GeneLevelStatistics {GiANT} | R Documentation |
Gene-level statistics
Description
Functions to calculate the gene-level statistic, as used in the gls
parameter of gsAnalysis
.
A gene-level statistic calculates a measure of correlation between the expression of a gene and the class labels.
Usage
gls.cor(dat, labs, method = "pearson")
gls.regression(dat, labs)
gls.foldChange(dat, labs, logMeasurements = TRUE)
gls.tStatistic(dat, labs, pValue = FALSE, alternative = "two.sided")
gls.moderateTStatistic(dat,labs)
gls.nBinomTest(dat, labs,
returnValue = c("pval", "qval", "foldChange", "log2FoldChange"),
dispersionMethod = "blind",
dispersionSharingMode = "fit-only",
dispersionFitType = "local")
Arguments
dat |
A numeric matrix of gene expression values for all analyzed genes. Here, each row corresponds to one gene, and each column corresponds to one sample. The rows must be named with the gene names used in the gene sets. |
labs |
A vector of class labels for the samples in |
logMeasurements |
For |
method |
For |
pValue |
For |
alternative |
For |
returnValue |
For |
dispersionMethod |
For |
dispersionSharingMode |
For |
dispersionFitType |
For |
Details
Standard functions for the calculation of gene-level statistics (to be used in an analysis pipeline defined by gsAnalysis
):
-
gls.cor
: Calculates the correlation of the gene expression values to the class labels. -
gls.regression
: Calculates the slope of a linear regression of the gene expression values and the class labels. -
gls.foldChange
: Calculates the (standard or log2) fold change between the measurements for the two classes. -
gls.tStatistic
: Calculates the p-value or the statistic of a two-sample t test for the measurements of the two classes -
gls.moderateTStatistic
: Calculates the moderate t statistic for the measurements of the two classes -
gls.nBinomTest
: Applies the negative binomial test for sequencing data based on the DESeq2 package to test for differences between two classes (seeresults
).
Value
Each of these function returns a numeric vector of gene-level statistics (one entry per gene).
See Also
geneSetAnalysis
, gsAnalysis
, gss
, transformation