EnrichmentBrowser (version 2.2.2)

ebrowser: Seamless navigation through enrichment analysis results

Description

This is the all-in-one wrapper function to perform the standard enrichment analysis pipeline implemented in the EnrichmentBrowser package. Given flat gene expression data, the data is read in and subsequently subjected to chosen enrichment analysis methods.

The results from different methods can be combined and investigated in detail in the default browser.

Usage

ebrowser( meth, exprs, pdat, fdat, org, data.type = c(NA, "ma", "rseq"),  norm.method = "quantile", de.method = "limma", gs, grn = NULL, perm = 1000, alpha = 0.05, beta = 1, comb = FALSE, browse = TRUE, nr.show = -1 )

Arguments

meth

Enrichment analysis method. Currently, the following enrichment analysis methods are supported: ‘ora’, ‘safe’, ‘gsea’, ‘samgs’, ‘ggea’, ‘spia’, ‘nea’, and ‘pathnet’. See sbea and nbea for details.

exprs

Expression matrix. A tab separated text file containing *normalized* expression values on a *log* scale. Columns = samples/subjects; rows = features/probes/genes; NO headers, row or column names. Supported data types are log2 counts (microarray single-channel), log2 ratios (microarray two-color), and log2-counts per million (RNA-seq logCPMs). See limma's user guide for definition and normalization of the different data types. Alternatively, this can be an object of ExpressionSet-class, assuming the expression matrix in the 'exprs' slot.

pdat

Phenotype data. A tab separated text file containing annotation information for the samples in either *two or three* columns. NO headers, row or column names. The number of rows/samples in this file should match the number of columns/samples of the expression matrix. The 1st column is reserved for the sample IDs; The 2nd column is reserved for a *BINARY* group assignment. Use '0' and '1' for unaffected (controls) and affected (cases) sample class, respectively. For paired samples or sample blocks a third column is expected that defines the blocks. If 'exprs' is an object of ExpressionSet-class, the 'pdat' argument can be left unspecified, which then expects group and optional block assignments in respectively named columns 'GROUP' (mandatory) and 'BLOCK' (optional) in the 'pData' slot of the ExpressionSet.

fdat

Feature data. A tab separated text file containing annotation information for the features. Exactly *TWO* columns; 1st col = feature IDs; 2nd col = corresponding KEGG gene ID for each feature ID in 1st col; NO headers, row or column names. The number of rows/features in this file should match the number of rows/features of the expression matrix. If 'exprs' is an object of ExpressionSet-class, the 'fdat' argument can be left unspecified, which then expects feature and gene IDs in respectively named columns 'PROBE' and 'GENE' in the 'fData' slot of the ExpressionSet.

org

Organism under investigation in KEGG three letter code, e.g. ‘hsa’ for ‘Homo sapiens’. See also kegg.species.code to convert your organism of choice to KEGG three letter code.

data.type

Expression data type. Use 'ma' for microarray and 'rseq' for RNA-seq data. If NA, data.type is automatically guessed. If the expression values in 'eset' are decimal numbers they are assumed to be microarray intensities. Whole numbers are assumed to be RNA-seq read counts. Defaults to NA.

norm.method

Determines whether and how the expression data should be normalized. For available microarray normalization methods see the man page of the limma function normalizeBetweenArrays. For available RNA-seq normalization methods see the man page of the EDASeq function betweenLaneNormalization. Defaults to 'quantile', i.e. normalization is carried out so that quantiles between arrays/lanes/samples are equal. Use 'none' to indicate that the data is already normalized and should not be normalized by ebrowser. See the man page of normalize for details.

de.method

Determines which method is used for per-gene differential expression analysis. See the man page of de.ana for details. Defaults to 'limma', i.e. differential expression is calculated based on the typical limma lmFit procedure.

gs

Gene sets. Either a list of gene sets (vectors of KEGG gene IDs) or a text file in GMT format storing all gene sets under investigation.

grn

Gene regulatory network. Either an absolute file path to a tabular file or a character matrix with exactly *THREE* cols; 1st col = IDs of regulating genes; 2nd col = corresponding regulated genes; 3rd col = regulation effect; Use '+' and '-' for activation/inhibition.

perm

Number of permutations of the expression matrix to estimate the null distribution. Defaults to 1000.

alpha

Statistical significance level. Defaults to 0.05.

beta

Log2 fold change significance level. Defaults to 1 (2-fold).

comb

Logical. Should results be combined if more then one enrichment method is selected? Defaults to FALSE.

browse

Logical. Should results be displayed in the browser for interactive exploration? Defaults to TRUE.

nr.show

Number of gene sets to show. As default all statistical significant gene sets are displayed.

Value

None, opens the browser to explore results.

References

Limma User's guide: http://www.bioconductor.org/packages/limma

See Also

read.eset to read expression data from file; probe.2.gene.eset to transform probe to gene level expression; kegg.species.code maps species name to KEGG code. get.kegg.genesets to retrieve gene set definitions from KEGG; compile.grn.from.kegg to construct a GRN from KEGG pathways; sbea to perform set-based enrichment analysis; nbea to perform network-based enrichment analysis; comb.ea.results to combine results from different methods; ea.browse for exploration of resulting gene sets

Examples

    # expression data from file
    exprs.file <- system.file("extdata/exprs.tab", package="EnrichmentBrowser")
    pdat.file <- system.file("extdata/pData.tab", package="EnrichmentBrowser")
    fdat.file <- system.file("extdata/fData.tab", package="EnrichmentBrowser")
    
    # getting all human KEGG gene sets
    # hsa.gs <- get.kegg.genesets("hsa")
    gs.file <- system.file("extdata/hsa_kegg_gs.gmt", package="EnrichmentBrowser")
    hsa.gs <- parse.genesets.from.GMT(gs.file)

    # set-based enrichment analysis
    ebrowser(   meth="ora", 
            exprs=exprs.file, pdat=pdat.file, fdat=fdat.file, 
            gs=hsa.gs, org="hsa", nr.show=3)

    # compile a gene regulatory network from KEGG pathways
    # hsa.grn <- compile.grn.from.kegg("hsa")
    pwys <- system.file("extdata/hsa_kegg_pwys.zip", package="EnrichmentBrowser")
    hsa.grn <- compile.grn.from.kegg(pwys)
   
    # network-based enrichment analysis
    ebrowser(   meth="ggea", 
            exprs=exprs.file, pdat=pdat.file, fdat=fdat.file, 
            gs=hsa.gs, grn=hsa.grn, org="hsa", nr.show=3 )

    # combining results
    ebrowser(   meth=c("ora", "ggea"), comb=TRUE,
            exprs=exprs.file, pdat=pdat.file, fdat=fdat.file, 
            gs=hsa.gs, grn=hsa.grn, org="hsa", nr.show=3 )