R/fetchGrinnModuNetwork.R
In kwanjeeraw/grinn: graph database and R package for omics data integration
Defines functions
fetchGrinnModuNetwork
Documented in
fetchGrinnModuNetwork
#'Combine a grinn network queried from Grinn internal database to a network module correlated to a phenotypic feature
#'@description from the list of keywords, input omics data e.g. normalized expression data or metabolomics data, and phenotypic 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. Identify correlation between the input omics data e.g. normalized gene expression data, and phenotypic data e.g. weight.
#'The function wraps around important aspects of \pkg{WGCNA} including \code{blockwiseModules}, \code{cor}, \code{corPvalueStudent}, \code{labeledHeatmap}.
#'These aspects automatically perform correlation network construction, module detection, and display module-phenotype correlations.
#'A module or the combination of modules can be selected from the heatmap of module-phenotype correlations for including in the network output, see more details below.
#'
#'3. Combine the grinn network to the network module.
#'@usage fetchGrinnModuNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datPheno, sfPower, minModuleSize, threshold)
#'@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.
#'@param datPheno data frame containing phenotypic data e.g. weight, age, insulin sensitivity. Columns correspond to phenotypes and rows to samples e.g. normals, tumors.
#'@param sfPower numerical value of soft-thresholding power for correlation network construction. It is automatically estimated using \code{pickSoftThreshold}, or it can be defined by users.
#'@param minModuleSize numerical value of minimum module size for module detection.
#'@param threshold numerical value to define the minimum value of similarity threshold, from 0 to 1, to include edges in the output.
#'@details
#'The function encapsulates several methods from \pkg{WGCNA} so that module-phenoty correlation analysis can be fasten. These methods include:
#'
#'- \code{pickSoftThreshold} estimates soft-thresholding powers from scale free topology to build the correlation network.
#'
#'- \code{blockwiseModules} automatically calculates a correlation network and detects modules.
#'Modules are the areas of the network where nodes are densely connected based on their topological overlap measure, see \pkg{WGCNA} for more details.
#'Each module is labeled by color. By using the color, a module or the combination of modules can be selected ("enter color to the terminal"), for including in the network output.
#'
#'- Module-phenotype correlations and significances 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.
#'
#'- \code{labeledHeatmap} plots a heatmap of module-phenotype correlations. A row is a module and a column is a phynotype.
#'Each cell presents the corresponding correlation and the pvalue inside parenthesis. Each cell is colored by correlation, red means positive and blue means negative correlation.
#'
#'- \code{exportNetworkToCytoscape} exports a network for using in Cytoscape (\url{http://cytoscape.org/}).
#'The selected module is exported as the network output in which an edge will be included if it similarity threshold above the cutoff, see \code{threshold}.
#'@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{pickSoftThreshold}}, \code{\link{blockwiseModules}}, \code{\link{labeledHeatmap}},
#'\code{\link{exportNetworkToCytoscape}}, \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 of metabolite module to phenotypic data
#'kw <- c('G160','G300','G371','G16414','G17191')
#'library(grinn)
#'data(dummy)
#'data(dummyPheno)
#'result <- fetchGrinnModuNetwork(txtInput=kw, from="metabolite", to="gene", datX=dummy, datPheno=dummyPheno, minModuleSize=5, threshold=0.2)
#'# enter module color(s) seperate by space:yellow brown purple
#'library(igraph)
#'plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
fetchGrinnModuNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX, datPheno, sfPower=NULL, minModuleSize = 10, threshold = 0.5){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,returnAs=returnAs,dbXref=dbXref)
modulenw = fetchWGCNAModule(datX=datX, datPheno=datPheno, sfPower=sfPower, minModuleSize=minModuleSize, threshold=threshold, returnAs=returnAs)
if(nrow(modulenw$nodes)>0){
#collect node info
moduattb = data.frame()
moduattb = plyr::ldply (apply(modulenw$nodes, MARGIN = 1, FUN=getModuleInfo, x = "id", y = "nodetype")) #format nodelist
modulenw$edges$source = lapply(modulenw$edges$source, FUN=formatId, y = moduattb) #format edgelist
modulenw$edges$target = lapply(modulenw$edges$target, FUN=formatId, y = moduattb) #format edgelist
}
if(nrow(basicnw$nodes)>0 && nrow(modulenw$nodes)>0){
cat("Formating and returning combined network ...\n")
basicnw$edges$corr_coef = 1
basicnw$nodes$modulecolor = ""
modulenw$edges$relsource = ""
modulenw$nodes$xref = ""
modulenw$nodes$gid = modulenw$nodes$id #same ids
pair = rbind(basicnw$edges,modulenw$edges)
if(nrow(moduattb)>0){attb = rbind(basicnw$nodes,moduattb,modulenw$nodes)}else{attb = rbind(basicnw$nodes,modulenw$nodes)}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)>0 && nrow(modulenw$nodes)==0){
cat("Formating and returning combined network ...\n")
pair = basicnw$edges
attb = basicnw$nodes
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)==0 && nrow(modulenw$nodes)>0){
cat("Formating and returning combined network ...\n")
pair = modulenw$edges
modulenw$nodes$xref = ""
modulenw$nodes$gid = modulenw$nodes$id #same ids
if(nrow(moduattb)>0){attb = rbind(moduattb,modulenw$nodes)}else{attb = modulenw$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 fetchGrinnModuNetwork
Documented in fetchGrinnModuNetwork
#'Combine a grinn network queried from Grinn internal database to a network module correlated to a phenotypic feature
#'@description from the list of keywords, input omics data e.g. normalized expression data or metabolomics data, and phenotypic 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. Identify correlation between the input omics data e.g. normalized gene expression data, and phenotypic data e.g. weight.
#'The function wraps around important aspects of \pkg{WGCNA} including \code{blockwiseModules}, \code{cor}, \code{corPvalueStudent}, \code{labeledHeatmap}.
#'These aspects automatically perform correlation network construction, module detection, and display module-phenotype correlations.
#'A module or the combination of modules can be selected from the heatmap of module-phenotype correlations for including in the network output, see more details below.
#'
#'3. Combine the grinn network to the network module.
#'@usage fetchGrinnModuNetwork(txtInput, from, to, filterSource, returnAs, dbXref, datX, datPheno, sfPower, minModuleSize, threshold)
#'@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.
#'@param datPheno data frame containing phenotypic data e.g. weight, age, insulin sensitivity. Columns correspond to phenotypes and rows to samples e.g. normals, tumors.
#'@param sfPower numerical value of soft-thresholding power for correlation network construction. It is automatically estimated using \code{pickSoftThreshold}, or it can be defined by users.
#'@param minModuleSize numerical value of minimum module size for module detection.
#'@param threshold numerical value to define the minimum value of similarity threshold, from 0 to 1, to include edges in the output.
#'@details
#'The function encapsulates several methods from \pkg{WGCNA} so that module-phenoty correlation analysis can be fasten. These methods include:
#'
#'- \code{pickSoftThreshold} estimates soft-thresholding powers from scale free topology to build the correlation network.
#'
#'- \code{blockwiseModules} automatically calculates a correlation network and detects modules.
#'Modules are the areas of the network where nodes are densely connected based on their topological overlap measure, see \pkg{WGCNA} for more details.
#'Each module is labeled by color. By using the color, a module or the combination of modules can be selected ("enter color to the terminal"), for including in the network output.
#'
#'- Module-phenotype correlations and significances 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.
#'
#'- \code{labeledHeatmap} plots a heatmap of module-phenotype correlations. A row is a module and a column is a phynotype.
#'Each cell presents the corresponding correlation and the pvalue inside parenthesis. Each cell is colored by correlation, red means positive and blue means negative correlation.
#'
#'- \code{exportNetworkToCytoscape} exports a network for using in Cytoscape (\url{http://cytoscape.org/}).
#'The selected module is exported as the network output in which an edge will be included if it similarity threshold above the cutoff, see \code{threshold}.
#'@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{pickSoftThreshold}}, \code{\link{blockwiseModules}}, \code{\link{labeledHeatmap}},
#'\code{\link{exportNetworkToCytoscape}}, \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 of metabolite module to phenotypic data
#'kw <- c('G160','G300','G371','G16414','G17191')
#'library(grinn)
#'data(dummy)
#'data(dummyPheno)
#'result <- fetchGrinnModuNetwork(txtInput=kw, from="metabolite", to="gene", datX=dummy, datPheno=dummyPheno, minModuleSize=5, threshold=0.2)
#'# enter module color(s) seperate by space:yellow brown purple
#'library(igraph)
#'plot(graph.data.frame(result$edges[,1:2], directed=FALSE))
fetchGrinnModuNetwork <- function(txtInput, from, to, filterSource=list(), returnAs="tab", dbXref="grinn", datX, datPheno, sfPower=NULL, minModuleSize = 10, threshold = 0.5){
basicnw = fetchGrinnNetwork(txtInput=txtInput,from=from,to=to,filterSource=filterSource,returnAs=returnAs,dbXref=dbXref)
modulenw = fetchWGCNAModule(datX=datX, datPheno=datPheno, sfPower=sfPower, minModuleSize=minModuleSize, threshold=threshold, returnAs=returnAs)
if(nrow(modulenw$nodes)>0){
#collect node info
moduattb = data.frame()
moduattb = plyr::ldply (apply(modulenw$nodes, MARGIN = 1, FUN=getModuleInfo, x = "id", y = "nodetype")) #format nodelist
modulenw$edges$source = lapply(modulenw$edges$source, FUN=formatId, y = moduattb) #format edgelist
modulenw$edges$target = lapply(modulenw$edges$target, FUN=formatId, y = moduattb) #format edgelist
}
if(nrow(basicnw$nodes)>0 && nrow(modulenw$nodes)>0){
cat("Formating and returning combined network ...\n")
basicnw$edges$corr_coef = 1
basicnw$nodes$modulecolor = ""
modulenw$edges$relsource = ""
modulenw$nodes$xref = ""
modulenw$nodes$gid = modulenw$nodes$id #same ids
pair = rbind(basicnw$edges,modulenw$edges)
if(nrow(moduattb)>0){attb = rbind(basicnw$nodes,moduattb,modulenw$nodes)}else{attb = rbind(basicnw$nodes,modulenw$nodes)}
attb = attb[!duplicated(attb[,2]),]
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)>0 && nrow(modulenw$nodes)==0){
cat("Formating and returning combined network ...\n")
pair = basicnw$edges
attb = basicnw$nodes
cat("Found ",nrow(pair)," relationships...\n")
}else if(nrow(basicnw$nodes)==0 && nrow(modulenw$nodes)>0){
cat("Formating and returning combined network ...\n")
pair = modulenw$edges
modulenw$nodes$xref = ""
modulenw$nodes$gid = modulenw$nodes$id #same ids
if(nrow(moduattb)>0){attb = rbind(moduattb,modulenw$nodes)}else{attb = modulenw$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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