consensusNet: Construction of a consensus coexpression network

In bingzhang16/NetSAM: Network Seriation And Modularization

Construction of a consensus coexpression network

Description

To increase robustness against errors in data, the consensusNet function uses a bootstrapping procedure to construct a coexpression network.

Usage

  consensusNet(data, organism="hsapiens",bootstrapNum=100, naPer=0.5, meanPer=0.8,varPer=0.8,method="rank_unsig",value=3/1000,pth=1e-6, nMatNet=2, nThreads=4)

Arguments

data	data should contain a file name with extension "cct" or "cbt" or a matrix or data.frame object in R. The first column and first row of the "cct" or "cbt" file should be the row and column names, respectively and other parts are the numeric values. The detail information of "cct" or "cbt" format can be found in the manual of NetGestalt (www.netgestalt.org). A matrix or data.frame object should have row and column names and only contain numeric or integer values.
organism	The organism of the input data. Currently, the package supports the following nine organisms: hsapiens, mmusculus, rnorvegicus, drerio, celegans, scerevisiae, cfamiliaris, dmelanogaster and athaliana. The default is "hsapiens".
bootstrapNum	Number of bootstrap data sets generated. Default is 100.
naPer	To remove ids with missing values in most of samples, the function calculates the percentage of missing values in all samples for each id and removes ids with over naPer missing values in all samples. The default naPer is 0.5.
meanPer	To remove ids with low values, the function calculates the mean of values for each id in all samples and remains top meanPer ids based on the mean. The default meanPer is 0.8.
varPer	Based on the remaining ids filtered by meanPer, the function can also remove less variable ids by calculating the standard deviation of values for each id in all samples and remaining top varPer ids based on the standard deviation. The default varPer is 0.8.
method	Method used for constructing correlation network with MatNet. Currently supports "rank", "value" and "rank_unsig". Default is "rank_unsig".
value	The corresponding value set for method. Default is 0.003.
pth	p-value threshold for including an edge. Default is 1.0e-6.
nMatNet	The number of concurrent running MatNet processes, default is 2.
nThreads	consensusNet function supports parallel computing based on multiple cores. The default is 4.

Author(s)

Jing Wang

Examples

  inputMatDir <- system.file("extdata","exampleExpressionData.cct",package="NetSAM")
  data <- read.table(inputMatDir, header=TRUE, row.names=1, stringsAsFactors=FALSE)
  net <- consensusNet(data, organism="hsapiens",bootstrapNum=10, naPer=0.5, meanPer=0.8,varPer=0.8,method="rank_unsig",value=3/1000,pth=1e-6, nMatNet=2, nThreads=4)

bingzhang16/NetSAM documentation built on April 3, 2024, 3:35 a.m.