Grinn

fetchCorrGrinnNetwork - compute a weighted correlation network and expand the network with information from Grinn internal database

Description

from input omics data e.g. normalized expression data or metabolomics data, it is a one step function to:
1. Compute a weighted correlation network of input omics data 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.
2. Expand the correlation network using information from the Grinn internal database. The nodes of the correlation network are the keywords input to query the Grinn internal database. The Grinn internal database contains the networks of the following types that can get expanded to: metabolite-protein, metabolite-protein-gene, metabolite-pathway, protein-gene, protein-pathway and gene-pathway, see also fetchGrinnNetwork.

Usage

fetchCorrGrinnNetwork(datX, datY, corrCoef, pval, method, returnAs, xTo, yTo, filterSource)

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 or it can be 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" (default), see cor.
returnAs	string of output type. Specify the type of the returned network. It can be one of "tab","json","cytoscape", default is "tab". "cytoscape" is the format used in Cytoscape.js
xTo	string of node type. It can be one of "metabolite","protein","gene","pathway". See below for details.
yTo	string of node type. It can be one of "metabolite","protein","gene","pathway". See below for details.
filterSource	string or list of pathway databases. The argument is required, if sourceTo or targetTo = "pathway". The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").

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. In this case:
- The correlation network can be expand from datX (by providing a value to xTo) or datY (by providing a value to yTo) or both entities to the specified nodetype.
Otherwise if datY is not given, the correlations of the columns of datX are computed. In this case:
- The correlation network can be expand from datX entites to a specific nodetype, by providing a value to xTo.
If xTo or yTo or both is given, the columns of both datX and datY are required to use grinn ids for extended queries on the Grinn internal database, see convertToGrinnID for id conversion.
If xTo = NULL and yTo = NULL, only the correlation network will be returned.

Value

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

Examples


# Compute a correlation network of metabolites and expand to a grinn network of metabolite-protein
dummy <- rbind(nodetype=rep("metabolite"),t(mtcars))
colnames(dummy) <- c('X1.1','X27967','X371','X4.1',paste0('X',sample(400:22000, 28)))
result <- fetchCorrGrinnNetwork(datX=dummy, corrCoef=0.7, pval=1e-12, method="spearman", returnAs="tab", xTo="protein")
library(igraph)
plot(graph.data.frame(result$edges[,1:2], directed=FALSE))

# Compute a correlation network of metabolites and proteins and expand to the grinn network of metabolite-pathway and protein-gene
dummyX <- rbind(nodetype=rep("metabolite"),t(mtcars)[,1:16])
colnames(dummyX) <- c('X1.1','X27967','X371','X4.1',paste0('X',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 <- fetchCorrGrinnNetwork(datX=dummyX, datY=dummyY, corrCoef=0.7, pval=1e-4, method="spearman", returnAs="json", xTo="pathway", yTo="gene")

References

Correlation-based analyses apply methods from the following publications:

Dudoit S, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, STATISTICA SINICA 2002;12:111.
Langfelder P, et al. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
Langfelder P, et al. Tutorials for the WGCNA package, click here