R/parse_TCGA_PAAD.R
In rkawalerski/pdacR: Obtains and analyzes PDAC transcriptomics data
Defines functions
parse_TCGA_PAAD
Documented in
parse_TCGA_PAAD
#' Parse data from tables for TCGA_PAAD
#'
#' @export
#' @import org.Hs.eg.db
#' @import stringr
#' @import plyr
#' @import openxlsx
parse_TCGA_PAAD <- function() {
# library(org.Hs.eg.db)
# library(stringr)
# library(plyr)
# library(openxlsx)
## =============================
# Parse expression data
## =============================
expression_file <- system.file("extdata/TCGA_PAAD",
"PAAD.183.FullSet.fixed_20141109.txt",
package = "pdacR")
ex <- read.table(file = expression_file,header = TRUE)
ex <- ex[-1,]
rownames(ex) <- ex[,1]
ex <- ex[,-1]
featInfo <- data.frame(SYMBOL = sapply(rownames(ex),FUN = function(x) {strsplit(x,"|",fixed=TRUE)[[1]][1]}),
ENTREZID = sapply(rownames(ex),FUN = function(x) {strsplit(x,"|",fixed=TRUE)[[1]][2]}))
## =============================
# Pull sample information
## =============================
sample_table_file <- system.file("extdata/TCGA_PAAD",
"Supplemental_Table_1_PAAD_sample_table_for_TCGA.xlsx",
package = "pdacR")
#---------------
sample_table_1 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 1)
sample_table_1$Patient <- sapply(X = sample_table_1$Tumor.Sample.ID,
FUN = function(x) {
gsub(pattern = "-01A",
replacement = "",
x = x)})
sample_table_1$Decision <- "whitelist"
names(sample_table_1)
#---------------
sample_table_2 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 2)
sample_table_2$Tumor.Sample.ID <- sapply(X = sample_table_2$Patient,
FUN = function(x) {
paste(x,"01A",sep="-")})
sample_table_2 <- sample_table_2[,(names(sample_table_2) %in% c("Patient","Decision","Batch","Pathology.Exclusion","Tumor.Sample.ID"))]
names(sample_table_2) <- gsub(x=names(sample_table_2),pattern="Pathology.Exclusion",replacement = "Histological.type.by.RHH")
summary(sample_table_2)
#---------------
sample_table_3 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 3)
sample_table_3$Tumor.Sample.ID <- sapply(X = sample_table_3$Patient,
FUN = function(x) {
paste(x,"01A",sep="-")})
sample_table_3 <- sample_table_3[,(names(sample_table_3) %in% c("Patient","Decision","Batch","Notes","Tumor.Sample.ID"))]
names(sample_table_3) <- gsub(x=names(sample_table_3),pattern="Notes",replacement = "Histological.type.by.RHH")
summary(sample_table_3)
#---------------
sample_table_4 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 4)
sample_table_4$Tumor.Sample.ID <- sapply(X = sample_table_4$Sample.ID,
FUN = function(x) {
paste(x,"A",sep="")})
sample_table_4 <- sample_table_4[,(names(sample_table_4) %in% c("Tumor.Sample.ID","Other.Notes","Pathology.Notes"))]
sample_table_4$Decision <- "normal"
sample_table_4$Patient <- gsub(sample_table_4$Tumor.Sample.ID,pattern="-11",replacement = "")
names(sample_table_4) <- gsub(x=names(sample_table_4),pattern="Pathology.Notes",replacement = "Histological.type.by.RHH")
summary(sample_table_4)
## =============================
# Join sample information tables
## =============================
sample_table <- join(sample_table_1,
sample_table_2,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
sample_table_3,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
sample_table_4,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
data.frame(Tumor.Sample.ID = "TCGA-HZ-A9TJ-06A",
Decision = "metastasis" ),
type="full",
by = "Tumor.Sample.ID")
## =============================
# Match expression and sample data order and remove cases without metadata
## =============================
data_cols_fixed <- sapply(names(ex),FUN=function(x) {substr(x = gsub(x = x,pattern = ".",replacement = "-",fixed = TRUE),
start = 1,stop = 16 )})
col_order <- match(x = data_cols_fixed,
table = sample_table$Tumor.Sample.ID)
sample_table <- sample_table[col_order,]
no_metadata = which(is.na(sample_table$Tumor.Sample.ID))
sample_table <- sample_table[-no_metadata,]
ex <- ex[,-no_metadata]
sampInfo <- sample_table
## =============================
# Clean up sample information strings and adjust sample information classes
## =============================
names(sampInfo) <- gsub(pattern = "\\(|\\)|=",
replacement = "",
x = names(sampInfo),
fixed = FALSE)
sampInfo <- (data.frame(lapply(X = sampInfo,FUN = function(x) {
if(is.character(x)){
return(as.factor(x))
}
return(x)
})))
# to numeric
sampInfo$Follow.up.days <-
as.numeric(as.character(sampInfo$Follow.up.days))
sampInfo$Initial.Slide.Tumor.Cellularity <-
as.numeric(as.character(sampInfo$Initial.Slide.Tumor.Cellularity))
# to factor
# sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX <-
# as.factor(sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX)
sampInfo$Bailey.Clusters = factor(sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX,
levels = c(1,2,3,4),
labels = c("Squamous","Immunogenic","Progenitor","ADEX"))
sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX = NULL
# sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM <-
# as.factor(sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM)
sampInfo$Collisson.Clusters = factor(sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM,
levels = c(1,2,3),
labels = c("Classical","Exocrine","QM"))
sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM <- NULL
# sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical <-
# as.factor(sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical)
sampInfo$Moffitt.Clusters = factor(sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical,
levels = c(1,2),
labels = c("Basal-like","Classical"))
sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical <- NULL
sampInfo$Moffitt.HighPurity.Clusters = factor(sampInfo$mRNA.Moffitt.clusters.76.High.Purity.Samples.Only.1basal.2classical,
levels = c(1,2),
labels = c("Basal-like","Classical"))
sampInfo$mRNA.Moffitt.clusters.76.High.Purity.Samples.Only.1basal.2classical <- NULL
sampInfo$RPPA.Clusters.All.150.Samples <-
as.factor(sampInfo$RPPA.Clusters.All.150.Samples)
# sampInfo$KRAS.Mutated.1.or.0 <-
# as.factor(sampInfo$KRAS.Mutated.1.or.0)
sampInfo$KRAS.Mutation.Status <- factor(sampInfo$KRAS.Mutated.1.or.0,
levels = c(0,1),
labels = c("No Mutation","Mutated"))
sampInfo$KRAS.Mutated.1.or.0 <- NULL
sampInfo$lncRNA.Clusters.All.150.Samples <-
as.factor(sampInfo$lncRNA.Clusters.All.150.Samples)
sampInfo$miRNA.Clusters.All.150.Samples <-
as.factor(sampInfo$miRNA.Clusters.All.150.Samples)
sampInfo$Methylation.Clusters.All.150.Samples <-
as.factor(sampInfo$Methylation.Clusters.All.150.Samples)
summary(sampInfo)
## =============================
# Coerce expression data to numeric
## =============================
ex <- apply(X=ex,MARGIN=2,FUN=as.numeric)
dimnames(ex)[[2]] <- sampInfo$Tumor.Sample.ID
# Prepare dataset for consensus clustering for tumor and stroma subtyping - DEPRECATED
# ================================================
# sampInfo$tumor.classifier.training <- FALSE
# sampInfo$stroma.classifier.training <- FALSE
# sampInfo[["cluster.MT"]] <- as.character(NA)
# sampInfo[["cluster.MS"]] <- as.character(NA)
# sampInfo$cluster.MT <- c('basal','classical')[sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical]
# sampleset <- which(sampInfo$Decision %in% "whitelist")
# sampInfo$tumor.classifier.training[sampleset] <- TRUE
# sampleset <- c(sampleset,which(sampInfo$Decision %in% "pseudonormal"))
# sampInfo$stroma.classifier.training[sampleset] <- TRUE
#
# ## =============================
# # Make stroma cluster calls
# ## =============================
# featureset <- which(featInfo$SYMBOL %in%
# c(as.character(gene_lists$Moffitt.Activated.25),
# as.character(gene_lists$Moffitt.Normal.25)))
# sampletree <- ConsensusClusterPlus::ConsensusClusterPlus(d = t(scale(t(as.matrix(log2(1+ex[featureset,sampleset]))))),
# seed = 1234,
# pFeature = 0.8,
# pItem = 0.8,
# maxK = 6,
# reps=200,
# distance="pearson",
# clusterAlg="hc")[[2]]$consensusTree
# tmp.cluster <- c("activated","normal")[cutree(tree = sampletree, k = 2)]
# sampInfo$cluster.MS[sampleset] <- tmp.cluster
# Convert cluster calls to factor
# =====================================================================
# sampInfo$cluster.MT <- as.factor(sampInfo$cluster.MT)
# sampInfo$cluster.MS <- as.factor(sampInfo$cluster.MS)
# sampInfo$MoffittTumor <- sampInfo$cluster.MT
# sampInfo$MoffittStroma <- sampInfo$cluster.MS
# sampInfo$cluster.MT <- NULL
# sampInfo$cluster.MS <- NULL
## =============================
# Wrangle survival data for shiny app
## =============================
sampInfo$survivalA <- sampInfo$Follow.up.days
sampInfo$Days.to.death <- NULL
sampInfo$Follow.up.days <- NULL
sampInfo$censorA.0yes.1no <- factor(sampInfo$Censored.1yes.0no,
levels = c(0,1,NaN),
labels = c("1","0","NA"))
sampInfo$Censored.1yes.0no <- NULL
## =============================
# Organize dataset
## =============================
sampInfo$Filter_by = sampInfo$Decision
sampInfo = sampInfo[,-which(colnames(sampInfo) == "Decision")]
sampInfo = sampInfo[,order(colnames(sampInfo))]
TCGA_PAAD <- list()
TCGA_PAAD$ex <- ex
TCGA_PAAD$sampInfo <- sampInfo
TCGA_PAAD$metadata <- list(log.transformed = FALSE,
reference = "Raphael BJ, et al, Cancer Cell, 2017, PMID:28810144",
accession = "DbGAaP: phs000178, BroadGDAC: PAAD",
description = "181 macrodissected primary tumors, 150 of which are whitelisted by histologic identification of neoplastic cellularity",
survivalA = "overall survival days",
survivalB = "None",
exp.type = "RNAseq",
default_selections = list(filter_column = "Filter_by",
filter_levels = c("Filter_by:exclude",
"Filter_by:metastasis",
"Filter_by:normal",
"Filter_by:pseudonormal"),
geneSets = c("Moffitt.Basal.25","Moffitt.Classical.25"),
sampleTracks = c("ABSOLUTE Purity")))
TCGA_PAAD$featInfo <- featInfo
TCGA_PAAD$sampInfo = TCGA_PAAD$sampInfo[,c(which(colnames(TCGA_PAAD$sampInfo) == "Tumor.Sample.ID"),
which(!(colnames(TCGA_PAAD$sampInfo) == "Tumor.Sample.ID")))]
## =============================
# Save dataset
## =============================
saveRDS(TCGA_PAAD,
file = "./data/TCGA_PAAD.rds",
compress = T)
return(NULL)
}
rkawalerski/pdacR documentation built on Jan. 25, 2025, 10:50 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions parse_TCGA_PAAD
Documented in parse_TCGA_PAAD
#' Parse data from tables for TCGA_PAAD
#'
#' @export
#' @import org.Hs.eg.db
#' @import stringr
#' @import plyr
#' @import openxlsx
parse_TCGA_PAAD <- function() {
# library(org.Hs.eg.db)
# library(stringr)
# library(plyr)
# library(openxlsx)
## =============================
# Parse expression data
## =============================
expression_file <- system.file("extdata/TCGA_PAAD",
"PAAD.183.FullSet.fixed_20141109.txt",
package = "pdacR")
ex <- read.table(file = expression_file,header = TRUE)
ex <- ex[-1,]
rownames(ex) <- ex[,1]
ex <- ex[,-1]
featInfo <- data.frame(SYMBOL = sapply(rownames(ex),FUN = function(x) {strsplit(x,"|",fixed=TRUE)[[1]][1]}),
ENTREZID = sapply(rownames(ex),FUN = function(x) {strsplit(x,"|",fixed=TRUE)[[1]][2]}))
## =============================
# Pull sample information
## =============================
sample_table_file <- system.file("extdata/TCGA_PAAD",
"Supplemental_Table_1_PAAD_sample_table_for_TCGA.xlsx",
package = "pdacR")
#---------------
sample_table_1 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 1)
sample_table_1$Patient <- sapply(X = sample_table_1$Tumor.Sample.ID,
FUN = function(x) {
gsub(pattern = "-01A",
replacement = "",
x = x)})
sample_table_1$Decision <- "whitelist"
names(sample_table_1)
#---------------
sample_table_2 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 2)
sample_table_2$Tumor.Sample.ID <- sapply(X = sample_table_2$Patient,
FUN = function(x) {
paste(x,"01A",sep="-")})
sample_table_2 <- sample_table_2[,(names(sample_table_2) %in% c("Patient","Decision","Batch","Pathology.Exclusion","Tumor.Sample.ID"))]
names(sample_table_2) <- gsub(x=names(sample_table_2),pattern="Pathology.Exclusion",replacement = "Histological.type.by.RHH")
summary(sample_table_2)
#---------------
sample_table_3 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 3)
sample_table_3$Tumor.Sample.ID <- sapply(X = sample_table_3$Patient,
FUN = function(x) {
paste(x,"01A",sep="-")})
sample_table_3 <- sample_table_3[,(names(sample_table_3) %in% c("Patient","Decision","Batch","Notes","Tumor.Sample.ID"))]
names(sample_table_3) <- gsub(x=names(sample_table_3),pattern="Notes",replacement = "Histological.type.by.RHH")
summary(sample_table_3)
#---------------
sample_table_4 <- read.xlsx(xlsxFile = sample_table_file,
sheet = 4)
sample_table_4$Tumor.Sample.ID <- sapply(X = sample_table_4$Sample.ID,
FUN = function(x) {
paste(x,"A",sep="")})
sample_table_4 <- sample_table_4[,(names(sample_table_4) %in% c("Tumor.Sample.ID","Other.Notes","Pathology.Notes"))]
sample_table_4$Decision <- "normal"
sample_table_4$Patient <- gsub(sample_table_4$Tumor.Sample.ID,pattern="-11",replacement = "")
names(sample_table_4) <- gsub(x=names(sample_table_4),pattern="Pathology.Notes",replacement = "Histological.type.by.RHH")
summary(sample_table_4)
## =============================
# Join sample information tables
## =============================
sample_table <- join(sample_table_1,
sample_table_2,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
sample_table_3,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
sample_table_4,
type="full",
by = "Tumor.Sample.ID")
sample_table <- join(sample_table,
data.frame(Tumor.Sample.ID = "TCGA-HZ-A9TJ-06A",
Decision = "metastasis" ),
type="full",
by = "Tumor.Sample.ID")
## =============================
# Match expression and sample data order and remove cases without metadata
## =============================
data_cols_fixed <- sapply(names(ex),FUN=function(x) {substr(x = gsub(x = x,pattern = ".",replacement = "-",fixed = TRUE),
start = 1,stop = 16 )})
col_order <- match(x = data_cols_fixed,
table = sample_table$Tumor.Sample.ID)
sample_table <- sample_table[col_order,]
no_metadata = which(is.na(sample_table$Tumor.Sample.ID))
sample_table <- sample_table[-no_metadata,]
ex <- ex[,-no_metadata]
sampInfo <- sample_table
## =============================
# Clean up sample information strings and adjust sample information classes
## =============================
names(sampInfo) <- gsub(pattern = "\\(|\\)|=",
replacement = "",
x = names(sampInfo),
fixed = FALSE)
sampInfo <- (data.frame(lapply(X = sampInfo,FUN = function(x) {
if(is.character(x)){
return(as.factor(x))
}
return(x)
})))
# to numeric
sampInfo$Follow.up.days <-
as.numeric(as.character(sampInfo$Follow.up.days))
sampInfo$Initial.Slide.Tumor.Cellularity <-
as.numeric(as.character(sampInfo$Initial.Slide.Tumor.Cellularity))
# to factor
# sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX <-
# as.factor(sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX)
sampInfo$Bailey.Clusters = factor(sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX,
levels = c(1,2,3,4),
labels = c("Squamous","Immunogenic","Progenitor","ADEX"))
sampInfo$mRNA.Bailey.Clusters.All.150.Samples.1squamous.2immunogenic.3progenitor.4ADEX = NULL
# sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM <-
# as.factor(sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM)
sampInfo$Collisson.Clusters = factor(sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM,
levels = c(1,2,3),
labels = c("Classical","Exocrine","QM"))
sampInfo$mRNA.Collisson.clusters.All.150.Samples.1classical.2exocrine.3QM <- NULL
# sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical <-
# as.factor(sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical)
sampInfo$Moffitt.Clusters = factor(sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical,
levels = c(1,2),
labels = c("Basal-like","Classical"))
sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical <- NULL
sampInfo$Moffitt.HighPurity.Clusters = factor(sampInfo$mRNA.Moffitt.clusters.76.High.Purity.Samples.Only.1basal.2classical,
levels = c(1,2),
labels = c("Basal-like","Classical"))
sampInfo$mRNA.Moffitt.clusters.76.High.Purity.Samples.Only.1basal.2classical <- NULL
sampInfo$RPPA.Clusters.All.150.Samples <-
as.factor(sampInfo$RPPA.Clusters.All.150.Samples)
# sampInfo$KRAS.Mutated.1.or.0 <-
# as.factor(sampInfo$KRAS.Mutated.1.or.0)
sampInfo$KRAS.Mutation.Status <- factor(sampInfo$KRAS.Mutated.1.or.0,
levels = c(0,1),
labels = c("No Mutation","Mutated"))
sampInfo$KRAS.Mutated.1.or.0 <- NULL
sampInfo$lncRNA.Clusters.All.150.Samples <-
as.factor(sampInfo$lncRNA.Clusters.All.150.Samples)
sampInfo$miRNA.Clusters.All.150.Samples <-
as.factor(sampInfo$miRNA.Clusters.All.150.Samples)
sampInfo$Methylation.Clusters.All.150.Samples <-
as.factor(sampInfo$Methylation.Clusters.All.150.Samples)
summary(sampInfo)
## =============================
# Coerce expression data to numeric
## =============================
ex <- apply(X=ex,MARGIN=2,FUN=as.numeric)
dimnames(ex)[[2]] <- sampInfo$Tumor.Sample.ID
# Prepare dataset for consensus clustering for tumor and stroma subtyping - DEPRECATED
# ================================================
# sampInfo$tumor.classifier.training <- FALSE
# sampInfo$stroma.classifier.training <- FALSE
# sampInfo[["cluster.MT"]] <- as.character(NA)
# sampInfo[["cluster.MS"]] <- as.character(NA)
# sampInfo$cluster.MT <- c('basal','classical')[sampInfo$mRNA.Moffitt.clusters.All.150.Samples.1basal.2classical]
# sampleset <- which(sampInfo$Decision %in% "whitelist")
# sampInfo$tumor.classifier.training[sampleset] <- TRUE
# sampleset <- c(sampleset,which(sampInfo$Decision %in% "pseudonormal"))
# sampInfo$stroma.classifier.training[sampleset] <- TRUE
#
# ## =============================
# # Make stroma cluster calls
# ## =============================
# featureset <- which(featInfo$SYMBOL %in%
# c(as.character(gene_lists$Moffitt.Activated.25),
# as.character(gene_lists$Moffitt.Normal.25)))
# sampletree <- ConsensusClusterPlus::ConsensusClusterPlus(d = t(scale(t(as.matrix(log2(1+ex[featureset,sampleset]))))),
# seed = 1234,
# pFeature = 0.8,
# pItem = 0.8,
# maxK = 6,
# reps=200,
# distance="pearson",
# clusterAlg="hc")[[2]]$consensusTree
# tmp.cluster <- c("activated","normal")[cutree(tree = sampletree, k = 2)]
# sampInfo$cluster.MS[sampleset] <- tmp.cluster
# Convert cluster calls to factor
# =====================================================================
# sampInfo$cluster.MT <- as.factor(sampInfo$cluster.MT)
# sampInfo$cluster.MS <- as.factor(sampInfo$cluster.MS)
# sampInfo$MoffittTumor <- sampInfo$cluster.MT
# sampInfo$MoffittStroma <- sampInfo$cluster.MS
# sampInfo$cluster.MT <- NULL
# sampInfo$cluster.MS <- NULL
## =============================
# Wrangle survival data for shiny app
## =============================
sampInfo$survivalA <- sampInfo$Follow.up.days
sampInfo$Days.to.death <- NULL
sampInfo$Follow.up.days <- NULL
sampInfo$censorA.0yes.1no <- factor(sampInfo$Censored.1yes.0no,
levels = c(0,1,NaN),
labels = c("1","0","NA"))
sampInfo$Censored.1yes.0no <- NULL
## =============================
# Organize dataset
## =============================
sampInfo$Filter_by = sampInfo$Decision
sampInfo = sampInfo[,-which(colnames(sampInfo) == "Decision")]
sampInfo = sampInfo[,order(colnames(sampInfo))]
TCGA_PAAD <- list()
TCGA_PAAD$ex <- ex
TCGA_PAAD$sampInfo <- sampInfo
TCGA_PAAD$metadata <- list(log.transformed = FALSE,
reference = "Raphael BJ, et al, Cancer Cell, 2017, PMID:28810144",
accession = "DbGAaP: phs000178, BroadGDAC: PAAD",
description = "181 macrodissected primary tumors, 150 of which are whitelisted by histologic identification of neoplastic cellularity",
survivalA = "overall survival days",
survivalB = "None",
exp.type = "RNAseq",
default_selections = list(filter_column = "Filter_by",
filter_levels = c("Filter_by:exclude",
"Filter_by:metastasis",
"Filter_by:normal",
"Filter_by:pseudonormal"),
geneSets = c("Moffitt.Basal.25","Moffitt.Classical.25"),
sampleTracks = c("ABSOLUTE Purity")))
TCGA_PAAD$featInfo <- featInfo
TCGA_PAAD$sampInfo = TCGA_PAAD$sampInfo[,c(which(colnames(TCGA_PAAD$sampInfo) == "Tumor.Sample.ID"),
which(!(colnames(TCGA_PAAD$sampInfo) == "Tumor.Sample.ID")))]
## =============================
# Save dataset
## =============================
saveRDS(TCGA_PAAD,
file = "./data/TCGA_PAAD.rds",
compress = T)
return(NULL)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.