plots: Visualization of gene expression

In EnrichmentBrowser: Seamless navigation through combined results of set-based and network-based enrichment analysis

Description

Visualization of differential gene expression via heatmap, p-value histogram and volcano plot (fold change vs. p-value).

Usage

pdistr(p)

volcano(fc, p)

exprsHeatmap(expr, grp, scale.rows = TRUE, log.thresh = 100)

Arguments

p	Numeric vector of p-values for each gene.
fc	Numeric vector of fold changes (typically on log2 scale).
expr	Expression matrix. Rows correspond to genes, columns to samples.
grp	*BINARY* group assignment for the samples. Use '0' and '1' for unaffected (controls) and affected (cases) samples, respectively.
scale.rows	Should rows of the expression matrix be scaled for better visibility of expression differences between sample groups? Defaults to TRUE.
log.thresh	Threshold for log2-transformation of the expression matrix. Particularly useful for heatmap visualization of RNA-seq read count data, where the max and the min of the expression matrix typically differ by several orders of magnitude. If the difference between min and max of the expression matrix is greater than the indicated threshold, log2-transformation is applied.

Value

None, plots to a graphics device.

Author(s)

Ludwig Geistlinger <[e-mail]>

See Also

deAna for differential expression analysis, ComplexHeatmap::Heatmap, and hist for generic plotting.

Examples

    # (1) simulating expression data: 100 genes, 12 samples
    se <- makeExampleData(what="SE") 
    
    # plot heatmap
    exprsHeatmap(expr=assay(se), grp=as.factor(se$GROUP))

    # (2) DE analysis
    se <- deAna(se)
    pdistr(rowData(se)$ADJ.PVAL)
    volcano(fc=rowData(se)$FC, p=rowData(se)$ADJ.PVAL)

EnrichmentBrowser documentation built on Dec. 12, 2020, 2 a.m.