R/fetchGrinnDiffCorrNetwork.R
In kwanjeeraw/grinn: graph database and R package for omics data integration
Defines functions
fetchGrinnDiffCorrNetwork
Documented in
fetchGrinnDiffCorrNetwork
#'Combine a grinn network queried from grinn internal database to a differential 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.
#'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 differential correlation network of input omics data from two conditions, see \code{datX1}, \code{datX2}, \code{datY1}, \code{datY2}.
#'Correlation coefficients, pvalues and relation directions among entities in each condition 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.
#'Then correlation coefficients are test for differential correlations using Fisher's z-test based on \pkg{DiffCorr}.
#'The differential correlation network is created by function \code{fetchDiffCorrNetwork}.
#'
#'3. Combine the grinn network to the correlation network.
#'@usage fetchGrinnDiffCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX1, datX2, datY1, datY2, pDiff, 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 datX1 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition.
#'Columns correspond to entities e.g. genes, metabolites, and rows to samples.
#'Require 'nodetype' at the first row to indicate the type of entities in each column. See below for details.
#'@param datX2 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition.
#'Use the same format as \code{datX1}.
#'@param datY1 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition.
#'Use the same format as \code{datX1}. If there is only one type of dataset, it can be NULL. See below for details.
#'@param datY2 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition.
#'Use the same format as \code{datX1}. If there is only one type of dataset, it can be NULL. See below for details.
#'@param pDiff numerical value to define the maximum value of pvalues (pvalDiff), 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", see \code{\link{cor}}.
#'@details To calculate the differential correlation network, require the input data from two conditions; 1 and 2.
#'The input data are matrices in which rows are samples and columns are entities.
#'For each condition, if datNormY is given, then the correlations between the columns of datNormX and the columns of datNormY are computed.
#'Otherwise if datNormY is not given, the correlations of the columns of datNormX are computed. Then correlation coefficients are test for significant correlation pairs.
#'The column names of the input data are required to use grinn ids. \code{convertToGrinnID} is provided for id conversion, see \code{\link{convertToGrinnID}}.
#'@return list of nodes and edges. The list is with the following componens: edges and nodes. Output includes correlation coefficients, pvalues and relation directions of each conditions,
#'and the pvalues (pvalDiff) after testing. 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}
#'@references Fukushima A. (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene, 10;518(1):209-14.
#'@export
#'@seealso \code{\link{cor}}, \code{\link{corPvalueStudent}}, \code{link{fetchDiffCorrNetwork}}, \code{\link{fetchGrinnNetwork}}, \pkg{\link{DiffCorr}}, \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 differential correlation network of metabolites
#'kw <- c('G160','G300','G371')
#'dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,])
#'colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
#'rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,])
#'colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
#'rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
#'result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="gene", datX1=dummyX1, datX2=dummyX2, pDiff=0.05)
#'library(igraph)
#'plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
#'# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to a differential correlation network of metabolites and proteins
#'dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,1:5])
#'colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1','G16962')
#'rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,1:5])
#'colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1','G16962')
#'rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
#'dummyY1 <- rbind(nodetype=rep("protein"),mtcars[1:16,6:10])
#'colnames(dummyY1) <- c('P28845','P08235','Q08AG9','P80365','P15538')
#'rownames(dummyY1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyY2 <- rbind(nodetype=rep("protein"),mtcars[17:32,6:10])
#'colnames(dummyY2) <- c('P28845','P08235','Q08AG9','P80365','P15538')
#'rownames(dummyY2)[-1] <- paste0(rep("cancer_"),1:16)
#'result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX1=dummyX1, datX2=dummyX2, datY1=dummyY1, datY2=dummyY2, pDiff=0.05)
fetchGrinnDiffCorrNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX1=datX1, datX2=datX2, datY1=NULL, datY2=NULL, pDiff=1e-4, method="spearman"){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,returnAs=returnAs,dbXref=dbXref)
corrnw = fetchDiffCorrNetwork(datX1=datX1,datY1=datY1,datX2=datX2,datY2=datY2,pDiff=pDiff,method=method,returnAs=returnAs)
if(nrow(corrnw$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$direction = 0
basicnw$edges$pvalDiff = 0
basicnw$edges$condition = ""
corrnw$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 fetchGrinnDiffCorrNetwork
Documented in fetchGrinnDiffCorrNetwork
#'Combine a grinn network queried from grinn internal database to a differential 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.
#'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 differential correlation network of input omics data from two conditions, see \code{datX1}, \code{datX2}, \code{datY1}, \code{datY2}.
#'Correlation coefficients, pvalues and relation directions among entities in each condition 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.
#'Then correlation coefficients are test for differential correlations using Fisher's z-test based on \pkg{DiffCorr}.
#'The differential correlation network is created by function \code{fetchDiffCorrNetwork}.
#'
#'3. Combine the grinn network to the correlation network.
#'@usage fetchGrinnDiffCorrNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX1, datX2, datY1, datY2, pDiff, 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 datX1 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition.
#'Columns correspond to entities e.g. genes, metabolites, and rows to samples.
#'Require 'nodetype' at the first row to indicate the type of entities in each column. See below for details.
#'@param datX2 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition.
#'Use the same format as \code{datX1}.
#'@param datY1 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of one condition.
#'Use the same format as \code{datX1}. If there is only one type of dataset, it can be NULL. See below for details.
#'@param datY2 data frame containing normalized, quantified omics data e.g. expression data, metabolite intensities of another condition.
#'Use the same format as \code{datX1}. If there is only one type of dataset, it can be NULL. See below for details.
#'@param pDiff numerical value to define the maximum value of pvalues (pvalDiff), 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", see \code{\link{cor}}.
#'@details To calculate the differential correlation network, require the input data from two conditions; 1 and 2.
#'The input data are matrices in which rows are samples and columns are entities.
#'For each condition, if datNormY is given, then the correlations between the columns of datNormX and the columns of datNormY are computed.
#'Otherwise if datNormY is not given, the correlations of the columns of datNormX are computed. Then correlation coefficients are test for significant correlation pairs.
#'The column names of the input data are required to use grinn ids. \code{convertToGrinnID} is provided for id conversion, see \code{\link{convertToGrinnID}}.
#'@return list of nodes and edges. The list is with the following componens: edges and nodes. Output includes correlation coefficients, pvalues and relation directions of each conditions,
#'and the pvalues (pvalDiff) after testing. 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}
#'@references Fukushima A. (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene, 10;518(1):209-14.
#'@export
#'@seealso \code{\link{cor}}, \code{\link{corPvalueStudent}}, \code{link{fetchDiffCorrNetwork}}, \code{\link{fetchGrinnNetwork}}, \pkg{\link{DiffCorr}}, \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 differential correlation network of metabolites
#'kw <- c('G160','G300','G371')
#'dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,])
#'colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
#'rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,])
#'colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1',letters[1:7])
#'rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
#'result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="gene", datX1=dummyX1, datX2=dummyX2, pDiff=0.05)
#'library(igraph)
#'plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
#'# Create metabolite-pathway network from the list of metabolites using grinn ids and combine the grinn network to a differential correlation network of metabolites and proteins
#'dummyX1 <- rbind(nodetype=rep("metabolite"),mtcars[1:16,1:5])
#'colnames(dummyX1) <- c('G1.1','G27967','G371','G4.1','G16962')
#'rownames(dummyX1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyX2 <- rbind(nodetype=rep("metabolite"),mtcars[17:32,1:5])
#'colnames(dummyX2) <- c('G1.1','G27967','G371','G4.1','G16962')
#'rownames(dummyX2)[-1] <- paste0(rep("cancer_"),1:16)
#'dummyY1 <- rbind(nodetype=rep("protein"),mtcars[1:16,6:10])
#'colnames(dummyY1) <- c('P28845','P08235','Q08AG9','P80365','P15538')
#'rownames(dummyY1)[-1] <- paste0(rep("normal_"),1:16)
#'dummyY2 <- rbind(nodetype=rep("protein"),mtcars[17:32,6:10])
#'colnames(dummyY2) <- c('P28845','P08235','Q08AG9','P80365','P15538')
#'rownames(dummyY2)[-1] <- paste0(rep("cancer_"),1:16)
#'result <- fetchGrinnDiffCorrNetwork(txtInput=kw, from="metabolite", to="pathway", datX1=dummyX1, datX2=dummyX2, datY1=dummyY1, datY2=dummyY2, pDiff=0.05)
fetchGrinnDiffCorrNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX1=datX1, datX2=datX2, datY1=NULL, datY2=NULL, pDiff=1e-4, method="spearman"){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,returnAs=returnAs,dbXref=dbXref)
corrnw = fetchDiffCorrNetwork(datX1=datX1,datY1=datY1,datX2=datX2,datY2=datY2,pDiff=pDiff,method=method,returnAs=returnAs)
if(nrow(corrnw$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$direction = 0
basicnw$edges$pvalDiff = 0
basicnw$edges$condition = ""
corrnw$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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