gene.similarity: Calculate adjacency matrix for gene-gene interaction

In BUS: Gene network reconstruction

Description

To calculate an adjacency matrix for gene-gene interaction (using correlation/mutual information metric). For gene expression data with M genes and N experiments, the adjacency matrix is in size of MxM. It is optional to get a trimmed adjacency matrix according to the argument net.trim, i.e. mrnet, clr andaracne (from the package minet).

Usage

gene.similarity(EXP, measure, net.trim, na.replica = 50)

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 adjacency matrix: "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.
na.replica	Times of replication for filling NANs in the impute method; default value is 50. The (smooth) bootstrapping approach is used to give an estimation to missing value in the data.

Value

An adjacency matrix in size of MxM with rows and columns both standing for genes. Element in row i and column j indicates the similarity between gene i and gene j.

Author(s)

Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini

Examples

data(copasi)
mat=as.matrix(copasi)[1:10,] 
rownames(mat)<-paste("G",1:nrow(mat), sep="")
res<-gene.similarity(mat,measure="corr",net.trim="none")

Example output

Loading required package: minet

BUS documentation built on Nov. 8, 2020, 6:55 p.m.