transformation: Transformations

In GiANT: Gene Set Uncertainty in Enrichment Analysis

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

All functions return a vector of transformed values having the same length as x.

See Also

geneSetAnalysis, gsAnalysis, gss, gls

GiANT documentation built on Oct. 23, 2020, 7:56 p.m.