Grinn

fetchModuGrinnNetwork - Compute a network module correlated to a phenotypic feature and expand the network with information from Grinn internal database

Description

from input omics data e.g. normalized expression data or metabolomics data, and phenotypic data it is a one step function to:
1. Identify correlation between the input omics data e.g. normalized gene expression data, and phenotypic data e.g. weight. The function wraps around important aspects of WGCNA including blockwiseModules, cor, corPvalueStudent, labeledHeatmap. These aspects automatically perform correlation network construction, module detection, and display module-phenotype correlations. A module or the combination of modules can be selected from the heatmap of module-phenotype correlations for including in the network output.
2. Expand the network module using information from the Grinn internal database. The nodes of the network module are the keywords input to query the grinn 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

fetchModuGrinnNetwork(datX, datPheno, sfPower, minModuleSize, threshold, returnAs, xTo, filterSource)

Arguments

datX	data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities. The column names of datNorm are required to use grinn ids. convertToGrinnID is provided for id conversion, see convertToGrinnID. 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.
datPheno	data frame containing phenotypic data e.g. weight, age, insulin sensitivity. Columns correspond to phenotypes and rows to samples e.g. normals, tumors.
sfPower	numerical value of soft-thresholding power for correlation network construction. It is automatically estimated using pickSoftThreshold, or it can be defined by users.
minModuleSize	numerical value of minimum module size for module detection.
threshold	numerical value to define the minimum value of similarity threshold, from 0 to 1, to include edges in the output.
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 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 is a matrix in which rows are samples and columns are entities. The network can be expand from datX entities to the specified nodetype by providing a value to xTo. If xTo = NULL, only the network module will be returned. The function encapsulates several methods from WGCNA so that module-phenoty correlation analysis can be fasten.

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 of metabolite module to phenotypic data and expand to a grinn network metabolite-pathway, using information from REACTOME and KEGG only
library(grinn)
data(dummy)
data(dummyPheno)
result <- fetchModuGrinnNetwork(datX=dummy, datPheno=dummyPheno, minModuleSize=5, threshold=0.2, returnAs="tab", xTo="pathway", filterSource=c("REACTOME","KEGG"))
#enter module color(s) seperate by space:yellow brown purple

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