wmwLeadingEdge: Identify BioQC leading-edge genes of one gene-set
In Accio/BioQC: Detect tissue heterogeneity in expression profiles with gene sets

Description Usage Arguments Value See Also Examples

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 `wmwTest`
`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 `background`, genes with expression higher than the median of the background are reported. Otherwise in the case of `geneset`, genes with expression higher than the median of the gene-set is reported. Default is `background`, which is consistent with the results of the Wilcoxon-Mann-Whitney tests.

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.

wmwTest

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")