# script for plots included in the article
#### installations ####
# source("https://bioconductor.org/biocLite.R")
# biocLite("BiocInstaller")
# # 1) library(devtools); install_github('RTCGA/RTCGA') # dev installation
# # 2) biocLite("RTCGA") # release installation
#
# biocLite("RTCGA.mutations")
# biocLite("RTCGA.clinical")
#
# devtools::install_github('cttobin/ggthemr')
# install_github('kassambara/survminer')
#### theme set up ####
library(RTCGA)
library(ggthemr)
ggthemr('dust')
#### survival curves ####
library(RTCGA.mutations)
library(dplyr)
# library(survminer)
mutationsTCGA(BRCA.mutations, OV.mutations) %>%
filter(Hugo_Symbol == 'TP53') %>%
filter(substr(bcr_patient_barcode, 14, 15) ==
"01") %>% # cancer tissue
mutate(bcr_patient_barcode =
substr(bcr_patient_barcode, 1, 12)) ->
BRCA_OV.mutations
library(RTCGA.clinical)
survivalTCGA(
BRCA.clinical,
OV.clinical,
extract.cols = "admin.disease_code"
) %>%
dplyr::rename(disease = admin.disease_code) ->
BRCA_OV.clinical
BRCA_OV.clinical %>%
left_join(
BRCA_OV.mutations,
by = "bcr_patient_barcode"
) %>%
mutate(TP53 =
ifelse(!is.na(Variant_Classification), "Mut","WILDorNOINFO")) ->
BRCA_OV.clinical_mutations
BRCA_OV.clinical_mutations %>%
select(times, patient.vital_status, disease, TP53) -> BRCA_OV.2plot
kmTCGA(
BRCA_OV.2plot,
explanatory.names = c("TP53", "disease"),
break.time.by = 400,
xlim = c(0,2000),
pval = TRUE,
tables.height = 0.4,
ggtheme = NULL) -> km_plot
#ggtheme set with ggthemr
km_plot$plot <- km_plot$plot + guides(col=guide_legend(nrow=2,bycol=TRUE))
pdf("devel/bioinfo_article/figures/surv.pdf", width = 10*2/3, height = 9*2/3, onefile=FALSE)
print(km_plot)
dev.off()
#### PCA ####
library(RTCGA.rnaseq)
# library(dplyr) if did not load at start
expressionsTCGA(BRCA.rnaseq, OV.rnaseq, HNSC.rnaseq) %>%
dplyr::rename(cohort = dataset) %>%
filter(substr(bcr_patient_barcode, 14, 15) == "01") -> BRCA.OV.HNSC.rnaseq.cancer
pcaTCGA(BRCA.OV.HNSC.rnaseq.cancer, "cohort", ggtheme = NULL) -> pca_plot
pdf("devel/bioinfo_article/figures/pca.pdf", width = 10*2/3, height = 9*2/3, onefile=FALSE)
pca_plot + theme(legend.position = "top")
dev.off()
#### boxplots ####
# library(RTCGA.rnaseq)
# # perfrom plot
# # library(dplyr) if did not load at start
# expressionsTCGA(
# ACC.rnaseq,
# BLCA.rnaseq,
# BRCA.rnaseq,
# OV.rnaseq,
# extract.cols = "MET|4233"
# ) %>%
# dplyr::rename(
# cohort = dataset,
# MET = `MET|4233`
# ) %>% #cancer samples
# filter(
# substr(bcr_patient_barcode, 14, 15) == "01"
# ) -> ACC_BLCA_BRCA_OV.rnaseq
# boxplotTCGA(
# ACC_BLCA_BRCA_OV.rnaseq,
# "reorder(cohort,log1p(MET), median)",
# "log1p(MET)",
# xlab = "Cohort Type",
# ylab = "Logarithm of MET",
# legend.title = "Cohorts",
# legend = "bottom",
# ggtheme = NULL
# ) -> boxplot1
#
#
# pdf("devel/bioinfo_article/figures/boxplot.pdf", width = 10*2/3, height = 9*2/3, onefile=FALSE)
# boxplot1 + theme(legend.position = "top")
# dev.off()
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