fetchCorrNetwork: Compute a weighted correlation network

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/fetchCorrNetwork.R

Description

from the input data e.g. normalized expression data, build the correlation network based on WGCNA. Correlation coefficients, pvalues and relation directions are calculated using WGCNA functions cor and corPvalueStudent. The correlation coefficients are continuous values between -1 (negative correlation) and 1 (positive correlation), with numbers close to 1 or -1, meaning very closely correlated. datX and datY are matrices in which rows are samples and columns are entities.

Usage

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fetchCorrNetwork(datX, datY, corrCoef, pval, method, returnAs)

Arguments

datX

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities. Columns correspond to entities e.g. genes, metabolites, and rows to samples e.g. normals, tumors. Require 'nodetype' at the first row to indicate the type of entities in each column. See below for details.

datY

data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities. Use the same format as datX. If there is only one type of dataset, datY must be datY = NULL. See below for details.

corrCoef

numerical value to define the minimum value of absolute correlation, from 0 to 1, to include edges in the output.

pval

numerical value to define the maximum value of pvalues, to include edges in the output.

method

string to define which correlation is to be used. It can be one of "pearson","kendall","spearman", see cor.

returnAs

string of output type. Specify the type of the returned network. It can be one of "tab","json","cytoscape". "cytoscape" is the format used in Cytoscape.js

Details

datX and datY are matrices in which rows are samples and columns are entities. If datY is given, then the correlations between the columns of datX and the columns of datY are computed. Otherwise if datY is not given, the correlations of the columns of datX are computed. If grinn functions will be used in further analyses, the column names of both datX and datY are suggested to use grinn ids. convertToGrinnID is provided for id conversion, see convertToGrinnID.

Value

list of nodes and edges. The list is with the following componens: edges and nodes. Return empty list if found nothing

Author(s)

Kwanjeera W kwanich@ucdavis.edu

References

Langfelder P. and Horvath S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559

Dudoit S., Yang YH., Callow MJ. and Speed TP. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, STATISTICA SINICA, 12:111

Langfelder P. and Horvath S. Tutorials for the WGCNA package http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

See Also

cor, corPvalueStudent, http://js.cytoscape.org/

Examples

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# Compute a correlation network of metabolites
#dummy <- rbind(nodetype=rep("metabolite"),t(mtcars))
#colnames(dummy) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 28)))
#result <- fetchCorrNetwork(datX=dummy, datY=NULL, corrCoef=0.7, pval=1e-12, method="spearman", returnAs="tab")
#library(igraph)
#plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
# Compute a correlation network of metabolites and proteins
#dummyX <- rbind(nodetype=rep("metabolite"),t(mtcars)[,1:16])
#colnames(dummyX) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 12)))
#dummyY <- rbind(nodetype=rep("protein"),t(mtcars)[,17:32])
#colnames(dummyY) <- c('P28845','P08235','Q08AG9','P80365',paste0('P',sample(10000:80000, 12)))
#result <- fetchCorrNetwork(datX=dummyX, datY=dummyY, corrCoef=0.7, pval=1e-4, method="spearman", returnAs="tab")

kwanjeeraw/grinn documentation built on May 20, 2019, 7:07 p.m.