plots: Visualization of gene expression

Description Usage Arguments Value Author(s) See Also Examples

Description

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

Usage

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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 <Ludwig.Geistlinger@sph.cuny.edu>

See Also

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

Examples

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    # (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 July 26, 2021, 9:54 p.m.