Probability Weighting Function
Calculates a Probability Weighting Function for a set of genes based on a given set of biased data (usually gene length) and each genes status as differentially expressed or not.
A named binary vector where 1 represents DE, 0 not DE and the names are gene IDs.
A string identifying the genome that
A string identifying the gene identifier used by
A numeric vector containing the data on which the DE may depend. Usually this is the median transcript length of each gene in bp. If set to
Plot the PWF or not? Calls
It is essential that the entire analysis pipeline, from summarizing raw reads through to using
goseq be done in just one gene identifier format. If your data is in a different format you will need to obtain the gene lengths and supply them to the
nullp function using the
bias.data arguement. Converting to a supported format from another format should be avoided whenever possible as this will almost always result in data loss.
NAs are allowed in the bias.data vector if you do not have information about a certain gene. Setting a gene to
NA is preferable to removing it from the analysis.
bias.data is left as
nullp attempts to use
getlength to fetch GO catgeory to gene identifier mappings.
It is recommended you review the fit produced by the
nullp function before proceeding by leaving
A data frame with 3 columns, named "DEgenes", "bias.data" and "pwf" with the rownames set to the gene names. Each row corresponds to a gene with the DEgenes column specifying if the gene is DE (1 for DE, 0 for not DE), the bias.data column giving the numeric value of the DE bias being accounted for (usually the gene length or number of counts) and the pwf column giving the genes value on the probability weighting function. This object is usually passed to
goseq to calculate enriched categories or
plotPWF for further plotting.
Matthew D. Young firstname.lastname@example.org
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
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