R: Transformations

Transformations {GiANT}

R Documentation

Transformations

Description

Functions to transform the gene-level statistic values prior to the calculation of the gene set statistics, as used in the transformation parameter of gsAnalysis. Most of the functions wrap existing R functions.

Usage

transformation.abs(x)

transformation.square(x)

transformation.localFdr(x,
	statistic="pvalue",
	cutoff.method="fndr",
	pct0=0.75)

transformation.binarize(x, quant)

transformation.rank(x)

transformation.adjust(x, adjMethod = "fdr")

transformation.adjustAndBinarize(x, adjMethod = "fdr", threshold = 0.05)

Arguments

`x`	A numeric vector of gene-level statistic values, one per gene. These values are calculated by the previous step (see `gls`).
`statistic`	Specifies the null model for `transformation.localFdr` (see `statistic` parameter of `fdrtool` for possible values).
`cutoff.method`	Type of cut-off method used in `transformation.localFdr` (see `cutoff.method` parameter of `fdrtool` for possible values).
`pct0`	Fraction of data used by `transformation.localFdr` if `cutoff.method="pct0"` (see `fdrtool` for a detailed description).
`quant`	For `transformation.binarize`, this numeric value in the interval [0,1] defines the percentage of gene-level statistic values which should be set to zero. The remaining values are set to one.
`adjMethod`	The method to use for the adjustment for multiple testing (see `method` parameter of `p.adjust` for possible values).
`threshold`	The threshold for differential expression of a gene (defaults to `0.05`). Values smaller than these threshold are set to 1 , others to 0.

Details

Standard transformation functions for gene-level statistics (to be used in an analysis pipeline defined by gsAnalysis):

transformation.abs: Calculates the absolute values of the elements in x (a wrapper for abs).
transformation.square: Squares all elements in x.
transformation.localFdr: Calculates the local fdr for the elements in x. This is a wrapper for fdrtool.
transformation.binarize: Binarizes the values in x by using the quant quantile as a threshold.
transformation.rank: Ranks the values in x and returns the rank vector.
transformation.adjust: Adjusts for multiple testing according to the adjustment method specified in adjMethod.
transformation.adjustAndBinarize: Adjusts for multiple testing according to the adjustment method specified in adjMethod and binarizes the resulting p-values according to threshold (values smaller than the threshold become 1 others 0).

Value