grinn R package

fetchDiffCorrGrinnNetwork

This example shows how to compute a differential correlation network and expand the network with information from Grinn internal database

INPUT

The table summarizes important arguments

Argument	Value	Description
datX1	data frame containing normalized-omic data of one condition	Columns correspond to entities e.g. genes and rows to samples. Require 'nodetype' at the first row to indicate the type of entities in each column.
datX2	data frame containing normalized-omic data of another condition	It uses the same format as datX1.
datY1	data frame containing normalized-omic data of one condition	It uses the same format as datX1. Or it can be NULL, if there is only one omic data type
datY2	data frame containing normalized-omic data of another condition	It uses the same format as datX1. Or it can be NULL, if there is only one omic data type
pDiff	numerical value	The maximum pvalues (pvalDiff), to include edges in the output
xTo	entity type e.g. metabolite	The correlation network can be expanded from datX entites to a specific entity type, by providing a value to sourceTo
yTo	entity type e.g. gene	The correlation network can be expanded from datY entites (if not NULL) to a specific entity type, by providing a value to yTo

EXECUTE FUNCTION

Compute a differential correlation network of metabolites in normal and cancer tissue and expand to the grinn network of metabolite-gene


#1. load metabolomics data from a csv file
datMet = read.csv("Lung_MET.csv", header=TRUE, row.names=1, stringsAsFactors=FALSE)
#2. show dimensions of metabolomics data
dim(datMet)
#3. show the first 10 rows and 10 columns
datMet[1:10,1:10]

#!!--- NOTE: The following codes convert kegg ids to grinn ids. If the input data is already the grinn ids, these steps can be skipped.
grinnID = convertToGrinnID(txtInput=colnames(datMet), nodetype="metabolite", dbXref="kegg") #call grinn function to convert ids
grinnID = grinnID[!duplicated(grinnID[,1]),] #keep the first mapped id
colnames(datMet) = lapply(colnames(datMet),function(x) ifelse(length(which(grinnID$FROM_kegg == x))>0,as.character(grinnID$GRINNID[which(grinnID$FROM_kegg == x)]),x))
#---------- END id conversion ----------#

datMetNormal = datMet[1:40,] #normal condition
datMetTumor = rbind(nodetype=rep("metabolite"),datMet[41:79,]) #tumor condition
result <- fetchDiffCorrGrinnNetwork(datX1=datMetNormal, datX2=datMetTumor, pDiff=1e-4, method="spearman", returnAs="tab", xTo="gene")
#display the first 10 edgelists
result$edges[1:10,]
datMet2 = datMet[c(1:7,42:50),]
datMetNormal = datMet2[1:7,]
datMetTumor = rbind(nodetype=rep("metabolite"),datMet2[8:13,])
datGeneNormal = datGene[1:7,]
datGeneTumor = rbind(nodetype=rep("gene"),datGene[8:13,])
result <- fetchDiffCorrGrinnNetwork(datX1=datMetNormal, datX2=datMetTumor, datY1=datGeneNormal, datY2=datGeneTumor, pDiff=1e-5, method="spearman", returnAs="tab", xTo="protein", yTo="pathway")

EXPORT OUTPUT

Export the network as tab-delimited files to visualize in Cytoscape


write.table(as.matrix(result$edges),"diffcorrNwEdge.txt",sep="\t",row.names = F, quote = FALSE)
write.table(as.matrix(result$nodes),"diffcorrNwNode.txt",sep="\t",row.names = F, quote = FALSE)

VISUALIZATION
The figure is generated by Cytoscape 3.1.1 using grinn style (grinn.xml). It is corresponding to the cytoscape file diffcorrNw.cys.

Diagram legend

References

Correlation-based analysis apply the Fisher's z-test method from the following publication:

Fukushima A DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 2013;10;518(1):209-14.

Metabolomics data used in this example are taken from the following publication:

Wikoff WR, et al. Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prev Res (Phila) 2015;8(5):410-8.