Plot the Probability Weighting Function

Description

Plots the Probability Weighting Function created by nullp by binning together genes.

Usage

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plotPWF(pwf,binsize="auto",pwf_col=3,pwf_lwd=2,xlab="Biased Data in <binsize> gene bins.",ylab="Proportion DE",...)

Arguments

pwf

A data frame with 3 columns named DEgenes, bias.data & pwf and row names giving the gene names. Usually generated by nullp.

binsize

Calculate and plot the fraction of genes that are DE in bins of this size. If set to "auto" the best binsize for visualization is attempted to be found automatically.

pwf_col

The colour of the probability weighting function

pwf_lwd

The width of the probability weighting function

xlab

The x-axis label. <binsize> is replaced by the binsize used.

ylab

The y-axis label.

...

Extra arguments that are passed to plot.

Details

This function is almost always called using the output from the nullp function. However, it can be used to visualize the length (or any other type of quantifyable) bias in ability to detect DE in a data set. The pwf arguement needs to be a data frame with 3 columns each containing numeric entries (although NAs are permitted in the bias.data and pwf columns), which must be named "DEgenes", "bias.data" and "pwf", although they can appear in any order. The row names are taken to be the gene names. The DEgenes column should be 0s or 1s where 1 represents a DE gene, 0 a gene which is not DE. The bias.data column is a quantification of the quantity for which there is a bias in detecting DE for the associated gene, this is usually gene length or the number of counts associated with a gene. Finally, the pwf column gives the probability weighting to be applied for a given gene.

Value

Nothing is returned.

Author(s)

Matthew D. Young myoung@wehi.edu.au

References

Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biology Date: Feb 2010 Vol: 11 Issue: 2 Pages: R14

See Also

nullp

Examples

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data(genes)
pwf <- nullp(genes, 'hg19', 'ensGene',plot.fit=FALSE)
plotPWF(pwf,binsize=200)