R/fetchGrinnCorrNetwork.R
In kwanjeeraw/grinn: graph database and R package for omics data integration
Defines functions
fetchGrinnCorrNetwork
Documented in
fetchGrinnCorrNetwork
#'Combine a grinn network queried from Grinn internal database to a weighted correlation network
#'@description 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 \code{\link{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 \code{datX} and \code{datY}.
#'Correlation coefficients, pvalues and relation directions are calculated using WGCNA functions \code{cor} and \code{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.
#'@usage fetchGrinnCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datY, corrCoef, pval, method)
#'@param txtInput list of keywords containing keyword ids e.g. txtInput = list('id1', 'id2').
#'The keyword ids are from the specified database, see \code{dbXref}. Default is grinn id e.g. G371.
#'@param from string of start node. It can be one of "metabolite","protein","gene","pathway".
#'@param to string of end node. It can be one of "metabolite","protein","gene","pathway".
#'@param filterSource string or list of pathway databases. The argument is required, if \code{from} or \code{to = "pathway"}, see \code{from} and \code{to}.
#'The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").
#'@param 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
#'@param dbXref string of database name. Specify the database name used for the txtInput ids, see \code{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).
#'@param 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.
#'@param datY data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities.
#'Use the same format as \code{datX} or it can be NULL. See below for details.
#'@param corrCoef numerical value to define the minimum value of absolute correlation, from 0 to 1, to include edges in the output.
#'@param pval numerical value to define the maximum value of pvalues, to include edges in the output.
#'@param method string to define which correlation is to be used. It can be one of "pearson","kendall","spearman" (default), see \code{\link{cor}}.
#'@details 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.
#'@return list of nodes and edges. The list is with the following componens: edges and nodes. Return empty list if found nothing
#'@author Kwanjeera W \email{kwanich@@ucdavis.edu}
#'@references Langfelder P. and Horvath S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559
#'@references 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
#'@references Langfelder P. and Horvath S. Tutorials for the WGCNA package \url{http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html}
#'@export
#'@seealso \code{\link{cor}}, \code{\link{corPvalueStudent}}, \code{\link{fetchGrinnNetwork}}, \url{http://js.cytoscape.org/}
#'@examples
#'# 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$edges[,1:2], directed=FALSE))
#'# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to a correlation network of metabolites and proteins
#'dummyX <- rbind(nodetype=rep("metabolite"),t(mtcars)[,1:16])
#'colnames(dummyX) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 12)))
#'dummyY <- rbind(nodetype=rep("protein"),t(mtcars)[,17:32])
#'colnames(dummyY) <- c('P28845','P08235','Q08AG9','P80365',paste0('P',sample(10000:80000, 12)))
#'result <- fetchGrinnCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX=dummyX, datY=dummyY, corrCoef=0.7, pval=1e-4, method="spearman")
fetchGrinnCorrNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX, datY=NULL, corrCoef=0.5, pval=1e-9, method="spearman"){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,dbXref=dbXref)
corrnw = fetchCorrNetwork(datX=datX,datY=datY,corrCoef=corrCoef,pval=pval,method=method,returnAs="tab")
if(nrow(corrnw$nodes)>0){
#collect node info
corrattb = data.frame()
corrattb = plyr::ldply (apply(corrnw$nodes, MARGIN = 1, FUN=getNodeInfo, x = "id", y = "nodetype")) #format nodelist
corrnw$edges$source = lapply(corrnw$edges$source, FUN=formatId, y = corrattb) #format edgelist
corrnw$edges$target = lapply(corrnw$edges$target, FUN=formatId, y = corrattb) #format edgelist
}
if(nrow(basicnw$nodes)>0 && nrow(corrnw$nodes)>0){
cat("Formating and returning combined network ...\n")
basicnw$edges$corr_coef = 1
basicnw$edges$pval = 0
basicnw$edges$direction = 0
corrnw$edges$relsource = ""
corrnw$nodes$xref = ""
corrnw$nodes$gid = corrnw$nodes$id #same ids
pair = rbind(basicnw$edges,corrnw$edges)
if(nrow(corrattb)>0){attb = rbind(basicnw$nodes,corrattb,corrnw$nodes)}else{attb = rbind(basicnw$nodes,corrnw$nodes)}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)>0 && nrow(corrnw$nodes)==0){
cat("Formating and returning combined network ...\n")
pair = basicnw$edges
attb = basicnw$nodes
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)==0 && nrow(corrnw$nodes)>0){
cat("Formating and returning combined network ...\n")
pair = corrnw$edges
corrnw$nodes$xref = ""
corrnw$nodes$gid = corrnw$nodes$id #same ids
if(nrow(corrattb)>0){attb = rbind(corrattb,corrnw$nodes)}else{attb = corrnw$nodes}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else{# if no mapped node found
print("Returning no data...")
pair = data.frame()
attb = data.frame()
cynetwork = list(nodes="", edges="")
}
out = switch(returnAs,
tab = list(nodes=attb, edges=pair),
json = list(nodes=jsonlite::toJSON(attb), edges=jsonlite::toJSON(pair)),
cytoscape = createCyNetwork(attb, pair),
stop("incorrect return type"))
}
kwanjeeraw/grinn documentation built on May 20, 2019, 7:07 p.m.
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Defines functions fetchGrinnCorrNetwork
Documented in fetchGrinnCorrNetwork
#'Combine a grinn network queried from Grinn internal database to a weighted correlation network
#'@description 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 \code{\link{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 \code{datX} and \code{datY}.
#'Correlation coefficients, pvalues and relation directions are calculated using WGCNA functions \code{cor} and \code{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.
#'@usage fetchGrinnCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datY, corrCoef, pval, method)
#'@param txtInput list of keywords containing keyword ids e.g. txtInput = list('id1', 'id2').
#'The keyword ids are from the specified database, see \code{dbXref}. Default is grinn id e.g. G371.
#'@param from string of start node. It can be one of "metabolite","protein","gene","pathway".
#'@param to string of end node. It can be one of "metabolite","protein","gene","pathway".
#'@param filterSource string or list of pathway databases. The argument is required, if \code{from} or \code{to = "pathway"}, see \code{from} and \code{to}.
#'The argument value can be any of "SMPDB","KEGG","REACTOME" or combination of them e.g. list("KEGG","REACTOME").
#'@param 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
#'@param dbXref string of database name. Specify the database name used for the txtInput ids, see \code{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).
#'@param 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.
#'@param datY data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities.
#'Use the same format as \code{datX} or it can be NULL. See below for details.
#'@param corrCoef numerical value to define the minimum value of absolute correlation, from 0 to 1, to include edges in the output.
#'@param pval numerical value to define the maximum value of pvalues, to include edges in the output.
#'@param method string to define which correlation is to be used. It can be one of "pearson","kendall","spearman" (default), see \code{\link{cor}}.
#'@details 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.
#'@return list of nodes and edges. The list is with the following componens: edges and nodes. Return empty list if found nothing
#'@author Kwanjeera W \email{kwanich@@ucdavis.edu}
#'@references Langfelder P. and Horvath S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9:559
#'@references 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
#'@references Langfelder P. and Horvath S. Tutorials for the WGCNA package \url{http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html}
#'@export
#'@seealso \code{\link{cor}}, \code{\link{corPvalueStudent}}, \code{\link{fetchGrinnNetwork}}, \url{http://js.cytoscape.org/}
#'@examples
#'# 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$edges[,1:2], directed=FALSE))
#'# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to a correlation network of metabolites and proteins
#'dummyX <- rbind(nodetype=rep("metabolite"),t(mtcars)[,1:16])
#'colnames(dummyX) <- c('G1.1','G27967','G371','G4.1',paste0('G',sample(400:22000, 12)))
#'dummyY <- rbind(nodetype=rep("protein"),t(mtcars)[,17:32])
#'colnames(dummyY) <- c('P28845','P08235','Q08AG9','P80365',paste0('P',sample(10000:80000, 12)))
#'result <- fetchGrinnCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX=dummyX, datY=dummyY, corrCoef=0.7, pval=1e-4, method="spearman")
fetchGrinnCorrNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX, datY=NULL, corrCoef=0.5, pval=1e-9, method="spearman"){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,dbXref=dbXref)
corrnw = fetchCorrNetwork(datX=datX,datY=datY,corrCoef=corrCoef,pval=pval,method=method,returnAs="tab")
if(nrow(corrnw$nodes)>0){
#collect node info
corrattb = data.frame()
corrattb = plyr::ldply (apply(corrnw$nodes, MARGIN = 1, FUN=getNodeInfo, x = "id", y = "nodetype")) #format nodelist
corrnw$edges$source = lapply(corrnw$edges$source, FUN=formatId, y = corrattb) #format edgelist
corrnw$edges$target = lapply(corrnw$edges$target, FUN=formatId, y = corrattb) #format edgelist
}
if(nrow(basicnw$nodes)>0 && nrow(corrnw$nodes)>0){
cat("Formating and returning combined network ...\n")
basicnw$edges$corr_coef = 1
basicnw$edges$pval = 0
basicnw$edges$direction = 0
corrnw$edges$relsource = ""
corrnw$nodes$xref = ""
corrnw$nodes$gid = corrnw$nodes$id #same ids
pair = rbind(basicnw$edges,corrnw$edges)
if(nrow(corrattb)>0){attb = rbind(basicnw$nodes,corrattb,corrnw$nodes)}else{attb = rbind(basicnw$nodes,corrnw$nodes)}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)>0 && nrow(corrnw$nodes)==0){
cat("Formating and returning combined network ...\n")
pair = basicnw$edges
attb = basicnw$nodes
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)==0 && nrow(corrnw$nodes)>0){
cat("Formating and returning combined network ...\n")
pair = corrnw$edges
corrnw$nodes$xref = ""
corrnw$nodes$gid = corrnw$nodes$id #same ids
if(nrow(corrattb)>0){attb = rbind(corrattb,corrnw$nodes)}else{attb = corrnw$nodes}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else{# if no mapped node found
print("Returning no data...")
pair = data.frame()
attb = data.frame()
cynetwork = list(nodes="", edges="")
}
out = switch(returnAs,
tab = list(nodes=attb, edges=pair),
json = list(nodes=jsonlite::toJSON(attb), edges=jsonlite::toJSON(pair)),
cytoscape = createCyNetwork(attb, pair),
stop("incorrect return type"))
}
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