Grinn

fetchCorrGrinnNetwork

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

INPUT

The table summarizes important arguments

Argument	Value	Description
datX	data frame containing normalized-omic data	Columns correspond to entities e.g. genes and rows to samples e.g. normals, tumors. Require 'nodetype' at the first row to indicate the type of entities in each column.
datY	data frame containing normalized-omic data	It uses the same format as datX. Or it can be NULL, if there is only one omic dataset
corrCoef	numerical value from 0-1	The minimum absolute correlations to include edges in the output
pval	numerical value	The maximum pvalues, 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 xTo
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 correlation network of metabolites and genes and expand to the grinn network of metabolite-protein and gene-pathway


#1. load metabolomics data from a csv file
datMet = read.csv("Lung_MET.csv", header=TRUE, row.names=1, stringsAsFactors=FALSE)
#2. use only a subset of metabolomics data
datMet = datMet[c(1:7,42:50),]
#3. show dimensions of metabolomics data
dim(datMet)
#4. 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 ----------#

#5. load transcriptomics data from a csv file
datGene = read.csv("Lung_GENE.csv", header=TRUE, row.names=1, stringsAsFactors=FALSE)
#6. show dimensions of phenotypic data
dim(datGene)
#7. show the first 10 rows and 10 columns
datGene[1:10,1:10]
#8. execute function
result <- fetchCorrGrinnNetwork(datX=datMet, datY=datGene, corrCoef=0.7, pval=1e-6, method="spearman", returnAs="tab", xTo="protein", yTo="pathway")
#display the first 10 edgelists
result$edges[1:10,]

EXPORT OUTPUT

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


write.table(as.matrix(result$edges),"corrNwEdge.txt",sep="\t",row.names = F, quote = FALSE)
write.table(as.matrix(result$nodes),"corrNwNode.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 corrNw.cys.

Diagram legend

References

Metabolomics and transcriptomics 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.
Zakaria N, et al. Human non-small cell lung cancer expresses putative cancer stem cell markers and exhibits the transcriptomic profile of multipotent cells. BMC Cancer 2015;15:84.