R/call_enr.R
In IOR-Bioinformatics/PCSF: Network-based interpretation of highthroughput data
#' Internal function \code{call_enr}
#'
#' This function is internally used to perform enrichment analysis employing ENRICHR API.
#'
#' @keywords internal
#'
#' @author Murodzhon Akhmedov
#'
#' @param clusters A subnetwork clustered using edge betweenness algorithm of \pkg{igraph} package.
#' @param mode A binary variable to choose the method for enrichment analysis, where 0 is for EnrichR API and 1 is for \pkg{topGO} package.
#' @param gene_universe A complete list of genes (vector of gene symbols) used as background in enrichment analysis by \pkg{topGO} package.
call_enr <- function(clusters, mode=0, gene_universe){
if( mode == 0){
# Enrichment analysis is performed with ENRICHR
ENRICHR_ADDLIST = 'http://amp.pharm.mssm.edu/Enrichr/addList'
ENRICHR_EXPORT = 'http://amp.pharm.mssm.edu/Enrichr/export'
# The list of databases to be checked in Enrichment Analysis
database = c("GO_Biological_Process_2015","KEGG_2016", "Reactome_2016", "BioCarta_2016")
# Enrichment results
enrichment_result = as.list(1:length(clusters))
enrichment_result_complete = as.list(1:length(clusters))
# Perform Enrichment Analysis for each cluster in the forest
for( a in 1:length(clusters)){
# List of genes to be regusted for enrichment via ENRICHR API
genes = clusters[[a]]
request = list(list = paste(genes, collapse = "\n"))
complete_request = POST(ENRICHR_ADDLIST, body = request)
output =content(complete_request, "text", encoding = "ISO-8859-1")
userListID = strsplit(strsplit(output, "\n")[[1]][3], ": ")[[1]][2]
response_collection=NULL
# Request enrichment for each database and comnine them all
for( b in 1:length(database)){
# Gather an EXPORT URL and the Response
url = paste0(ENRICHR_EXPORT, "?userListId=",userListID, "&backgroundType=", database[b])
response = GET(url)
response = content(response, "text", encoding = "ISO-8859-1")
response = strsplit(response, "\n")[[1]]
# if(length(unlist(response ))==1)
# next
response = lapply(response, function(x){sp = strsplit(x, "\t")[[1]]; return (sp)})
# If the response contains some elements then combine it
if(length(response)>1){
x = length(response)-1
m_resp = as.data.frame(matrix(0, nrow = x, ncol = length(response[[1]])))
colnames(m_resp) = response[[1]]
for(i in 1:x){
m_resp[i,] = response[[i+1]]
}
response_collection = rbind(response_collection,m_resp)
}
}
if(is.null(response_collection))
next
# Reorder the enrichment according to the "Adjusted P-value" and select the top 15 enrichments
ordered_resp = data.frame(response_collection$`Term`, response_collection$`Adjusted P-value`, response_collection$`Combined Score`)
ordered_resp = ordered_resp[order(ordered_resp[,2]),][1:15,]
ordered_resp[,2] = signif(as.numeric(as.character(ordered_resp[,2])), 3)
ordered_resp[,3] = signif(as.numeric(as.character(ordered_resp[,3])), 3)
# Convert the enrichment table into HTML format in order to display it
enrich = "<!DOCTYPE html> <html> <head> <style>
table {font-family: arial, sans-serif; font-size: 10px; border-collapse: collapse;width: 100%;} td,
th { border: 1px solid #dddddd; text-align: center; padding: 5px;}
tr:nth-child(even) {background-color: #dddddd;}
</style> </head> <body>
<table> <tr> <th>Term</th> <th>Adjusted P-value</th> <th>Combined Score</th> </tr>";
for(i in 1:nrow(ordered_resp)){
enrich = paste0(enrich, " <tr>")
for(j in 1:ncol(ordered_resp)){
enrich = paste0(enrich, "<td>",ordered_resp[i,j], "</td>")
}
enrich = paste0(enrich, "</tr> ")
}
enrich = paste0(enrich, "</table> </body> </html>")
# Attach the Enrichment Analysis for the current cluster
enrichment_result[[a]] = enrich
enrichment_result_complete[[a]] = response_collection
}
} else{
# Enrichment analysis is performed by topGO
# Enrichment results
enrichment_result = as.list(1:length(clusters))
enrichment_result_complete = as.list(1:length(clusters))
# Perform Enrichment Analysis for each cluster in the forest
for( a in 1:length(clusters)){
# List of genes to be regusted for enrichment by topGO
genes = clusters[[a]]
fg <- factor(as.integer(gene_universe %in% genes))
names(fg) <- gene_universe
tgData <- new("topGOdata", description = "simple_session", ontology = "BP",
allGenes=fg, nodeSize=15, annot=annFUN.org, mapping = "org.Hs.eg.db", ID = "symbol")
resultFisher <- runTest(tgData, algorithm = "classic", statistic = "fisher")
resultKS <- runTest(tgData, algorithm = "classic", statistic = "ks")
res_table_top15 <- GenTable(tgData, classicFisher = resultFisher,
classicKS = resultKS,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = 15)
res_table_top1000 <- GenTable(tgData, classicFisher = resultFisher,
classicKS = resultKS,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = 1000)
# Reorder the enrichment according to the "Adjusted P-value" and select the top 15 enrichments
#ordered_resp[,2] = signif(as.numeric(as.character(ordered_resp[,2])), 3)
#ordered_resp[,3] = signif(as.numeric(as.character(ordered_resp[,3])), 3)
# Convert the enrichment table into HTML format in order to display it
enrich = "<!DOCTYPE html> <html> <head> <style>
table {font-family: arial, sans-serif; font-size: 10px; border-collapse: collapse;width: 100%;} td,
th { border: 1px solid #dddddd; text-align: center; padding: 5px;}
tr:nth-child(even) {background-color: #dddddd;}
</style> </head> <body>
<table> <tr> <th>GO.ID</th> <th>Term</th> <th>Annotated</th> <th>Significant</th> <th>Expected</th> <th>classicFisher</th> <th>classicKS</th> </tr>";
for(i in 1:nrow(res_table_top15)){
enrich = paste0(enrich, " <tr>")
for(j in 1:ncol(res_table_top15)){
enrich = paste0(enrich, "<td>",res_table_top15[i,j], "</td>")
}
enrich = paste0(enrich, "</tr> ")
}
enrich = paste0(enrich, "</table> </body> </html>")
# Attach the Enrichment Analysis for the current cluster
enrichment_result[[a]] = enrich
enrichment_result_complete[[a]] = res_table_top1000
}
}
return (list(enrichment_result, enrichment_result_complete))
}
IOR-Bioinformatics/PCSF documentation built on June 2, 2019, 10:03 p.m.
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#' Internal function \code{call_enr}
#'
#' This function is internally used to perform enrichment analysis employing ENRICHR API.
#'
#' @keywords internal
#'
#' @author Murodzhon Akhmedov
#'
#' @param clusters A subnetwork clustered using edge betweenness algorithm of \pkg{igraph} package.
#' @param mode A binary variable to choose the method for enrichment analysis, where 0 is for EnrichR API and 1 is for \pkg{topGO} package.
#' @param gene_universe A complete list of genes (vector of gene symbols) used as background in enrichment analysis by \pkg{topGO} package.
call_enr <- function(clusters, mode=0, gene_universe){
if( mode == 0){
# Enrichment analysis is performed with ENRICHR
ENRICHR_ADDLIST = 'http://amp.pharm.mssm.edu/Enrichr/addList'
ENRICHR_EXPORT = 'http://amp.pharm.mssm.edu/Enrichr/export'
# The list of databases to be checked in Enrichment Analysis
database = c("GO_Biological_Process_2015","KEGG_2016", "Reactome_2016", "BioCarta_2016")
# Enrichment results
enrichment_result = as.list(1:length(clusters))
enrichment_result_complete = as.list(1:length(clusters))
# Perform Enrichment Analysis for each cluster in the forest
for( a in 1:length(clusters)){
# List of genes to be regusted for enrichment via ENRICHR API
genes = clusters[[a]]
request = list(list = paste(genes, collapse = "\n"))
complete_request = POST(ENRICHR_ADDLIST, body = request)
output =content(complete_request, "text", encoding = "ISO-8859-1")
userListID = strsplit(strsplit(output, "\n")[[1]][3], ": ")[[1]][2]
response_collection=NULL
# Request enrichment for each database and comnine them all
for( b in 1:length(database)){
# Gather an EXPORT URL and the Response
url = paste0(ENRICHR_EXPORT, "?userListId=",userListID, "&backgroundType=", database[b])
response = GET(url)
response = content(response, "text", encoding = "ISO-8859-1")
response = strsplit(response, "\n")[[1]]
# if(length(unlist(response ))==1)
# next
response = lapply(response, function(x){sp = strsplit(x, "\t")[[1]]; return (sp)})
# If the response contains some elements then combine it
if(length(response)>1){
x = length(response)-1
m_resp = as.data.frame(matrix(0, nrow = x, ncol = length(response[[1]])))
colnames(m_resp) = response[[1]]
for(i in 1:x){
m_resp[i,] = response[[i+1]]
}
response_collection = rbind(response_collection,m_resp)
}
}
if(is.null(response_collection))
next
# Reorder the enrichment according to the "Adjusted P-value" and select the top 15 enrichments
ordered_resp = data.frame(response_collection$`Term`, response_collection$`Adjusted P-value`, response_collection$`Combined Score`)
ordered_resp = ordered_resp[order(ordered_resp[,2]),][1:15,]
ordered_resp[,2] = signif(as.numeric(as.character(ordered_resp[,2])), 3)
ordered_resp[,3] = signif(as.numeric(as.character(ordered_resp[,3])), 3)
# Convert the enrichment table into HTML format in order to display it
enrich = "<!DOCTYPE html> <html> <head> <style>
table {font-family: arial, sans-serif; font-size: 10px; border-collapse: collapse;width: 100%;} td,
th { border: 1px solid #dddddd; text-align: center; padding: 5px;}
tr:nth-child(even) {background-color: #dddddd;}
</style> </head> <body>
<table> <tr> <th>Term</th> <th>Adjusted P-value</th> <th>Combined Score</th> </tr>";
for(i in 1:nrow(ordered_resp)){
enrich = paste0(enrich, " <tr>")
for(j in 1:ncol(ordered_resp)){
enrich = paste0(enrich, "<td>",ordered_resp[i,j], "</td>")
}
enrich = paste0(enrich, "</tr> ")
}
enrich = paste0(enrich, "</table> </body> </html>")
# Attach the Enrichment Analysis for the current cluster
enrichment_result[[a]] = enrich
enrichment_result_complete[[a]] = response_collection
}
} else{
# Enrichment analysis is performed by topGO
# Enrichment results
enrichment_result = as.list(1:length(clusters))
enrichment_result_complete = as.list(1:length(clusters))
# Perform Enrichment Analysis for each cluster in the forest
for( a in 1:length(clusters)){
# List of genes to be regusted for enrichment by topGO
genes = clusters[[a]]
fg <- factor(as.integer(gene_universe %in% genes))
names(fg) <- gene_universe
tgData <- new("topGOdata", description = "simple_session", ontology = "BP",
allGenes=fg, nodeSize=15, annot=annFUN.org, mapping = "org.Hs.eg.db", ID = "symbol")
resultFisher <- runTest(tgData, algorithm = "classic", statistic = "fisher")
resultKS <- runTest(tgData, algorithm = "classic", statistic = "ks")
res_table_top15 <- GenTable(tgData, classicFisher = resultFisher,
classicKS = resultKS,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = 15)
res_table_top1000 <- GenTable(tgData, classicFisher = resultFisher,
classicKS = resultKS,
orderBy = "classicFisher", ranksOf = "classicFisher", topNodes = 1000)
# Reorder the enrichment according to the "Adjusted P-value" and select the top 15 enrichments
#ordered_resp[,2] = signif(as.numeric(as.character(ordered_resp[,2])), 3)
#ordered_resp[,3] = signif(as.numeric(as.character(ordered_resp[,3])), 3)
# Convert the enrichment table into HTML format in order to display it
enrich = "<!DOCTYPE html> <html> <head> <style>
table {font-family: arial, sans-serif; font-size: 10px; border-collapse: collapse;width: 100%;} td,
th { border: 1px solid #dddddd; text-align: center; padding: 5px;}
tr:nth-child(even) {background-color: #dddddd;}
</style> </head> <body>
<table> <tr> <th>GO.ID</th> <th>Term</th> <th>Annotated</th> <th>Significant</th> <th>Expected</th> <th>classicFisher</th> <th>classicKS</th> </tr>";
for(i in 1:nrow(res_table_top15)){
enrich = paste0(enrich, " <tr>")
for(j in 1:ncol(res_table_top15)){
enrich = paste0(enrich, "<td>",res_table_top15[i,j], "</td>")
}
enrich = paste0(enrich, "</tr> ")
}
enrich = paste0(enrich, "</table> </body> </html>")
# Attach the Enrichment Analysis for the current cluster
enrichment_result[[a]] = enrich
enrichment_result_complete[[a]] = res_table_top1000
}
}
return (list(enrichment_result, enrichment_result_complete))
}
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