wmwLeadingEdge {BioQC} | R Documentation |
Identify BioQC leading-edge genes of one gene-set
wmwLeadingEdge( matrix, indexVector, valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), thr = 0.05, reference = c("background", "geneset") )
matrix |
A numeric matrix |
indexVector |
An integer vector, giving indices of a gene-set of interest |
valType |
Value type, consistent with the types in |
thr |
Threshold of the value, greater or less than which the gene-set is considered significantly enriched in one sample |
reference |
Character string, which reference is used? If |
A list of integer vectors.
BioQC leading-edge genes are defined as those features whose expression is higher than the median expression of the background in a sample. The function identifies leading-edge genes of a given dataset (specified by the index vector) in a number of samples (specified by the matrix, with genes/features in rows and samples in columns) in three steps. The function calls wmwTest
to run BioQC and identify samples in which the gene-set is significantly enriched. The enrichment criteria is specified by valType
and thr
. Then the function identifies genes in the gene-set that have greater or less expresion than the median value of the reference
in those samples showing significant enrichment. Finally, it reports either leading-edge genes in individual samples, or the intersection/union of leading-edge genes in multiple samples.
myProfile <- c(rnorm(5, 3), rnorm(15, -3), rnorm(100, 0)) myProfile2 <- c(rnorm(15, 3), rnorm(5, -3), rnorm(100, 0)) myProfile3 <- c(rnorm(10, 5), rnorm(10, 0), rnorm(100, 0)) myProfileMat <- cbind(myProfile, myProfile2, myProfile3) wmwLeadingEdge(myProfileMat, 1:20, valType="p.greater") wmwLeadingEdge(myProfileMat, 1:20, valType="log10p.less") wmwLeadingEdge(myProfileMat, 1:20, valType="U", reference="geneset") wmwLeadingEdge(myProfileMat, 1:20, valType="abs.log10p.greater")