plots: Visualization of gene expression

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

Visualization of gene expression

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)

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.

Value

None, plots to a graphics device.

Author(s)

Ludwig Geistlinger

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)

lgeistlinger/EnrichmentBrowser documentation built on Oct. 29, 2023, 5:08 p.m.