the design matrix. If the design matrix includes an intercept, this parameter can be a vector of integers that specify the columns of the design matrix. If this parameter is `NULL`, all pairwise comparisons based on `group` or `voom.results$targets$group` are created, assuming that `group` is the primary factor in the `design` matrix. Likewise, all the coefficients of the primary factor are used if the `design` matrix has an intercept.
`logFC` | double, an K x L matrix indicates the log2 fold change of each gene for each contrast. K is the number of genes included in the analysis. If logFC=NULL, the logFC values are estimated using the `ebayes` for each contrast. For `egsea.ora`, it can be a matrix or vector of the same length of `entrezIDs`. If logFC=NULL, 1 is used as a default value. Then, the regulation direction in heatmaps and pathway maps is not indicative of the gene regulation direction.
`gs.annots` | list, list of objects of class GSCollectionIndex. It is generated using one of these functions: `buildIdx`, `buildMSigDBIdx`, `buildKEGGIdx`, `buildGeneSetDBIdx`, and `buildCustomIdx`.
`symbolsMap` | dataframe, an K x 2 matrix stores the gene symbol of each Entrez Gene ID. The first column must be the Entrez Gene IDs and the second column must be the Gene Symbols. It is used for the heatmap visualization. In `egsea` and `egsea.cnt`, the number of rows should match that of the voom.results and counts, respectively. Default symbolsMap=NULL.
`baseGSEAs` | character, a vector of the gene set tests that should be included in the ensemble. Type `egsea.base` to see the supported GSE methods. By default, all supported methods are used.
`minSize` | integer, the minimum size of a gene set to be included in the analysis. Default minSize= 2.
`display.top` | integer, the number of top gene sets to be displayed in the EGSEA report. You can always access the list of all tested gene sets using the returned gsa list. Default is 20.
`combineMethod` | character, determines how to combine p-values from different GSEA method. Type `egsea.combine`() to see supported methods.
`combineWeights` | double, a vector determines how different GSEA methods will be weighted. Its values should range between 0 and 1. This option is not supported currently.
`sort.by` | character, determines how to order the analysis results in the stats table. Type `egsea.sort`() to see all available options. For `egsea.ora`, it takes "p.value", "p.adj" or "Significance".
`report.dir` | character, directory into which the analysis results are written out.
`kegg.dir` | character, the directory of KEGG pathway data file (.xml) and image file (.png). Default kegg.dir=paste0(report.dir, "/kegg-dir/").
`logFC.cutoff` | numeric, cut-off threshold of logFC and is used for the calculation of Sginificance Score and Regulation Direction. Default logFC.cutoff=0.
`fdr.cutoff` | numeric, cut-off threshold of DE genes and is used for the calculation of Significance Score and Regulation Direction. Default fdr.cutoff = 0.05.
`sum.plot.axis` | character, the x-axis of the summary plot. All the values accepted by the sort.by parameter can be used. Default sum.plot.axis="p.value".
`sum.plot.cutoff` | numeric, cut-off threshold to filter the gene sets of the summary plots based on the values of the sum.plot.axis. Default sum.plot.cutoff=NULL.
`vote.bin.width` | numeric, the bin width of the vote ranking. Default vote.bin.width=5.
`num.threads` | numeric, number of CPU cores to be used. Default num.threads=2.
`report` | logical, whether to generate the EGSEA interactive report. It takes longer time to run. Default is True.
`interactive` | logical, whether to generate interactive tables and plots. Note this might dramatically increase the size of the EGSEA report.
`keep.base` | logical, whether to write out the results of the individual GSE methods. Default FALSE.
`verbose` | logical, whether to print out progress messages and warnings.
`keep.limma` | logical, whether to store the results of the limma analysis in the EGSEAResults object.
`keep.set.scores` | logical, whether to calculate the gene set enrichment scores per sample for the methods that support this option, i.e., "ssgsea".
`counts` | double, an K x M numeric matrix of read counts where genes are the rows and samples are the columns. In this case, TMM normalization is used to calculate the normalization factors. `counts` can be also a `DGEList` object. In this case, the normalization factors can be calculated by the user prior to invoking this method. This is REQUIRED.
`group` | character, vector or factor giving the experimental group/condition for each sample/library. This is REQUIRED.
`design` | double, an M x N numeric matrix giving the design matrix of the linear model fitting. N is the number of coefficients in model. If this parameter is `NULL`, `model.matrix(~0+group)` is used to create a deisgn matrix.
`geneIDs` | character, a vector of Gene IDs to be tested for ORA. They must be Entrez IDs if EGSEAdata collections are used.
`universe` | character, a vector of Enterz IDs to be used as a background list. If universe=NULL, the background list is created from the AnnotationDbi package.
`title` | character, a short description of the experimental contrast.
`expr` | double, an K x M numeric matrix of intensities where genes are the rows and samples are the columns. In this case, it is assumed that the expression values are already normalized log2 intensities and filtered, i.e. rows corresponding to control and low-quality probes removed. Row names of `expr` must match the first column in `probe.annot`. This is REQUIRED.
`probe.annot` | double, an K x n numeric matrix where rows are probes, the first column contains probe IDs, the second column contains Entrez Gene IDs, and the third column (optional) contains the Gene Symbols. Symbols are used for the heatmap visualization Additional annotation columns can be added for the probes, e.g., Chromosome, QualityScore, etc. These additional columns are not used. This is REQUIRED.
`probeMap.method` | character, the method to be used in mapping the Probe IDs into Enterz Gene IDs. Accepted methods include: "avg", "med", "var", "sum" and "iqr". EGSEA selects the probe with the highest average, median, variance, sum or IQR of expression, respectively, as a representative for each expressed gene.

### Details

EGSEA, an acronym for Ensemble of Gene Set Enrichment Analyses, utilizes the analysis results of eleven prominent GSE algorithms from the literature to calculate collective significance scores for gene sets. These methods include: ora, globaltest, plage, safe, zscore, gage, ssgsea, roast, fry, padog, camera and gsva. The ora, gage, camera and gsva methods depend on a competitive null hypothesis while the remaining seven methods are based on a self-contained hypothesis. Conveniently, the algorithm proposed here is not limited to these twelve GSE methods and new GSE tests can be easily integrated into the framework. This function takes the voom object and the contrast matrix as parameters. The results of EGSEA can be seen using the `topSets` function.
EGSEA report is an interactive HTML report that is generated if `report=TRUE` to enable a swift navigation through the results of an EGSEA analysis. The following pages are generated for each gene set collection and contrast/comparison:
1. Stats Table page shows the detailed statistics of the EGSEA analysis for the `display.top` gene sets. It shows the EGSEA scores, individual rankings and additional annotation for each gene set. Hyperlinks to the source of each gene set can be seen in this table when they are available. The "Direction" column shows the regulation direction of a gene set which is calculated based on the `logFC`, which is either calculated from the limma differential expression analysis or provided by the user. The `logFC.cutoff` and `fdr.cutoff` are applied for this calculation. The calculations of the EGSEA scores can be seen in the references section. The method `topSets` can be used to generate custom Stats Table.
2. Heatmaps page shows the heatmaps of the gene fold changes for the gene sets that are presented in the Stats Table page. Red indicates up-regulation while blue indicates down-regulation. Only genes that appear in the input expression/count matrix are visualized in the heat map. Gene names are coloured based on their statistical significance in the `limma` differential expression analysis. The "Interpret Results" link below each heat map allows the user to download the original heat map values along with additional statistics from `limma` DE analysis ( if available) so that they can be used to perform further analysis in R, e.g., customizing the heat map visualization. Additional heat maps can be generated and customized using the method `plotHeatmap`.
3. Summary Plots page shows the methods ranking plot along with the summary plots of EGSEA analysis. The method plot uses multidimensional scaling (MDS) to visualize the ranking of individual methods on a given gene set collection. The summary plots are bubble plots that visualize the distribution of gene sets based on the EGSEA Significance Score and another EGSEA score (default, p-value). Two summary plots are generated: ranking and directional plots. Each gene set is reprersented with a bubble which is coloured based on the EGSEA ranking (in ranking plots ) or gene set regulation direction (in directional plots) and sized based on the gene set cardinality (in ranking plots) or EGSEA Significance score (in directional plots). Since the EGSEA "Significance Score" is proportional to the p-value and the absolute fold changes, it could be useful to highlight gene sets that have high Significance scores. The blue labels on the summary plot indicate gene sets that do not appear in the top 10 list of gene sets based on the "sort.by" argument (black labels) yet they appear in the top 5 list of gene sets based on the EGSEA "Significance Score". If two contrasts are provided, the rank is calculated based on the "comparison" analysis results and the "Significance Score" is calculated as the mean. If `sort.by = NULL`, the slot `sort.by` of the `object` is used to order gene sets. The method `plotSummary` can be used to customize the Summary plots by changing the x-axis score and filtering bubbles based on the values of the x-axis. The method `plotMethods` can be used to generate Methods plots.
4. Pathways page shows the KEGG pathways for the gene sets that are presented in the Stats Table of a KEGG gene set collection. The gene fold changes are overlaid on the pathway maps and coloured based on the gene regulation direction: blue for down-regulation and red for up-regulation. The method `plotPathway` can be used to generate additional pathway maps. Note that this page only appears if a KEGG gene set collection is used in the EGSEA analysis.
5. Go Graphs page shows the Gene Ontology graphs for top 5 GO terms in each of three GO categories: Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC). Nodes are coloured based on the default `sort.by` score where red indicates high significance and yellow indicates low significance. The method `plotGOGraph` can be used to customize GO graphs by changing the default sorting score and the number of significance nodes that can be visualized. It is recommended that a small number of nodes is selected. Note that this page only appears if a Gene Ontology gene set collection is used, i.e., for the c5 collection from MSigDB or the gsdbgo collection from GeneSetDB.
Finally, the "Interpret Results" hyperlink in the EGSEA report allows the user to download the fold changes and limma analysis results and thus improve the interpretation of the results.
Note that the running time of this function significantly increseas when `report = TRUE`. For example, the analysis in the example section below was conducted on the $203$ signaling and disease KEGG pathways using a MacBook Pro machine that had a 2.8 GHz Intel Core i7 CPU and 16 GB of RAM. The execution time varied between 23.1 seconds (single thread) to 7.9 seconds (16 threads) when the HTML report generation was disabled. The execution time took 145.5 seconds when the report generation was enabled using 16 threads.

`egsea.ora` takes a list of gene IDs and uses the gene set collections from EGSEAdata or a custom-built collection to find over-represented gene sets in this list. It takes the advantage of the existing EGSEA reporting capabilities and generate an interative report for the ORA analysis. The results can be explored using the `topSets` function.

### Value

`egsea` returns an object of the class EGSEAResults, which stores the top gene sets and the detailed analysis results for each contrast and the comparative analysis results.

`egsea.cnt` returns an object of the class EGSEAResults, which stores the top gene sets and the detailed analysis results for each contrast and the comparative analysis results.

`egsea.ora` returns an object of the class EGSEAResults, which stores the top gene sets and the detailed analysis results.

`egsea.ma` returns an object of the class EGSEAResults, which stores the top gene sets and the detailed analysis results for each contrast and the comparative analysis results.

### References

Monther Alhamdoosh, Milica Ng, Nicholas J. Wilson, Julie M. Sheridan, Huy Huynh, Michael J. Wilson, Matthew E. Ritchie; Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics 2017; 33 (3): 414-424. doi: 10.1093/bioinformatics/btw623

### See Also

`topSets`, `egsea.base`, `egsea.sort`, `buildIdx`, `buildMSigDBIdx`, `buildKEGGIdx`, `buildGeneSetDBIdx`, and `buildCustomIdx`

### Examples

    # Example of egsea
    library(EGSEAdata)
    data(il13.data)
    v = il13.data$voom
    contrasts = il13.data$contra
    gs.annots = buildIdx(entrezIDs=rownames(v$E), species="human",
    msigdb.gsets="none",
             kegg.updated=FALSE, kegg.exclude = c("Metabolism"))
    # set report = TRUE to generate the EGSEA interactive report
    gsa = egsea(voom.results=v, contrasts=contrasts,  gs.annots=gs.annots,
             symbolsMap=v$genes, baseGSEAs=egsea.base()[-c(2,5,6,9,12)],
    			display.top = 5, sort.by="avg.rank",
    			report.dir="./il13-egsea-report",
             num.threads = 2, report = FALSE)
    topSets(gsa)

    # Example of egsea.cnt
    library(EGSEAdata)
    data(il13.data.cnt)
    cnt = il13.data.cnt$counts
    group = il13.data.cnt$group
    design = il13.data.cnt$design
    contrasts = il13.data.cnt$contra
    genes = il13.data.cnt$genes
    gs.annots = buildIdx(entrezIDs=rownames(cnt), species="human",
    msigdb.gsets="none",
             kegg.updated=FALSE, kegg.exclude = c("Metabolism"))
    # set report = TRUE to generate the EGSEA interactive report
    gsa = egsea.cnt(counts=cnt, group=group, design=design, contrasts=contrasts,
             gs.annots=gs.annots,
             symbolsMap=genes, baseGSEAs=egsea.base()[-c(2,5,6,9,12)],
    display.top = 5,
              sort.by="avg.rank",
    report.dir="./il13-egsea-cnt-report",
             num.threads = 2, report = FALSE)
    topSets(gsa)

    # Example of egsea.ora
    library(EGSEAdata)
    data(il13.data)
    voom.results = il13.data$voom
    contrast = il13.data$contra
    library(limma)
    vfit = lmFit(voom.results, voom.results$design)
    vfit = contrasts.fit(vfit, contrast)
    vfit = eBayes(vfit)
    top.Table = topTable(vfit, coef=1, number=Inf, p.value=0.05, lfc=1)
    deGenes = as.character(top.Table$FeatureID)
    logFC =  top.Table$logFC
    names(logFC) = deGenes
    gs.annots = buildIdx(entrezIDs=deGenes, species="human",
    msigdb.gsets="none",
             kegg.updated=FALSE, kegg.exclude = c("Metabolism"))
    # set report = TRUE to generate the EGSEA interactive report
    gsa = egsea.ora(geneIDs=deGenes, universe=
    as.character(voom.results$genes[,1]),
                 logFC =logFC, title="X24IL13-X24",
    gs.annots=gs.annots,
                 symbolsMap=top.Table[, c(1,2)], display.top = 5,
                  report.dir="./il13-egsea-ora-report", num.threads = 2,
    				report = FALSE)
    topSets(gsa)

    # Example of egsea.ma
    library(Glimma)
    data(arraydata)
    expr = arraydata$arrays$E
    group = as.factor(arraydata$targets$Condition)
    levels(group) = c("DPcreEzh2", "DPev", "LumcreEzh2", "Lumev")
    probe.annot = arraydata$arrays$genes[-1]
    design <- model.matrix(~0+ group + as.factor(arraydata$targets$Experiment))
    colnames(design)[1:4] = levels(group)
    colnames(design)[5:6] = c("Exp2", "Exp3")
    contr = makeContrasts("DPEzh2KOvsWT" = DPcreEzh2-DPev,
                            "LumEzh2KOvsWT" = LumcreEzh2-Lumev,
                          levels=colnames(design))

    gs.annots = buildIdx(entrezIDs=unique(probe.annot[, 2]),
    		 	species="mouse",
    			msigdb.gsets="none",
             kegg.updated=FALSE, kegg.exclude = c("Metabolism"))

    # set report = TRUE to generate the EGSEA interactive report
    gsa = egsea.ma(expr=expr, group=group,
    			probe.annot = probe.annot,
    			design=design, contrasts=contr,
             gs.annots=gs.annots,
             baseGSEAs=egsea.base()[-c(2,5,6,9,12)],
    			display.top = 5,
              sort.by="avg.rank",
    			report.dir="./ezh2-egsea-ma-report",
             num.threads = 2, report = FALSE)
    topSets(gsa)

[Package EGSEA version 1.10.1 Index]