.Rhistory

cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RNA-Seq vs RPPA"
cor_df <- rbind(cor_df, cor_gene_res)
# rnaseq viper
genes <- rownames(rnaseq)
cor_res <- lapply(1:length(genes), function(k){
fit <- cor.test(as.numeric(rnaseq[rownames(rnaseq) == genes[k], ]),
as.numeric(viper[rownames(viper) == genes[k], ]),
method = "pearson",
exact = FALSE)
################################ comment: add p-value here ############################
data.frame(feature = genes[k],
cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RNA-Seq vs VIPER"
cor_df <- rbind(cor_df, cor_gene_res)
# rppa viper
genes <- rownames(rppa)
cor_res <- lapply(1:length(genes), function(k){
fit <- cor.test(as.numeric(rppa[rownames(rppa) == genes[k], ]),
as.numeric(viper[rownames(viper) == genes[k], ]),
method = "pearson",
exact = FALSE)
################################ comment: add p-value here ############################
data.frame(feature = genes[k],
cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RPPA vs VIPER"
cor_df <- rbind(cor_df, cor_gene_res)
p_meds <- ddply(cor_df, .(type), summarise, med = round(median(cor_val), digits = 4))
cor_df$type <- factor(cor_df$type , levels=c("RNA-Seq vs VIPER", "RNA-Seq vs RPPA", "RPPA vs VIPER"))
p <- ggplot(cor_df, aes(x = type, y = cor_val, fill = type)) +
geom_boxplot(color = "black", width = 0.5) +
labs(x = "Data", y = "Pearson Correlation Value") +
theme(axis.text.x = element_text(size = 8), legend.position = "none") +
geom_text(data = p_meds, aes(x = type, y = med, label = med),
size = 3, vjust = -1)
return(p)
}
options(timeout = 200)
# ccle and gcsi rnaseq -> gencode v33
gencode_basic <- getGencodeFile(file = "gencode.\\{v}\\.basic.annotation.gtf.gz", version = "33")
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=TRUE)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BiocStyle")
library(BiocStyle)
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=TRUE)
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=FALSE)
library(AnnotationGx)
options(timeout = 200)
# ccle and gcsi rnaseq -> gencode v33
gencode_basic <- getGencodeFile(file = "gencode.\\{v}\\.basic.annotation.gtf.gz", version = "33")
load("C:/DrugVIPER/Data/Gencode.v33.annotation.RData")
ls()
rm(list=ls())
load("C:/DrugVIPER/Data/Gencode.v33.annotation.RData")
ls()
gencode_entrez <- getGencodeFile(file = "gencode\\.{v}\\.metadata.EntrezGene.gz", version = "33")
# ccle and gcsi rnaseq -> gencode v33
gencode_basic <- getGencodeFile(file = "gencode.\\{v}\\.basic.annotation.gtf.gz", version = "33")
rm(list=ls())
g
# ccle and gcsi rnaseq -> gencode v33
gencode_basic <- getGencodeFile(file = "gencode.\\{v}\\.basic.annotation.gtf.gz", version = "33")
gencode_entrez <- getGencodeFile(file = "gencode\\.{v}\\.metadata.EntrezGene.gz", version = "33")
colnames(gencode_entrez) <- c("transcript_id", "entrez_id")
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by = join_by("transcript_id"))
library(join_by)
library(dplyr)
join_by
left_join
head(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")])
head(gencode_entrez)
join_by("transcript_id")
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by = join_by("transcript_id"))
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by = join_by("transcript_id"))
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by =join_by("transcript_id"))
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by ="transcript_id")
head(gene_entrez)
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez, by =join_by("transcript_id"))
# map gencode_entrez to gene_id (include gene_name for naming later)
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez) # , by=join_by("transcript_id")
head(gene_entrez)
table(gene_entrez$entrez_id)
gene_entrez <- na.omit(distinct(gene_entrez[, c("gene_name", "gene_id", "entrez_id")]))
head(gene_entrez)
# add entrez ids
ccle_rnaseq_mapped <- map_entrez(ccle_rnaseq_gene_annot, ccle_rnaseq_exp, gene_entrez)
map_entrez <- function(gene_annot, exp, gene_entrez) {
# add row numbers to retain order later
gene_annot$id <- seq.int(nrow(gene_annot))
# add entrez
gene_annot_entrez <- left_join(as.data.frame(gene_annot), gene_entrez[ ,   c("gene_id", "entrez_id")], by = join_by("gene_id"))
# remove rows with NA for entrez
gene_annot_entrez <- gene_annot_entrez[!is.na(gene_annot_entrez$entrez_id), ]
# matching assays for rows with entrez id
exp_entrez <- exp[gene_annot_entrez$id, ]
# make entrez id the rownames
row.names(exp_entrez) <- gene_annot_entrez$entrez_id
return (list(gene_annot_entrez, exp_entrez))
}
############################################################
## Computing viper scores (pan-cancer)
############################################################
corr_pancancer <- function(viper_pancancer, exp_entrez, label) {
# get matching cell_lines and genes
matching_cell_lines <- intersect(colnames(viper_pancancer), colnames(exp_entrez))
matching_genes <- intersect(row.names(viper_pancancer), row.names(exp_entrez))
viper_exp <- viper_pancancer[matching_genes, matching_cell_lines]
exp <- exp_entrez[matching_genes, matching_cell_lines]
# scaling
viper_exp <- t(scale(t(viper_exp)))
exp <- t(scale(t(exp)))
to_keep <- as.vector(which(rowSums(!is.na(exp)) >= 20 & rowSums(!is.na(viper_exp)) >= 20))
viper_exp <- viper_exp[to_keep, ]
exp <- exp[to_keep, ]
genes <- rownames(viper_exp)
cor_res <- lapply(1:length(genes), function(k){
tryCatch({
fit <- cor.test(as.numeric(viper_exp[rownames(viper_exp) == genes[k], ]),
as.numeric(exp[rownames(exp) == genes[k], ]),
method = "pearson")
data.frame(feature = genes[k],
cor_val = fit$estimate,
################# comment: keep p-value ##########################
pval = fit$p.value
)
}, error = function(e) {
# don't add to cor_res
}
)
})
cor_gene_res <- do.call(rbind, cor_res)
cor_gene_res <- na.omit(cor_gene_res)
################# comment: do adjustment for multiple test ##########################
cor_gene_res$padj = p.adjust(cor_gene_res$pval, "fdr")
med <- round(median(cor_gene_res$cor_val), digits = 6)
# boxplot
ggplot(data = cor_gene_res,aes(y=cor_val)) +
geom_boxplot() +
labs(title=label, x="", y = "Pearson correlation") +
annotate("text", x=Inf, y=Inf, label=paste("median correlation = ", med), angle=0, size=4, color="blue", vjust = 2, hjust = 1.1) +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())
}
cor_all_data <- function(rnaseq, rppa, viper) {
# scale all
rnaseq <- t(scale(t(rnaseq)))
rppa <- t(scale(t(rppa)))
viper <- t(scale(t(viper)))
cor_df <- data.frame(feature = character(),
cor_val = numeric(),
pval = numeric(),
type = character())
# rnaseq rppa
genes <- rownames(rnaseq)
cor_res <- lapply(1:length(genes), function(k){
fit <- cor.test(as.numeric(rnaseq[rownames(rnaseq) == genes[k], ]),
as.numeric(rppa[rownames(rppa) == genes[k], ]),
method = "pearson",
exact = FALSE)
################################ comment: add p-value here ############################
data.frame(feature = genes[k],
cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RNA-Seq vs RPPA"
cor_df <- rbind(cor_df, cor_gene_res)
# rnaseq viper
genes <- rownames(rnaseq)
cor_res <- lapply(1:length(genes), function(k){
fit <- cor.test(as.numeric(rnaseq[rownames(rnaseq) == genes[k], ]),
as.numeric(viper[rownames(viper) == genes[k], ]),
method = "pearson",
exact = FALSE)
################################ comment: add p-value here ############################
data.frame(feature = genes[k],
cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RNA-Seq vs VIPER"
cor_df <- rbind(cor_df, cor_gene_res)
# rppa viper
genes <- rownames(rppa)
cor_res <- lapply(1:length(genes), function(k){
fit <- cor.test(as.numeric(rppa[rownames(rppa) == genes[k], ]),
as.numeric(viper[rownames(viper) == genes[k], ]),
method = "pearson",
exact = FALSE)
################################ comment: add p-value here ############################
data.frame(feature = genes[k],
cor_val = fit$estimate,
pval = fit$p.value
)
})
cor_gene_res <- do.call(rbind, cor_res)
#################### comment: compute adjusted p-value ##########################
cor_gene_res$padj <- p.adjust(cor_gene_res$pval, "fdr")
cor_gene_res$type <- "RPPA vs VIPER"
cor_df <- rbind(cor_df, cor_gene_res)
p_meds <- ddply(cor_df, .(type), summarise, med = round(median(cor_val), digits = 4))
cor_df$type <- factor(cor_df$type , levels=c("RNA-Seq vs VIPER", "RNA-Seq vs RPPA", "RPPA vs VIPER"))
p <- ggplot(cor_df, aes(x = type, y = cor_val, fill = type)) +
geom_boxplot(color = "black", width = 0.5) +
labs(x = "Data", y = "Pearson Correlation Value") +
theme(axis.text.x = element_text(size = 8), legend.position = "none") +
geom_text(data = p_meds, aes(x = type, y = med, label = med),
size = 3, vjust = -1)
return(p)
}
args(map_entrez)
gene_annot <- ccle_rnaseq_gene_annot
load(file = "C:/DrugVIPER/Data/Analysis_Tasks_data.rda")
gene_annot <- ccle_rnaseq_gene_annot
exp <- ccle_rnaseq_exp
gene_entrez <- gene_entrez
# add row numbers to retain order later
gene_annot$id <- seq.int(nrow(gene_annot))
head(gene_annot)
# add entrez
gene_annot_entrez <- left_join(as.data.frame(gene_annot), gene_entrez[ ,   c("gene_id", "entrez_id")], by = join_by("gene_id"))
# add entrez  by = join_by("gene_id")
gene_annot_entrez <- left_join(as.data.frame(gene_annot), gene_entrez[ ,   c("gene_id", "entrez_id")])
head(gene_annot_entrez)
# remove rows with NA for entrez
gene_annot_entrez <- gene_annot_entrez[!is.na(gene_annot_entrez$entrez_id), ]
# matching assays for rows with entrez id
exp_entrez <- exp[gene_annot_entrez$id, ]
# make entrez id the rownames
row.names(exp_entrez) <- gene_annot_entrez$entrez_id
return (list(gene_annot_entrez, exp_entrez))
map_entrez <- function(gene_annot, exp, gene_entrez) {
# add row numbers to retain order later
gene_annot$id <- seq.int(nrow(gene_annot))
# add entrez  by = join_by("gene_id")
gene_annot_entrez <- left_join(as.data.frame(gene_annot), gene_entrez[ ,   c("gene_id", "entrez_id")])
# remove rows with NA for entrez
gene_annot_entrez <- gene_annot_entrez[!is.na(gene_annot_entrez$entrez_id), ]
# matching assays for rows with entrez id
exp_entrez <- exp[gene_annot_entrez$id, ]
# make entrez id the rownames
row.names(exp_entrez) <- gene_annot_entrez$entrez_id
return (list(gene_annot_entrez, exp_entrez))
}
####################### comment: error by using by = join_by("transcript_id") #################################
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez) # , by=join_by("transcript_id")
gene_entrez <- na.omit(distinct(gene_entrez[, c("gene_name", "gene_id", "entrez_id")]))
# add entrez ids
ccle_rnaseq_mapped <- map_entrez(ccle_rnaseq_gene_annot, ccle_rnaseq_exp, gene_entrez)
ccle_rnaseq_gene_annot_entrez <- ccle_rnaseq_mapped[[1]] # gene_annot
ccle_rnaseq_exp_entrez <- ccle_rnaseq_mapped[[2]]  # exp
gcsi_rnaseq_mapped <- map_entrez(gcsi_rnaseq_gene_annot, gcsi_rnaseq_exp, gene_entrez)
gcsi_rnaseq_gene_annot_entrez <- gcsi_rnaseq_mapped[[1]] # gene_annot
gcsi_rnaseq_exp_entrez <- gcsi_rnaseq_mapped[[2]]  # exp
# map gencode_entrez to gene_id (include gene_name for naming later)
################### comment: , by = join_by("transcript_id")
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez)
gene_entrez <- na.omit(distinct(gene_entrez[, c("gene_name", "gene_id", "entrez_id")]))
# add entrez ids
ccle_rppa_mapped <- map_entrez(ccle_rppa_gene_annot, ccle_rppa_exp, gene_entrez)
ccle_rppa_gene_annot_entrez <- ccle_rppa_mapped[[1]] # gene_annot
ccle_rppa_exp_entrez <- ccle_rppa_mapped[[2]]  # exp
# aggregate ccle rppa by row names (take median)
ccle_rppa_exp_entrez <- aggregate(ccle_rppa_exp_entrez, list(row.names(ccle_rppa_exp_entrez)), median, na.rm = TRUE)
row.names(ccle_rppa_exp_entrez) <- ccle_rppa_exp_entrez$Group.1
ccle_rppa_exp_entrez <- ccle_rppa_exp_entrez[, -1]
# map gencode_entrez to gene_id (include gene_name for naming later)
################################### comment , by = join_by("transcript_id")
gene_entrez <- left_join(as.data.frame(gencode_basic)[, c("gene_name", "gene_id", "transcript_id")], gencode_entrez)
gene_entrez <- na.omit(distinct(gene_entrez[, c("gene_name", "gene_id", "entrez_id")]))
# add entrez ids
mclp_rppa_mapped <- map_entrez(mclp_rppa_gene_annot, mclp_rppa_exp, gene_entrez)
mclp_rppa_gene_annot_entrez <- mclp_rppa_mapped[[1]] # gene_annot
mclp_rppa_exp_entrez <- mclp_rppa_mapped[[2]]  # exp
# Will replace using hgnc_complete_set.txt
# hgnc annotations
hgnc.complete.set <- read.delim("C:/DrugVIPER/Data/hgnc_complete_set.txt")
sub.hgnc.complete.set <- hgnc.complete.set[!is.na(hgnc.complete.set$entrez_id), ]
# ccle rnaseq
# ensembl to entrez id
ensembl_entrez_gene_id <- sapply(1:nrow(ccle_rnaseq_gene_annot), function(k){
# splitting to remove version number
hgnc.complete.set[hgnc.complete.set$ensembl_gene_id == unlist(strsplit(ccle_rnaseq_gene_annot$gene_id[k], ".", fixed = TRUE))[1], "entrez_id"]
})
# ccle rnaseq
# ensembl to entrez id
ensembl_entrez_gene_id <- sapply(1:nrow(ccle_rnaseq_gene_annot), function(k){
# splitting to remove version number
hgnc.complete.set[hgnc.complete.set$ensembl_gene_id == unlist(strsplit(ccle_rnaseq_gene_annot$gene_id[k], ".", fixed = TRUE))[1], "entrez_id"]
})
rownames(ccle_rnaseq_exp) <- as.numeric(ensembl_entrez_gene_id)
# ccle rppa - some ensembl_gene_ids are missing
ensembl_entrez_gene_id <- sapply(1:nrow(ccle_rppa_gene_annot), function(k){
if (ccle_rppa_gene_annot$gene_id[k] == "") {
# ensembl id missing in gene annotations, will use symbol to match
hgnc.complete.set[hgnc.complete.set$symbol == ccle_rppa_gene_annot$Target_Genes[k], "entrez_id"]
} else {
hgnc.complete.set[hgnc.complete.set$ensembl_gene_id == unlist(strsplit(ccle_rppa_gene_annot$gene_id[k], ".", fixed = TRUE))[1], "entrez_id"]
}
})
rownames(ccle_rppa_exp) <- as.numeric(ensembl_entrez_gene_id)
# mclp rppa
ensembl_entrez_gene_id <- sapply(1:nrow(mclp_rppa_gene_annot), function(k){
hgnc.complete.set[hgnc.complete.set$ensembl_gene_id == unlist(strsplit(mclp_rppa_gene_annot$gene_id[k], ".", fixed = TRUE))[1], "entrez_id"]
})
rownames(mclp_rppa_exp) <- as.numeric(ensembl_entrez_gene_id)
# list of available regulons from aracne.networks
available_regulons <- data(package="aracne.networks")$results[, "Item"] # regulons in aracne.networks
all_regulons <- lapply(available_regulons, base::get)
all_regulons
dim(ccle_rnaseq_exp)
ccle_rnaseq_exp[1:3,1:4]
dim(gcsi_rnaseq_exp)
ccle_viper_scores <- viper(ccle_rnaseq_exp, all_regulons, verbose = FALSE)
ccle_viper_scores <- viper(ccle_rnaseq_exp, all_regulons, verbose = FALSE)
ccle_viper_scores <- viper(ccle_rnaseq_exp, all_regulons, verbose = FALSE)
ls()
rm(list=ls())
library(ggplot2)
library(tidyverse)
library(dplyr)
library(SummarizedExperiment)
library(miscTools)
library(corrplot)
library(reshape2)
library(ggvenn)
library(PharmacoGx)
library(gridExtra)
library(AnnotationGx)
knitr::opts_chunk$set(echo = TRUE)
rem <- function(x){
x <- as.matrix(x)
x <- t(apply(x,1,as.numeric))
# data is log2(TPM+0.001)
r <- as.numeric(apply(x, 1, function(i) sum(round(i, 6) == round(log2(0.001), 6)) ))
remove <- which(r > dim(x)[2]*0.5) # should be 0.5 or less or more for our data?
return(remove)
}
CCLE_RPPA_SE <- readRDS("C:/DrugVIPER/results/CCLE_RPPA_SE.rds")
CCLE_RNAseq_SE <- readRDS("C:/DrugVIPER/Data/CCLE_RNAseq.rds")
MCLP_RPPA_SE <- readRDS("C:/DrugVIPER/results/MCLP_RPPA_SE.rds")
gCSI_RNAseq_SE <- readRDS("C:/DrugVIPER/Data/gCSI_RNAseq.rds")
# Note: replacing CCLE_RNAseq_SE and gCSI_RNAseq_SE with local downloads (downloading timeouts)
#CCLE <- downloadPSet("CCLE_2015")
#gCSI <- downloadPSet("gCSI_2019")
CCLE_RNAseq_SE <- summarizeMolecularProfiles(CCLE, mDataType="Kallisto_0.46.1.rnaseq", fill.missing = FALSE)
CCLE <- readRDS("C:/DrugVIPER/Data/CCLE_RNAseq.rds")
gCSI <- readRDS("C:/DrugVIPER/Data/gCSI_RNAseq.rds")
# Note: replacing CCLE_RNAseq_SE and gCSI_RNAseq_SE with local downloads (downloading timeouts)
#CCLE <- downloadPSet("CCLE_2015")
#gCSI <- downloadPSet("gCSI_2019")
CCLE_RNAseq_SE <- summarizeMolecularProfiles(CCLE, mDataType="Kallisto_0.46.1.rnaseq", fill.missing = FALSE)
library(PharmacoGx)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("PharmacoGx")
library(PharmacoGx)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CoreGx")
library(CoreGx)
install.packages("htmltools")
library(htmltools)
library(CoreGx)
install.packages("rlang")
install.packages("rlang")
library(rlang)
library(CoreGx)
library(CoreGx)
install.packages("rlang")
library(rlang)
library(CoreGx)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CoreGx")
library(CoreGx)
library(PharmacoGx)
library(ggplot2)
library(tidyverse)
library(dplyr)
library(SummarizedExperiment)
library(miscTools)
library(corrplot)
library(reshape2)
library(ggvenn)
library(PharmacoGx)
library(gridExtra)
knitr::opts_chunk$set(echo = TRUE)
library(AnnotationGx)
install.packages("cli")
install.packages("cli")
library(AnnotationGx)
library(cli)
library(AnnotationGx)
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=TRUE)
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=TRUE, force = TRUE)
library(AnnotationGx)
remotes::install_github("bhklab/AnnotationGx", build_manual=TRUE, build_vignettes=TRUE)
remotes::install_github("bhklab/AnnotationGx@development", build_manual=TRUE, build_vignettes=TRUE)
library(PharmacoGx)
knitr::opts_chunk$set(echo = TRUE)
rem <- function(x){
x <- as.matrix(x)
x <- t(apply(x,1,as.numeric))
# data is log2(TPM+0.001)
r <- as.numeric(apply(x, 1, function(i) sum(round(i, 6) == round(log2(0.001), 6)) ))
remove <- which(r > dim(x)[2]*0.5) # should be 0.5 or less or more for our data?
return(remove)
}
install_github("DeveloperName/PackageName")
library(devtools)
install_github("DeveloperName/PackageName")
install_github("bhklab/PredictioR")
devtools::install_github("bhklab/PredictioR")
rm(list=ls())
library(devtools)
library(roxygen2)
library(testthat)
library(usethis)
getwd()
devtools::create("PredictioR")
devtools::create("C:/ImmuneData/PredictioR")
devtools::create("C:/ImmuneData")
x <- c(1:10)
devtools::use_data(x)
getwd()
library(devtools)
library(roxygen2)
devtools::document()
devtools::document("C:/SignatureSets")
devtools::document("C:/SignatureSets/ADO")
roxygen2::roxygenise("C:/SignatureSets")
usethis::use_package_doc()
getwd("C:/SignatureSets")
setwd("C:/SignatureSets")
usethis::use_package_doc()
?roxygen2
roxygen2::roxygenise("ADO")
roxygen2::roxygenise()
rlang::last_trace()
roxygen2::roxygenise()
roxygen2::roxygenise("ADO")
getwd()
usethis::use_vignette()
usethis::use_vignette("SignatureSets")
app_dir <- str_split(rstudioapi::getActiveDocumentContext()$path,'SignatureSets')[[1]][1]
library(stringr)
app_dir <- str_split(rstudioapi::getActiveDocumentContext()$path,'SignatureSets')[[1]][1]
dir_GeneSig <- file.path(app_dir, 'SignatureSets', 'data')
dir_GeneSig_info <- file.path(app_dir, 'SignatureSets', 'data-raw')
signature <- read.csv( file.path(dir_GeneSig_info, "signature_INFO.csv"))
signature$Signature <- as.character( signature$Signature )
signature$method <- as.character( signature$method )
i<- which(signature$Signature == "APM_Thompson")
load(file.path(dir_GeneSig, "APM_Thompson.rda"))
sig
knitr::kable(sig, format="html",
table.attr = "style='width:30%;'", row.names = FALSE) %>% kableExtra::kable_styling()
i<- which(signature$Signature == "T_cell_exclusion")
load(file.path(dir_GeneSig, "T_cell_exclusion.rda"))
sig
signature
i<-10
i<- which(signature$Signature == "APM_Thompson")
load(file.path(dir_GeneSig, "APM_Thompson.rda"))
i<- which(signature$Signature == "COX_IS")
load(file.path(dir_GeneSig, "COX_IS.rda"))
knitr::kable(sig, format="html",
table.attr = "style='width:30%;'", row.names = FALSE) %>% kableExtra::kable_styling()
i<- which(signature$Signature == "APM_Thompson")
load(file.path(dir_GeneSig, "APM_Thompson.rda"))
knitr::kable(sig, format="html",
table.attr = "style='width:30%;'", row.names = FALSE) %>% kableExtra::kable_styling()
file.path(dir_GeneSig_info, "clustering_signatures.JPG")
knitr::include_graphics(file.path(dir_GeneSig_info, "clustering_signatures.JPG"))
i<- which(signature$Signature == "APM_Thompson")
load(file.path(dir_GeneSig, "APM_Thompson.rda"))
sig
class(sig)
load(file.path(dir_GeneSig, "APM_Thompson.rda"))
sig
knitr::kable(sig, format="html", row.names = FALSE) %>% kableExtra::kable_styling()