statTest: Analyze dysregulation significance

In Huang-lab/oppti: Outlier Protein and Phosphosite Target Identifier

Analyze dysregulation significance

Description

Rank-order markers by the significance of deviation of the observed expressions from the (matched) imputed expressions based on the Kolmogorov-Smirnov (KS) test.

Usage

statTest(dat, dat.imp, marker.proc.list = NULL, pval.insig = 0.2)

Arguments

dat	an object of log2-normalized protein (or gene) expressions, containing markers in rows and samples in columns.
dat.imp	the imputed data that putatively represents the expressions of the markers in the (matched) normal states.
marker.proc.list	character array, the row names of the data to be processed for dysregulation significance.
pval.insig	p-value threshold to determine spurious (null) dysregulation events.

Value

each marker's p-value of the statistical significance between its observed vs imputed values computed by the KS test.

ranked p-values (KS test) of the significant markers, which are lower than pval.insig.

ranked significantly dysregulated markers with p-values lower than pval.insig.

ranked p-values (KS test) of the insignificant markers, which are greater than pval.insig.

ranked insignificantly dysregulated markers (spurious dysregulations) with p-values greater than pval.insig.

Examples

set.seed(1)
dat = setNames(as.data.frame(matrix(runif(10*10),10,10),
row.names = paste('marker',1:10,sep='')), paste('sample',1:10,sep=''))
dat.imp = artImpute(dat, ku=6)
result = statTest(dat, dat.imp) # the dysregulations on marker4 is
# statistically significant with p-value 0.05244755.

Huang-lab/oppti documentation built on March 26, 2023, 12:52 p.m.