mlDNA (version 1.1)

exp2net: Inferring transcriptional networks from gene expression data

Description

This function infers transcriptional networks from gene expression data with different statistical methods, including five correlation measures (i.e., the Gini correlation coefficient [GCC], the Pearson's product moment correlation coefficient [PCC], the Kendall tau rank correlation coefficient [KCC], the Spearman's rank correlation coefficient [SCC] and the Tukey's biweight correlation coefficient [BiWt]) and two non-correlation measures (mutual information [MI] and the maximal information-based nonparametric exploration [MINE]).

Usage

exp2net( expmat, method = c("GCC", "PCC", "SCC", "KCC", "BiWt", "MI", "MINE"), pvalue = 0.01, cpus = 1, expDescribe = "Control",  connListFlag = TRUE, distmatFlag = TRUE, saveType = "bigmatrix", netResFileDic, ...  )

Arguments

expmat

a numberic matrix recording gene expression data.

method

a character string specifying the statistical method will be used to calculating the associations between any pairs of genes.

pvalue

a numeric value denoting the significance level of the association will be used to filter unsignficant interactions (i.e., edge) in the network.

cpus

an integer specifying the number of cpus will be used for parallel computing.

expDescribe

an character string describing the expmat.

connListFlag

a logical value indicating whether the connected genes for each gene will be recorded.

distmatFlag

a logical value indicating whether the distance matrix will be calculated.

saveType

an character string indicating the format ("matrix", "bigmatrix") of matrix.

netResFileDic

a character string specifying the file directory will be used to store network-related results.

...

Furture parameters for calcluating distances between two gene sets. For instance, v = c(g1, g2, ..., gn), to = c(g1, g3, ..., gm).

Value

A list with 12 components:

expmat

the input gene expression data.

method

the method used to calcluate the association between two genes.

pvalue

the significance level used to detect edges in the network.

expDescribe

the characterized string for gene expression data.

netResFileDic

the file directory for storing network-related result.

adjmat

adjacency matrix recording the association between any pairs of genes in the big.matrix format.

adjmat_backingfile

the root name for the file for the cache of adjmat. Default: expDescribe_method_adjmat_bfile

adjmat_descriptorfile

the file to be used for the description of the adjmat. Default: expDescribe_method_adjmat_dfile

threshold

the correlation score at the significance level of pvalue.

graph

an igraph object for the constructed network in the edgelist format. This object is save in the file: expDescribe_graph.

connectivityList

a list; For each component, it is a list with three component: "pos" (connected genes with positive correlations), "neg" (connected genes with negative correlations), "all" (all connected genes)

distmatrix

a numeric matrix; the shorest-path distance between any pair of genes in the network.

References

[1] Scott L. Carter, Christian M. Brechbuhler, Michael Griffin and Andrew T. Bond. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics, 2004, 20(14): 2242-2250.

Examples

 ## Not run: 
# 
#    ##suppose the network-related results are stored at:
#    netResFileDic = "/home/wanglab/mlDNA/network/"
#    
#    ##build transcriptional network from the first 1000 genes,
#    ##here a higher number of cpus is suggested. 
#    res <- exp2net( expmat = ControlExpMat[1:1000,], method = "GCC", 
#                    pvalue = 0.01, cpus = 2, 
#                    expDescribe = "Control", connListFlag = TRUE, 
#                    distmatFlag = TRUE, 
#                    saveType = "bigmatrix", netResFileDic = netResFileDic, 
#                    v = rownames(ControlExpMat)[1:10],  ##for calculating distance matrix
#                    to =  rownames(ControlExpMat)[100:120] ) ##from "v" to "to"
#                    
# ## End(Not run)