Basic correlation plot function for normalized or unnormalized counts.

Description

This function plots a heatmap of the "n" features with greatest variance across rows.

Usage

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plotCorr(obj, n, norm = TRUE, log = TRUE, fun = cor, ...)

Arguments

obj

A MRexperiment object with count data.

n

The number of features to plot. This chooses the "n" features with greatest variance.

norm

Whether or not to normalize the counts - if MRexperiment object.

log

Whether or not to log2 transform the counts - if MRexperiment object.

fun

Function to calculate pair-wise relationships. Default is pearson correlation

...

Additional plot arguments.

Value

plotted correlation matrix

See Also

cumNormMat

Examples

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data(mouseData)
plotCorr(obj=mouseData,n=200,cexRow = 0.4,cexCol = 0.4,trace="none",dendrogram="none",
         col = colorRampPalette(brewer.pal(9, "RdBu"))(50))

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