gene.pvalue: Calculates p-value for gene-gene interaction

Description

To calculate p-value for the null hypothesis that there is no gene-gene interaction. For gene expression data with M genes, a p-value matrix under MxM single null hypotheses (each two genes have no interaction) is computed; besides, matrices with correct p-values are output: corrected permutation method using a distribution of MxMxP (P number of permutations) null hypotheses tests (multi.perm.p.value). p-values are calculated based on the adjacency matrix for gene-gene interaction computed by function gene.similarity.

Usage

gene.pvalue(EXP, measure, net.trim, n.replica = 400)

Arguments

EXP

Gene expression data in form of a matrix. Row stands for genes and column for experiments.

measure

Metric used to calculate similarity between genes: "corr" for correlation, "MI" for mutual information.

net.trim

Method used to trim the network: "mrnet", "clr", "aracne" and "none" . "mrnet" infers a network using the maximum relevance/minimum redundancy feature selection method; "clr" use the CLR algorithm; "aracne" applies the data processing inequality to all triplets of nodes in order to remove the least significant edge in each triplet. These options come from the package minet, and they are used only for mutual information. "none" indicates no trim operation. It should be chosen when correlation is considered.

n.replica

Number of permutations used for the correction of multiple hypothesis testing; default value is 400.

Value

single.perm.p.value: A matrix of single p-values obtained with permutation method + beta distribution for extreme values (for MI) or obtained with the exact distribution computed directly by cor.test (for correlation)
multi.perm.p.value: A matrix of corrected p-values obtained with permutation method

Details

Normally, in a permutation method, we use the empirical distribution of some statistics to estimate the p-value. To get a simple p-value for no interaction between gene i and j, empirical distribution of a vector with length of P (number of replicates) is used; to correct for multiple hypothesis with permutations, an empirical distribution of a vector with length of PxM (M being the number of hypotheses tested) is used.

Examples

Run this code

data(copasi)
mat=as.matrix(copasi)[1:10,]
rownames(mat)<-paste("G",1:nrow(mat), sep="")
gene.pvalue(mat,measure="MI",net.trim="mrnet")