exp2net: Inferring transcriptional networks from gene expression data

In mlDNA: Machine Learning-based Differential Network Analysis of Transcriptome 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.

Note

[1] The GCC, PCC, SCC and KCC calcluate the adjacency matrix more quickly than BiWt, MI and MINE.

[2] The threshold is determined with the permutation method by generating the background distribution of correlations by permuting the expression levels of nrow(expmat) genes from the original expression dataset(expmat) (Carter et al., 2004).

[3] The adjacency and distance matrix can be stored in big.matrix format which can be used to greatly save the memory space. However, this big.matrix optional can only be used on Linux. More information about the big.matrix can be found in the R package bigmemory.

[4] The functions for graph analysis (i.e., getting information of nodes and edges) can be found in the R package igraph.

[5] The calculation of distance matrix is time-consuming for large-scale network.

Author(s)

Chuang Ma, Xiangfeng Wang.

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)

mlDNA documentation built on May 2, 2019, 2:15 p.m.