R/cbaf-availableData.R
In armanshahrisa/cbaf: Automated functions for comparing various omic data from cbioportal.org
Defines functions
availableData
Documented in
availableData
#' @title Check which Data types are available for each cancer study.
#'
#' @description This function checks all the cancer studies that are registered
#' in 'cbioportal.org' to examine whether or not they contain RNA-Seq,
#' microRNA-Seq, microarray(mRNA), microarray(miRNA) and methylation data.
#'
#' @details
#' \tabular{lllll}{
#' Package: \tab cbaf \cr
#' Type: \tab Package \cr
#' Version: \tab 1.20.0 \cr
#' Date: \tab 2022-10-24 \cr
#' License: \tab Artistic-2.0 \cr
#' }
#'
#'
#'
#' @importFrom cBioPortalData cBioPortal getStudies sampleLists molecularProfiles
#'
#' @importFrom openxlsx createWorkbook addWorksheet writeData saveWorkbook
#'
#' @importFrom utils head setTxtProgressBar txtProgressBar
#'
#'
#'
#' @usage availableData(excelFileName, oneOfEach = FALSE)
#'
#' @param excelFileName a character string that is required to name the output
#' and, if requested, excel file.
#'
#' @param oneOfEach a character string that is used to alter the function's
#' behavior to select unique one cancer of each type that contains data for the
#' requested technique. The default value is \code{"FALSE"}. Supported
#' techniques include \code{"RNA-Seq"}, \code{"RNA-SeqRTN"},
#' \code{"microRNA-Seq"}, \code{"Microarray.mRNA"}, \code{"Microarray.mRNA"},
#' and \code{"methylation"}.
#'
#'
#' @return An excel file that contains all the cancer studies versus available
#' data types
#'
#'
#'
#' @author Arman Shahrisa, \email{shahrisa.arman@hotmail.com} [maintainer,
#' copyright holder]
#' @author Maryam Tahmasebi Birgani, \email{tahmasebi-ma@ajums.ac.ir}
#'
#' @export
################################################################################
################################################################################
################ Dataset availability in all cBioportal Cancers ################
################################################################################
################################################################################
availableData <- function(excelFileName, oneOfEach = FALSE){
##############################################################################
# Check input parameters
if(!is.character(excelFileName)){
stop("[availableData] 'excelFileName' must be a character string!")
}
supported.techniques <- c("RNA-Seq",
"RNA-SeqRTN",
"microRNA-Seq",
"Microarray.mRNA",
"Microarray.microRNA",
"methylation")
if(! oneOfEach == FALSE){
if(! oneOfEach %in% supported.techniques){
stop("[availableData] 'oneOfEach' must be either 'RNA-Seq', 'RNA-SeqRTN, 'microRNA-Seq', 'microarray.mRNA', 'microarray.microRNA', 'methylation', or simply FALSE !")
} else{
continue <- TRUE
}
}
if(oneOfEach == FALSE){
if(file.exists(paste(excelFileName, ".xlsx", sep = ""))){
message("[availableData] Warning! '", excelFileName, ".xlsx", "' already exists!")
choiceYesNo <- readline(prompt = "[availableData] Overwrite the file? (yes/no): ")
if(choiceYesNo == "yes"){
# Remove the previous file
file.remove(paste(excelFileName, ".xlsx", sep = ""))
continue <- TRUE
}else if(choiceYesNo == "no"){
continue <- FALSE
}else{
stop("[availableData] please type 'yes' or 'no'!")
}
} else{
continue <- TRUE
}
}
if(continue){
############################################################################
# Prerequisites
# Prerequisites for cBioportal
cbio <- cBioPortal()
studies <- getStudies(cbio)
# Converting new format to the old format
colnames(studies)[colnames(studies) == "studyId"] <- "cancer_study_id"
list_of_studies <- studies
#! mycgds = CGDS("http://www.cbioportal.org/")
#! list_of_studies <- getCancerStudies(mycgds)
message("[availableData] Checking all cancer studies")
# create progress bar
pb <- txtProgressBar(min = 0, max = nrow(list_of_studies)*2, style = 3)
i <- 0
############################################################################
## core segment
# looking for supported techniques at level 1
list_of_available_data_L1 <- sapply(
list_of_studies$cancer_study_id, function(cs, cbio_api) {
# Obtain available techniques
samp <- sampleLists(cbio_api, cs)
# Converting new format to the old format
colnames(samp)[colnames(samp) == "sampleListId"] <- "case_list_id"
colnames(samp)[colnames(samp) == "name"] <- "case_list_name"
available_options_1 <- samp
#! available_options_1 <- getCaseLists(cgds, cs)
# Update progressbar
i <<- i + 1
setTxtProgressBar(pb, i)
if(length(available_options_1) > 1){
if(any(colnames(available_options_1) == "case_list_name")){
description <- available_options_1$case_list_name
c(RNA.Seq = as.character(
any(RNA.Seq_L1.terms %in% description)
),
RNA.Seq.RTN = as.character(
any(RNA.Seq_L1.terms %in% description)
),
microRNA.Seq = as.character(
any(microRNA.Seq_L1.terms %in% description)
),
microarray_with_mRNA_data = as.character(
any(microarray.with.mRNA_L1.terms %in% description)
),
microarray_with_microRNA_data = as.character(
any(microarray.with.microRNA_L1.terms %in% description)
),
methylation = as.character(
any(methylation_L1.terms %in% description))
)
} else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}, cbio ) #! mycgds
# looking for supported techniques at level 2
list_of_available_data_L2 <- sapply(
list_of_studies$cancer_study_id, function(cs, cbio_api) {
# Obtain available techniques
mols <- molecularProfiles(cbio_api, cs)
# Converting new format to the old format
colnames(mols)[colnames(mols) == "name"] <- "genetic_profile_name"
colnames(mols)[colnames(mols) == "molecularProfileId"] <-
"genetic_profile_id"
available_options_2 <- mols
#! available_options_2 <- getGeneticProfiles(cgds, cs)
# Update progressbar
i <<- i + 1
setTxtProgressBar(pb, i)
if(length(available_options_2) > 1){
if(any(colnames(available_options_2) == "genetic_profile_name")){
description <- available_options_2$genetic_profile_name
c(RNA.Seq = as.character(
any(RNA.Seq_L2.terms %in% description)
),
RNA.Seq.RTN = as.character(
any(RNA.Seq_rtn_L2.terms %in% description)
),
microRNA.Seq = as.character(
any(microRNA.Seq_L2.terms %in% description)
),
microarray_with_mRNA_data = as.character(
any(microarray.with.mRNA_L2.terms %in% description)
),
microarray_with_microRNA_data = as.character(
any(microarray.with.microRNA_L2.terms %in% description)
),
methylation = as.character(
any(methylation_L2.terms %in% description))
)
} else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}, cbio) #! mycgds
# close progressbar
close(pb)
# Find double positives
for(col_number in seq_len(ncol(list_of_available_data_L1))){
for(row_number in seq_len(nrow(list_of_available_data_L1))){
current_L1 <- list_of_available_data_L1[row_number, col_number]
if(current_L1 == "TRUE"){
current_L2 <- list_of_available_data_L2[row_number, col_number]
if(current_L2 == "FALSE"){
current_L1 <- FALSE
list_of_available_data_L1[row_number, col_number] <- current_L1
}
}
}
}
# Replacing True and False with available and ""
list_of_available_data_L1[list_of_available_data_L1=="TRUE"] <- "available"
list_of_available_data_L1[list_of_available_data_L1=="FALSE"] <- "-"
# joining list.of.available.data to list.of.studies
combined_list <- cbind(list_of_studies[,"cancer_study_id"],
list_of_studies[,"name"],
t(list_of_available_data_L1),
list_of_studies[,"allSampleCount"],
list_of_studies[,"pmid"],
list_of_studies[,"description"])
colnames(combined_list) <-
c("Cancer_Study_ID", "Cancer_Study_Name", "RNA.Seq", "RNA.Seq.RTN",
"microRNA.Seq", "microarray_with_mRNA_data" ,
"microarray_with_microRNA_data", "methylation", "Number of All Samples",
"PMID", "Description")
rownames(combined_list) <- seq_len(nrow(combined_list))
############################################################################
# Exporting results
# Converting matrix to data.frame, and the store as an excel file
combined_list_dataframe <- data.frame(combined_list)
colnames(combined_list_dataframe) <-
gsub("_", " ", colnames(combined_list_dataframe))
colnames(combined_list_dataframe) <-
gsub("\\.", "-", colnames(combined_list_dataframe))
colnames(combined_list_dataframe) <-
gsub("\\-RTN", " (RTN)", colnames(combined_list_dataframe))
rownames(combined_list_dataframe) <- seq_len(nrow(combined_list))
if(oneOfEach == FALSE){
# Store Xlsx file
ad <- createWorkbook()
addWorksheet(ad, sheetName = "Available Data")
writeData(ad,
sheet = "Available Data",
x = combined_list_dataframe,
rowNames = TRUE)
saveWorkbook(ad, file=paste(excelFileName, ".xlsx", sep = ""))
# message("[availableData] Finished.")
message(c("[availableData] The output was stored as '", excelFileName, ".xlsx","'."))
}else{
if(oneOfEach == "RNA-Seq"){
positive_index <- combined_list_dataframe$`RNA-Seq` == "available"
}else if(oneOfEach == "RNA-SeqRTN"){
positive_index <- combined_list_dataframe$`RNA-Seq (RTN)`== "available"
}else if(oneOfEach == "microRNA-Seq"){
positive_index <- combined_list_dataframe$`microRNA-Seq` == "available"
}else if(oneOfEach == "Microarray.mRNA"){
positive_index <-
combined_list_dataframe$`microarray with mRNA data` == "available"
}else if(oneOfEach == "Microarray.microRNA"){
positive_index <-
combined_list_dataframe$`microarray with microRNA data`== "available"
}
if(sum(positive_index) == 0){
stop("No '", oneOfEach,"' study is available!")
}else{
# Obtaining positive cancer studies
positive_list <-
combined_list_dataframe$`Cancer Study Name`[positive_index]
# Obtaining unique cancer types
unique_cancer_types <- sort(unique(gsub("\\(.*" ,"", positive_list)))
unique_studies <- vector("character", length = length(unique_cancer_types))
# Finding best studies
for(CSbest in seq_along(unique_cancer_types)){
positive_candidate_indices <- grep(unique_cancer_types[CSbest], positive_list)
positive_candidates <- positive_list[positive_candidate_indices]
# Filter overlapping unique Cancers
clean_names_positives <-
gsub("\\(.*" ,"", positive_candidates)
correct_positives_index <-
which(clean_names_positives == unique_cancer_types[CSbest])
positive_candidates <- positive_candidates[correct_positives_index]
if(length(positive_candidates) > 1){
tcga_indices <- grep("TCGA", positive_candidates)
if(length(tcga_indices) >= 1){
tcga_studies <- positive_candidates[tcga_indices]
final_candidate <- tcga_studies[length(tcga_studies)]
}else{
final_candidate <-
positive_candidates[length(positive_candidates)]
}
unique_studies[CSbest] <- final_candidate
} else {
unique_studies[CSbest] <- positive_candidates
}
}
}
message("[availableData] generating list of cancers studies ...")
sort(unique_studies)
}
}else{
message("[availableData] Function was haulted!")
}
}
armanshahrisa/cbaf documentation built on Nov. 5, 2022, 3:21 a.m.
R Package Documentation
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Defines functions availableData
Documented in availableData
#' @title Check which Data types are available for each cancer study.
#'
#' @description This function checks all the cancer studies that are registered
#' in 'cbioportal.org' to examine whether or not they contain RNA-Seq,
#' microRNA-Seq, microarray(mRNA), microarray(miRNA) and methylation data.
#'
#' @details
#' \tabular{lllll}{
#' Package: \tab cbaf \cr
#' Type: \tab Package \cr
#' Version: \tab 1.20.0 \cr
#' Date: \tab 2022-10-24 \cr
#' License: \tab Artistic-2.0 \cr
#' }
#'
#'
#'
#' @importFrom cBioPortalData cBioPortal getStudies sampleLists molecularProfiles
#'
#' @importFrom openxlsx createWorkbook addWorksheet writeData saveWorkbook
#'
#' @importFrom utils head setTxtProgressBar txtProgressBar
#'
#'
#'
#' @usage availableData(excelFileName, oneOfEach = FALSE)
#'
#' @param excelFileName a character string that is required to name the output
#' and, if requested, excel file.
#'
#' @param oneOfEach a character string that is used to alter the function's
#' behavior to select unique one cancer of each type that contains data for the
#' requested technique. The default value is \code{"FALSE"}. Supported
#' techniques include \code{"RNA-Seq"}, \code{"RNA-SeqRTN"},
#' \code{"microRNA-Seq"}, \code{"Microarray.mRNA"}, \code{"Microarray.mRNA"},
#' and \code{"methylation"}.
#'
#'
#' @return An excel file that contains all the cancer studies versus available
#' data types
#'
#'
#'
#' @author Arman Shahrisa, \email{shahrisa.arman@hotmail.com} [maintainer,
#' copyright holder]
#' @author Maryam Tahmasebi Birgani, \email{tahmasebi-ma@ajums.ac.ir}
#'
#' @export
################################################################################
################################################################################
################ Dataset availability in all cBioportal Cancers ################
################################################################################
################################################################################
availableData <- function(excelFileName, oneOfEach = FALSE){
##############################################################################
# Check input parameters
if(!is.character(excelFileName)){
stop("[availableData] 'excelFileName' must be a character string!")
}
supported.techniques <- c("RNA-Seq",
"RNA-SeqRTN",
"microRNA-Seq",
"Microarray.mRNA",
"Microarray.microRNA",
"methylation")
if(! oneOfEach == FALSE){
if(! oneOfEach %in% supported.techniques){
stop("[availableData] 'oneOfEach' must be either 'RNA-Seq', 'RNA-SeqRTN, 'microRNA-Seq', 'microarray.mRNA', 'microarray.microRNA', 'methylation', or simply FALSE !")
} else{
continue <- TRUE
}
}
if(oneOfEach == FALSE){
if(file.exists(paste(excelFileName, ".xlsx", sep = ""))){
message("[availableData] Warning! '", excelFileName, ".xlsx", "' already exists!")
choiceYesNo <- readline(prompt = "[availableData] Overwrite the file? (yes/no): ")
if(choiceYesNo == "yes"){
# Remove the previous file
file.remove(paste(excelFileName, ".xlsx", sep = ""))
continue <- TRUE
}else if(choiceYesNo == "no"){
continue <- FALSE
}else{
stop("[availableData] please type 'yes' or 'no'!")
}
} else{
continue <- TRUE
}
}
if(continue){
############################################################################
# Prerequisites
# Prerequisites for cBioportal
cbio <- cBioPortal()
studies <- getStudies(cbio)
# Converting new format to the old format
colnames(studies)[colnames(studies) == "studyId"] <- "cancer_study_id"
list_of_studies <- studies
#! mycgds = CGDS("http://www.cbioportal.org/")
#! list_of_studies <- getCancerStudies(mycgds)
message("[availableData] Checking all cancer studies")
# create progress bar
pb <- txtProgressBar(min = 0, max = nrow(list_of_studies)*2, style = 3)
i <- 0
############################################################################
## core segment
# looking for supported techniques at level 1
list_of_available_data_L1 <- sapply(
list_of_studies$cancer_study_id, function(cs, cbio_api) {
# Obtain available techniques
samp <- sampleLists(cbio_api, cs)
# Converting new format to the old format
colnames(samp)[colnames(samp) == "sampleListId"] <- "case_list_id"
colnames(samp)[colnames(samp) == "name"] <- "case_list_name"
available_options_1 <- samp
#! available_options_1 <- getCaseLists(cgds, cs)
# Update progressbar
i <<- i + 1
setTxtProgressBar(pb, i)
if(length(available_options_1) > 1){
if(any(colnames(available_options_1) == "case_list_name")){
description <- available_options_1$case_list_name
c(RNA.Seq = as.character(
any(RNA.Seq_L1.terms %in% description)
),
RNA.Seq.RTN = as.character(
any(RNA.Seq_L1.terms %in% description)
),
microRNA.Seq = as.character(
any(microRNA.Seq_L1.terms %in% description)
),
microarray_with_mRNA_data = as.character(
any(microarray.with.mRNA_L1.terms %in% description)
),
microarray_with_microRNA_data = as.character(
any(microarray.with.microRNA_L1.terms %in% description)
),
methylation = as.character(
any(methylation_L1.terms %in% description))
)
} else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}, cbio ) #! mycgds
# looking for supported techniques at level 2
list_of_available_data_L2 <- sapply(
list_of_studies$cancer_study_id, function(cs, cbio_api) {
# Obtain available techniques
mols <- molecularProfiles(cbio_api, cs)
# Converting new format to the old format
colnames(mols)[colnames(mols) == "name"] <- "genetic_profile_name"
colnames(mols)[colnames(mols) == "molecularProfileId"] <-
"genetic_profile_id"
available_options_2 <- mols
#! available_options_2 <- getGeneticProfiles(cgds, cs)
# Update progressbar
i <<- i + 1
setTxtProgressBar(pb, i)
if(length(available_options_2) > 1){
if(any(colnames(available_options_2) == "genetic_profile_name")){
description <- available_options_2$genetic_profile_name
c(RNA.Seq = as.character(
any(RNA.Seq_L2.terms %in% description)
),
RNA.Seq.RTN = as.character(
any(RNA.Seq_rtn_L2.terms %in% description)
),
microRNA.Seq = as.character(
any(microRNA.Seq_L2.terms %in% description)
),
microarray_with_mRNA_data = as.character(
any(microarray.with.mRNA_L2.terms %in% description)
),
microarray_with_microRNA_data = as.character(
any(microarray.with.microRNA_L2.terms %in% description)
),
methylation = as.character(
any(methylation_L2.terms %in% description))
)
} else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}else{
c(RNA.Seq = "FALSE",
RNA.Seq.RTN = "FALSE",
microRNA.Seq = "FALSE",
microarray_with_mRNA_data = "FALSE",
microarray_with_microRNA_data = "FALSE",
methylation = "FALSE")
}
}, cbio) #! mycgds
# close progressbar
close(pb)
# Find double positives
for(col_number in seq_len(ncol(list_of_available_data_L1))){
for(row_number in seq_len(nrow(list_of_available_data_L1))){
current_L1 <- list_of_available_data_L1[row_number, col_number]
if(current_L1 == "TRUE"){
current_L2 <- list_of_available_data_L2[row_number, col_number]
if(current_L2 == "FALSE"){
current_L1 <- FALSE
list_of_available_data_L1[row_number, col_number] <- current_L1
}
}
}
}
# Replacing True and False with available and ""
list_of_available_data_L1[list_of_available_data_L1=="TRUE"] <- "available"
list_of_available_data_L1[list_of_available_data_L1=="FALSE"] <- "-"
# joining list.of.available.data to list.of.studies
combined_list <- cbind(list_of_studies[,"cancer_study_id"],
list_of_studies[,"name"],
t(list_of_available_data_L1),
list_of_studies[,"allSampleCount"],
list_of_studies[,"pmid"],
list_of_studies[,"description"])
colnames(combined_list) <-
c("Cancer_Study_ID", "Cancer_Study_Name", "RNA.Seq", "RNA.Seq.RTN",
"microRNA.Seq", "microarray_with_mRNA_data" ,
"microarray_with_microRNA_data", "methylation", "Number of All Samples",
"PMID", "Description")
rownames(combined_list) <- seq_len(nrow(combined_list))
############################################################################
# Exporting results
# Converting matrix to data.frame, and the store as an excel file
combined_list_dataframe <- data.frame(combined_list)
colnames(combined_list_dataframe) <-
gsub("_", " ", colnames(combined_list_dataframe))
colnames(combined_list_dataframe) <-
gsub("\\.", "-", colnames(combined_list_dataframe))
colnames(combined_list_dataframe) <-
gsub("\\-RTN", " (RTN)", colnames(combined_list_dataframe))
rownames(combined_list_dataframe) <- seq_len(nrow(combined_list))
if(oneOfEach == FALSE){
# Store Xlsx file
ad <- createWorkbook()
addWorksheet(ad, sheetName = "Available Data")
writeData(ad,
sheet = "Available Data",
x = combined_list_dataframe,
rowNames = TRUE)
saveWorkbook(ad, file=paste(excelFileName, ".xlsx", sep = ""))
# message("[availableData] Finished.")
message(c("[availableData] The output was stored as '", excelFileName, ".xlsx","'."))
}else{
if(oneOfEach == "RNA-Seq"){
positive_index <- combined_list_dataframe$`RNA-Seq` == "available"
}else if(oneOfEach == "RNA-SeqRTN"){
positive_index <- combined_list_dataframe$`RNA-Seq (RTN)`== "available"
}else if(oneOfEach == "microRNA-Seq"){
positive_index <- combined_list_dataframe$`microRNA-Seq` == "available"
}else if(oneOfEach == "Microarray.mRNA"){
positive_index <-
combined_list_dataframe$`microarray with mRNA data` == "available"
}else if(oneOfEach == "Microarray.microRNA"){
positive_index <-
combined_list_dataframe$`microarray with microRNA data`== "available"
}
if(sum(positive_index) == 0){
stop("No '", oneOfEach,"' study is available!")
}else{
# Obtaining positive cancer studies
positive_list <-
combined_list_dataframe$`Cancer Study Name`[positive_index]
# Obtaining unique cancer types
unique_cancer_types <- sort(unique(gsub("\\(.*" ,"", positive_list)))
unique_studies <- vector("character", length = length(unique_cancer_types))
# Finding best studies
for(CSbest in seq_along(unique_cancer_types)){
positive_candidate_indices <- grep(unique_cancer_types[CSbest], positive_list)
positive_candidates <- positive_list[positive_candidate_indices]
# Filter overlapping unique Cancers
clean_names_positives <-
gsub("\\(.*" ,"", positive_candidates)
correct_positives_index <-
which(clean_names_positives == unique_cancer_types[CSbest])
positive_candidates <- positive_candidates[correct_positives_index]
if(length(positive_candidates) > 1){
tcga_indices <- grep("TCGA", positive_candidates)
if(length(tcga_indices) >= 1){
tcga_studies <- positive_candidates[tcga_indices]
final_candidate <- tcga_studies[length(tcga_studies)]
}else{
final_candidate <-
positive_candidates[length(positive_candidates)]
}
unique_studies[CSbest] <- final_candidate
} else {
unique_studies[CSbest] <- positive_candidates
}
}
}
message("[availableData] generating list of cancers studies ...")
sort(unique_studies)
}
}else{
message("[availableData] Function was haulted!")
}
}
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