/
s_gene_data_class.R
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s_gene_data_class.R
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#setwd('~/projects/expression_var/R/')
library(methods)
#source('~/R/s_function.R', chdir = T)
library(futile.logger)
source('s_project_funcs.R')
library(stringr)
library(plyr)
#library(tidyr)
GENE <- setRefClass("GENE",
fields = list( data = "data.frame",
gene_name = "character",
batch_name = 'character',
chr_str = 'character'),
methods = list(
get_samples = function(input_data = NULL, invert = FALSE){
if (is.null(input_data)){
sample_cols = sort(grep('(NA|HG)[0-9]+', colnames(data),value = T, invert = invert))
}else{
sample_cols = sort(grep('(NA|HG)[0-9]+', colnames(input_data),value = T, invert = invert))
}
return (sample_cols)
},
subset_features_to_snp_contain_region = function(batch_mode, debug = F){
#Subset the features only to the SNP regions
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@3@"]]))##:ess-bp-end:##
}
set_feature_id()
if ( !grepl('TFsnpMatch', batch_mode)) return (NULL)
if (batch_name %in% c('445samples_sailfish')){
other_batch_name = '445samples_snpOnly'
}else{
#if (grepl('Conserve', batch_name)){
# other_batch_name = str_replace(batch_name, 'Conserve[0-9]+', 'Conserve0')
#}else{
other_batch_name = f_p('%s_snpOnly', str_replace(batch_name, '(_rareVar|Conserve.*|RareConserve.*)', ''))
#}
}
snp_gene <- GENE(data = data.frame(), gene_name = gene_name, chr_str = chr_str, batch_name = other_batch_name)
snp_gene$read_data()
snp_gene$rmdup_based_on_features()
snp_gene$set_feature_id()
snp_tf_features = grep('enhancer|promoter', rownames(snp_gene$data), value =T)
loc_tf_features = grep('enhancer|promoter', rownames(data), value = T)
rare_tf_features=setdiff(loc_tf_features, snp_tf_features)
if (!grepl('rareVar', batch_name)){
f_ASSERT( length(setdiff(f_simple_locs(snp_tf_features), f_simple_locs(loc_tf_features))) == 0, 'SNP TF features are bigger than All TF features' )
}
flog.info('Remove rare features: %s out of %s', length(rare_tf_features), length(loc_tf_features))
selected_data = data[! (rownames(data) %in% rare_tf_features),]
data <<- selected_data
},
shuffle_rare_features_out_of_snp_contain_region = function(batch_mode, debug = F){
#Subset the features only to the SNP regions
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@3@"]]))##:ess-bp-end:##
}
set_feature_id()
if ( !grepl('TFrareShuffle|TFrarePromoter', batch_mode)) return (NULL)
if (batch_name %in% c('445samples_sailfish')){
other_batch_name = '445samples_snpOnly'
}else{
#if (grepl('Conserve', batch_name)){
# other_batch_name = str_replace(batch_name, 'Conserve[0-9]+', 'Conserve0')
#}else{
other_batch_name = f_p('%s_snpOnly', str_replace(batch_name, '(_rareVar|Conserve.*|RareConserve.*)', ''))
#}
}
snp_gene <- GENE(data = data.frame(), gene_name = gene_name, chr_str = chr_str, batch_name = other_batch_name)
snp_gene$read_data()
#snp_gene$rmdup_based_on_features()
snp_gene$set_feature_id()
snp_gene$print_types()
snp_tf_features = grep('enhancer|promoter', rownames(snp_gene$data), value =T)
loc_tf_features = grep('enhancer|promoter', rownames(data), value = T)
rare_tf_features=setdiff(loc_tf_features, snp_tf_features)
if (!grepl('rareVar', batch_name)){
f_ASSERT( length(setdiff(f_simple_locs(snp_tf_features), f_simple_locs(loc_tf_features))) == 0, 'SNP TF features are bigger than All TF features' )
}
set.seed(2)
sample_cols = grep('t_',get_samples(), value = T, invert = TRUE)
if (length(rare_tf_features) > 0){
new_data = data
if (batch_mode == 'TFrareShuffle'){
flog.info('Shuffle rare features: %s out of %s', length(rare_tf_features), length(loc_tf_features))
permutate_names = sample(x =sample_cols, size = length(sample_cols))
#sample_data = new_data[, sample_cols]
#sample_data[is.na(sample_data)] = 0
#new_data[,sample_cols] = sample_data
new_data[rare_tf_features, sample_cols] = data[rare_tf_features, permutate_names]
}else if(batch_mode == 'TFrarePromoter'){
rare_enh = grep('enhancer', rare_tf_features, value = T)
flog.info('Enhancer rare features: %s out of %s', length(rare_enh), length(loc_tf_features))
print(table(data$type))
new_data = data[!(rownames(new_data) %in% rare_enh), ]
print(table(new_data$type))
}
data <<- new_data
return (rare_tf_features)
}
},
set_na_to_zero = function(){
new_data = data
sample_cols = get_samples()
sample_data = new_data[, sample_cols]
sample_data[is.na(sample_data)] = 0
new_data[,sample_cols] = sample_data
data <<- new_data
},
rmdup_based_on_features = function(){
new_data <- data %>% dplyr::arrange(gene, -cor ) %>% distinct(feature, feature_start, feature_end, gene, .keep_all = T) %>% as.data.frame
flog.info('New: %s out of old %s', nrow(new_data), nrow(data))
data <<- new_data
},
set_feature_id = function(){
index = paste(data$type, data$feature, data$hic_fragment_id, data$pair, data$feature_start, data$feature_end ,sep=':')
duplicated_index = index[duplicated(index)]
merge_cols = c('feature','feature_start', 'feature_end', 'hic_fragment_id', 'type', 'pair')
f_ASSERT( all(grepl('SNP', duplicated_index)), 'Duplicated snps error')
flog.info('Number of duplicated SNPs %s', length(duplicated_index))
data <<- data[!duplicated(data[,merge_cols]),] #Remove the SNPs with same rs names. Potential problem
rownames(data) <<- paste(data$type, data$feature, data$hic_fragment_id, data$pair, data$feature_start, data$feature_end ,sep=':')
},
get_validation_samples = function(other_info){
validation_samples = read.table('./data/raw_data/CEU47/validating_samples.txt', header = T)
if (grepl('ctcf', other_info, ignore.case = T)){
return (validation_samples$ctcf)
}else if(grepl('pu1', other_info, ignore.case = T)){
return (validation_samples$pu1)
}else{
return (NULL)
}
},
hic_within_1Mb = function(return_pos = FALSE){
all.entrezgene = read.table('./data/raw_data/rnaseq/all.ensemble.genes.gene_start',sep='\t', header = TRUE, quote = "")
row.names(all.entrezgene) = all.entrezgene$ensembl_transcript_id
loc_gene_name = str_replace(gene_name, '[.][0-9]+', '')
gene_start = all.entrezgene[loc_gene_name, 'transcript_start'] - 1000000
gene_end = all.entrezgene[loc_gene_name, 'transcript_end'] + 1000000
hic_ids = as.numeric(data$hic_fragment_id)
hic_ids[is.na(hic_ids)] = all.entrezgene[loc_gene_name, 'transcript_start'] #For the SNPs only overlap with hic-id.
subset_data = data[ hic_ids > gene_start & hic_ids < gene_end,]
flog.info('Filtered %s features out of 1Mb regions', nrow(data) - nrow(subset_data))
data <<- subset_data
if (return_pos == TRUE){
return (c(gene_start, gene_end))
}
},
read_test_data = function(test_batch, test_individuals = NULL, debug = FALSE) {
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@3@"]]))##:ess-bp-end:##
}
#Seperate part of samples for final validation
if (!is.null(test_individuals)){
part_data = data[, setdiff(colnames(data), test_individuals)]
shared_data = data[, intersect(colnames(data), test_individuals)]
colnames(shared_data) = paste0('t_', colnames(shared_data))
data <<- cbind(part_data, shared_data)
return (TRUE)
}
#Read additional data.
output_dir = f_p('./data/%s/', test_batch)
expression_file = f_p('%s/rnaseq/%s/%s.txt', output_dir, chr_str, test_gene_name())
if ( !file.exists(expression_file) ){
flog.info('Missing testing data! %s', expression_file)
return (FALSE)
}
test_data <- read.table(expression_file, header = T, na.strings = 'NA')
sample_cols = get_samples(test_data)
flog.info('Test samples: %s', length(sample_cols))
#colnames(test_data) = paste0('t_', sample_cols)
if ('cor' %in% colnames(data)){
test_data <- test_data[,c('chr', 'start', 'end', 'gene','feature',
'feature_start', 'feature_end', 'hic_fragment_id' ,
'type', 'cor', 'pair', sample_cols )]
}else{
test_data <- test_data[,c('chr', 'start', 'end', 'gene','feature',
'feature_start', 'feature_end', 'hic_fragment_id' ,
'type', sample_cols )]
}
merge_cols = c('feature','feature_start', 'feature_end', 'hic_fragment_id', 'type', 'pair')
test_merge_data = test_data[!duplicated(test_data[, merge_cols]),c(merge_cols,sample_cols)]
test_samples = paste0('t_', sample_cols)
colnames(test_merge_data) = c(merge_cols, test_samples )
merge_data = merge(data, test_merge_data, by = merge_cols, all.x = TRUE)
f_assert(nrow(merge_data) == nrow(data), 'Merge error')
inserted_data = merge_data[, test_samples]
duplicated_rows=f_duplicated_cols(data[, merge_cols])
missing_data = merge_data[rowSums(is.na(inserted_data)) == length(test_samples),merge_cols]
flog.info('Missing features in the test data: %s', nrow(missing_data))
data <<- merge_data
return (TRUE)
},
test_gene_name = function(){
return (str_replace(gene_name, '.[0-9]+$',''))
},
read_data = function(debug=FALSE) {
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@2@"]]))##:ess-bp-end:##
}
output_dir = f_p('./data/%s/', batch_name)
expression_file = f_p('%s/rnaseq/%s/%s.txt', output_dir, chr_str, gene_name)
result <- try(
data <<- read.table(f_p('%s.gz', expression_file), header = T, na.strings = 'NA'),
silent = TRUE
)
if (class(result) == "try-error"){
cat(f_p('Read %s.gz failed ', expression_file), '\n')
data <<- read.table(expression_file, header = T, na.strings = 'NA')
}
sample_cols = get_samples()
flog.info('Number of samples: %s, features %s', length(sample_cols), nrow(data))
if ('cor' %in% colnames(data)){
data <<- data[,c('chr', 'start', 'end', 'gene','feature',
'feature_start', 'feature_end', 'hic_fragment_id' ,
'type', 'cor', 'pair', sample_cols )]
}else{
data <<- data[,c('chr', 'start', 'end', 'gene','feature',
'feature_start', 'feature_end', 'hic_fragment_id' ,
'type', sample_cols )]
}
},
change_expression =function(new_batch_name, batch_mode = 'TF', debug = FALSE) {
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@2@"]]))##:ess-bp-end:##
}
#Change the mRNA values to another normalization method,
#e.g. quantile to peer normalization.
if (!is.null(new_batch_name)){
if (batch_mode != 'random'){
new_data = read.table(f_p('./data/%s/rnaseq/GEUVADIS.Gene.DATA_MATRIX', new_batch_name), header = TRUE)
}else{
new_data = shuffle_gene_expression(new_batch_name)
}
rownames(new_data) = new_data$gene
samples = get_samples()
mRNA_index = which(data$type == 'gene')
name_shared = intersect(samples , colnames(new_data))
name_diff = setdiff(samples , colnames(new_data))
flog.info('Set diff: %s', paste(name_diff, sep = '', collapse = '.'))
if (grepl('randomN[0-9]+', batch_mode)){
seed_num=f_extract_pattern_nums(batch_mode,'randomN')
set.seed(seed_num)
flog.info('Set random seed: %s', seed_num)
permutate_names = sample(x =name_shared, size = length(name_shared))
data[mRNA_index, name_shared] <<- new_data[gene_name, permutate_names]
}else{
data[mRNA_index, name_shared] <<- new_data[gene_name, name_shared]
}
}else{
flog.info('New batch_name is null: %s', new_batch_name)
}
},
add_snp_location = function(loc_batch_name)
{
if(!grepl('SNP', loc_batch_name, ignore.case = T)){
return (NULL)
}
promoter_hic = unique(subset(data, type %in% c( 'promoter'))$hic_fragment_id)
snp_data = data %>% filter(type == 'SNP')
promoter_snps = data$type == 'SNP' & data$hic_fragment_id %in% promoter_hic
enhancer_snps = (data$type == 'SNP' &!( data$hic_fragment_id %in% promoter_hic))
new_data = data
new_data[promoter_snps,'feature'] = paste0(data[promoter_snps,'feature'],'.pro')
new_data[enhancer_snps,'feature'] = paste0(data[enhancer_snps,'feature'],'.enh')
data <<- new_data
},
shuffle_gene_expression = function(loc_batch_name){
shuffle_file = f_p('./data/%s/rnaseq/GEUVADIS.GeneRandom.DATA_MATRIX', loc_batch_name)
if(!file.exists(shuffle_file)){
loc_data = read.table(f_p('./data/%s/rnaseq/GEUVADIS.Gene.DATA_MATRIX', loc_batch_name), header = TRUE)
set.seed(897)
permutate_names = sample(x =1:nrow(loc_data), size = nrow(loc_data))
random_data = loc_data
random_data$gene = loc_data[permutate_names, 'gene']
write.table(random_data, file = shuffle_file, quote = FALSE, sep = ' ', row.names = FALSE, col.names = TRUE)
flog.info('Write random expression file: %s', shuffle_file)
}else{
random_data = read.table(shuffle_file, header = TRUE)
}
return (random_data)
},
print_types = function(){
print(table(data$type))
},
subset_snps_in_tf_regions = function(batch_mode, debug = F){
if (f_judge_debug(debug)){
##:ess-bp-start::browser@nil:##
browser(expr=is.null(.ESSBP.[["@2@"]]))##:ess-bp-end:##
}
if ( !grepl( 'SNPinTF', batch_mode)) return (NULL)
regulatory_regions = subset(data, type == 'promoter' | type == 'enhancer')[, c('chr', 'feature_start', 'feature_end', 'feature')]
tf_binding_regions = regulatory_regions[grep('H3K', regulatory_regions$feature, invert = T),]
SNP_regions = subset(data, type == 'SNP')[, c('chr', 'feature_start', 'feature_end', 'feature')]
tf_bed = makeGRangesFromDataFrame(tf_binding_regions)
SNP_bed = makeGRangesFromDataFrame(SNP_regions)
matches = as.data.frame( findOverlaps(tf_bed, SNP_bed) )
removed_SNPs = setdiff(rownames(SNP_regions), rownames(SNP_regions[unique(matches[,2]),]))
if (batch_mode == 'randomSNPinTF'){
removed_SNPs = sample(rownames(SNP_regions), size = length(removed_SNPs))
}
flog.info('%s of %s SNPs are filtered out of the TF binding regions', length(removed_SNPs) , nrow(SNP_regions))
data <<- data[setdiff(rownames(data), removed_SNPs),]
}
))
.DollarNames.GENE <- function(x, pattern){
my_funcs = grep('callSuper|show|copy|export|field|getClass|getRefClass|import|trace|init|usingMethods',getRefClass(class(x))$methods(), value = T, invert = T)
grep(pattern, c( my_funcs, names(getRefClass(class(x))$fields())), value=TRUE)
}
f_simple_locs <- function(input_features){
#simple_locs<-ldply( str_split(input_features, ':') ) %>%
#unite(col=new, c(V1,V2,V5,V6),sep=':') %>% select(new) %>%
#unique %>% unlist
simple_locs<-ldply( str_split(input_features, ':') ) %>%
mutate(new=paste(V1,V2,V5,V6,sep=':')) %>% select(new) %>%
unique %>% unlist
return (simple_locs)
}
library(RUnit)
options(run.main=FALSE)
if (getOption('run.main', default=TRUE)) {
runTestFile('./test/t_gene_data_class.R',testFuncRegexp = '^t_.*')
}
#runTestFile('./test/t_gene_data_class.R',testFuncRegexp = '^t_.*')