Grinn

fetchPtCorrGrinnNetwork - compute a partial 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 partial correlation network of input omics data using qpgraph functions. 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

fetchPtCorrGrinnNetwork(datX, corrCoef, pval, alpha, epsilon, matrix.completion, returnAs, xTo, 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.
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.
alpha	a numeric value specifying significance level of each test used in qpAvgNrr.
epsilon	a numeric value specifying the maximum cutoff value of the non-rejection rate met by the edges that are included in the qp-graph, see qpGraph.
matrix.completion	a string specifying algorithm to employ in the matrix completion operations used in qpPAC.
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.
filterSource	string or list of pathway databases. The argument is required, if xTo = "pathway". The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").

Details

datX is matrix in which rows are samples and columns are entities.
- The correlation network can be expand from datX entites to a specific nodetype, by providing a value to xTo.
If xTo is given, the columns of datX are required to use grinn ids for extended queries on the Grinn internal database, see convertToGrinnID for id conversion.
If xTo = 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 partial correlation network of metabolites and expand to a grinn network of metabolite-protein
dummy <- rbind(nodetype=rep("metabolite"),t(mtcars))
colnames(dummy) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 28)))
result <- fetchPtCorrGrinnNetwork(datX=dummy, corrCoef=0.7, pval=0.05, returnAs="tab", xTo="protein")
library(igraph)
plot(graph.data.frame(result$edges[,1:2], directed=FALSE))

References

Partial correlation-based analyses apply methods from the following publications:

Castelo R, et al. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. Mach. Learn. Res., 7:2621-2650.
Castelo R, et al. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J Comput Biol, 16(2), pp. 213-27.