fetchGrinnCorrNetwork: Combine a grinn network queried from Grinn internal database...
In kwanjeeraw/grinn: graph database and R package for omics data integration

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

from the list of keywords and input omics data e.g. normalized expression data or metabolomics data, it is a one step function to:

1. Build an integrated network (grinn network) by connecting these keywords to a specified node type, see fetchGrinnNetwork. The keywords can be any of these node types: metabolite, protein, gene and pathway. The Grinn internal database contains the networks of the following types that can be quried: metabolite-protein, metabolite-protein-gene, metabolite-pathway, protein-gene, protein-pathway and gene-pathway.

2. Compute a weighted correlation network of input omics data, see datX and datY. 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.

3. Combine the grinn network to the correlation network.

1	fetchGrinnCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datY, corrCoef, pval, method)

`txtInput`	list of keywords containing keyword ids e.g. txtInput = list('id1', 'id2'). The keyword ids are from the specified database, see `dbXref`. Default is grinn id e.g. G371.
`from`	string of start node. It can be one of "metabolite","protein","gene","pathway".
`to`	string of end node. It can be one of "metabolite","protein","gene","pathway".
`filterSource`	string or list of pathway databases. The argument is required, if `from` or `to = "pathway"`, see `from` and `to`. The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").
`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
`dbXref`	string of database name. Specify the database name used for the txtInput ids, see `txtInput`. It can be one of "grinn","chebi","kegg","pubchem","inchi","hmdb","smpdb","reactome","uniprot","ensembl","entrezgene". Default is "grinn". If pubchem is used, it has to be pubchem SID (substance ID).
`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`.

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.

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

Kwanjeera W kwanich@ucdavis.edu

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

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

# Create metabolite-gene network from the list of metabolites using grinn ids and combine the grinn network to a correlation network of metabolites
kw <- c('G160','G300','G371')
dummy <- rbind(nodetype=rep("metabolite"),t(mtcars))
colnames(dummy) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 28)))
result <- fetchGrinnCorrNetwork(txtInput=kw, from="metabolite", to="gene", datX=dummy, corrCoef=0.7, pval=1e-10, method="spearman")
library(igraph)
plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to 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 <- fetchGrinnCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX=dummyX, datY=dummyY, corrCoef=0.7, pval=1e-4, method="spearman")