kostsMethod: The Kost Method For Combining P-values

In EmpiricalBrownsMethod: Uses Brown's method to combine p-values from dependent tests

Description

Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package provides an implementation of Kost's Method for combining dependent P-values which is appropriate for highly correlated data sets, like those found in high-throughput biological experiments.

Usage

  kostsMethod(data_matrix, p_values, extra_info)

Arguments

data_matrix	An m x n numeric matrix with m variables in rows and n samples in columns.
p_values	A numeric vector of p-values with length m.
extra_info	boolean, TRUE additionally returns the p-value from Fisher's method, the scale factor c, and the new degrees of freedom from Brown's Method

Value

The output is a list containing list(P_test=p_brown, P_Fisher=p_fisher, Scale_Factor_C=c, DF=df)

P_test	p-value for Kost's method
P_Fisher	p-value for Fisher's method
Scale_Factor	the scale factor c
DF	the degrees of freedom

Examples

## restore the saved values to the current environment
  data(ebmTestData)
  glypGenes <- pathways$gene[pathways$pathway == "GLYPICAN 3 NETWORK"]
  glypPvals <- allPvals$pvalue.with.CHD4[match(glypGenes, allPvals$gene)]
  glypDat   <- as.matrix(dat[match(glypGenes, dat$V1), 2:ncol(dat)])
  kostsMethod(data_matrix=glypDat, p_values=glypPvals, extra_info=TRUE);

Example output

$P_test
[1] 7.570776e-07

$P_Fisher
[1] 1.438732e-08

$Scale_Factor_C
[1] 1.349049

$DF
[1] 10.37768

EmpiricalBrownsMethod documentation built on Nov. 8, 2020, 8:16 p.m.