# load libraries
library(tidyverse)
library(rkip)
library(org.Hs.eg.db)
library(cgdsr)
library(cRegulome)
# download data
## atlas expression data
if(!file.exists('data/atlas_expression.tsv')) {
download.file(
url = 'https://www.ebi.ac.uk/gxa/experiments-content/E-GEOD-3325/resources/DifferentialSecondaryDataFiles.Microarray/normalized-expressions',
destfile = 'data/atlas_expression.tsv'
)
}
if(!file.exists('data/atlas_design.tsv')) {
download.file(
url = 'https://www.ebi.ac.uk/gxa/experiments-content/E-GEOD-3325/resources/ExperimentDesignFile.Microarray/experiment-design',
destfile = 'data/atlas_design.tsv'
)
}
## annotation data
if(!file.exists('data/annotations.tsv')) {
ann <- annotation_get(
go_id = c('GO:0001837', 'GO:0006914', 'GO:0008429'),
go_names = c('emt', 'autophagy', 'pebp'),
org_db = org.Hs.eg.db,
columns = 'SYMBOL',
remove_predicted = FALSE
)
write_tsv(ann, 'data/annotations.tsv')
}
# get cancer studies
cgd <- CGDS('http://www.cbioportal.org/')
#studies_ids <- getCancerStudies(cgd) %>%
# filter(grepl('prad', cancer_study_id)) %>%
# pull(cancer_study_id)
#map(studies_ids,
# function(x) {
# getGeneticProfiles(cgd,
# cancerStudy = x)
# })
#map(studies_ids,
# function(x) {
# getCaseLists(cgd,
# cancerStudy = x)
# })
study_profile <- list(
prad_su2c_2015 = 'prad_su2c_2015_rna_seq_mrna',
prad_broad_2013 = 'prad_broad_2013_mrna',
prad_broad = 'prad_broad_mrna',
prad_fhcrc = 'prad_fhcrc_rna_seq_mrna',
prad_mskcc = 'prad_mskcc_mrna_zbynorm',
prad_tcga_pub = 'prad_tcga_pub_rna_seq_v2_mrna',
prad_tcga = 'prad_tcga_rna_seq_v2_mrna',
prad_mskcc_cheny1_organoids_2014 = 'prad_mskcc_cheny1_organoids_2014_rna_seq_rna'
)
ann <- read_tsv('data/annotations.tsv')
gene_ids <- unique(ann$symbol)
imap(study_profile, function(x, .y) {
fl <- paste('data/', .y, '.tsv', sep = '')
if(!file.exists(fl)) {
getProfileData(cgd,
genes = gene_ids,
geneticProfiles = x,
caseList = paste(.y, 'all', sep = '_')) %>%
write_tsv(path = fl)
}
return(NULL)
})
# get string interacitons
if(!file.exists('data/string_interactions.tsv')) {
df <- interactions_get(data.frame(symbol = unique(ann$symbol)),
input_directory = 'data/',
species = 9606,
evidence = TRUE)
write_tsv(df, 'data/string_interactions.tsv')
}
# get tf data
#tf <- c('ERCC6', 'VEZF1')
#map(tf, function(x) {
# fl <- paste('data/', x, '_cor.tsv', sep = '')
# if(!file.exists(fl)) {
# url <- paste('http://cistrome.org/CistromeCancer/CancerTarget/examples/',
# x, '.cor.csv',
# sep = '')
# read_csv(url) %>%
# write_tsv(path = fl)
# }
# return(NULL)
#})
#map(tf, function(x) {
# fl <- paste('data/', x, '_rp.tsv', sep = '')
# if(!file.exists(fl)) {
# url <- paste('http://cistrome.org/CistromeCancer/CancerTarget/examples/',
# x, '.rp.csv',
# sep = '')
# read_csv(url) %>%
# write_tsv(path = fl)
# }
# return(NULL)
#})
# predicted miRNA
#url <- 'http://www.targetscan.org/vert_72/temp/TargetScan_7.2_ENST00000261313.2_predicted_targeting_details.txt'
#if(!file.exists('data/predicted_targeting.tsv')) {
# download.file(url,
# destfile = 'data/predicted_targeting.tsv')
#}
if(!file.exists('data/cRegulome.db')) {
get_db(test = FALSE, destfile = 'data/cRegulome.db.gz')
}
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