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STAARpipeline_Gene_Centric_Coding.r
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#####################################################################
# Gene-centric analysis for coding rare variants using STAARpipeline
# Xihao Li, Zilin Li
# Initiate date: 11/04/2021
# Current date: 12/28/2022
#####################################################################
rm(list=ls())
gc()
## load required packages
library(gdsfmt)
library(SeqArray)
library(SeqVarTools)
library(STAAR)
library(STAARpipeline)
###########################################################
# User Input
###########################################################
## aGDS directory
agds_dir <- get(load("/path_to_the_file/agds_dir.Rdata"))
## Null model
obj_nullmodel <- get(load("/path_to_the_file/obj_nullmodel.Rdata"))
## QC_label
QC_label <- "annotation/filter"
## variant_type
variant_type <- "SNV"
## geno_missing_imputation
geno_missing_imputation <- "mean"
## Annotation_dir
Annotation_dir <- "annotation/info/FunctionalAnnotation"
## Annotation channel
Annotation_name_catalog <- get(load("/path_to_the_file/Annotation_name_catalog.Rdata"))
# Or equivalently
# Annotation_name_catalog <- read.csv("/path_to_the_file/Annotation_name_catalog.csv")
## Use_annotation_weights
Use_annotation_weights <- TRUE
## Annotation name
Annotation_name <- c("CADD","LINSIGHT","FATHMM.XF","aPC.EpigeneticActive","aPC.EpigeneticRepressed","aPC.EpigeneticTranscription",
"aPC.Conservation","aPC.LocalDiversity","aPC.Mappability","aPC.TF","aPC.Protein")
## output path
output_path <- "/path_to_the_output_file/"
## output file name
output_file_name <- "TOPMed_F5_LDL_Coding"
## input array id from batch file (Harvard FAS RC cluster)
arrayid <- as.numeric(commandArgs(TRUE)[1])
###########################################################
# Main Function
###########################################################
## gene number in job
gene_num_in_array <- 50
group.num.allchr <- ceiling(table(genes_info[,2])/gene_num_in_array)
sum(group.num.allchr)
chr <- which.max(arrayid <= cumsum(group.num.allchr))
group.num <- group.num.allchr[chr]
if (chr == 1){
groupid <- arrayid
}else{
groupid <- arrayid - cumsum(group.num.allchr)[chr-1]
}
genes_info_chr <- genes_info[genes_info[,2]==chr,]
sub_seq_num <- dim(genes_info_chr)[1]
if(groupid < group.num)
{
sub_seq_id <- ((groupid - 1)*gene_num_in_array + 1):(groupid*gene_num_in_array)
}else
{
sub_seq_id <- ((groupid - 1)*gene_num_in_array + 1):sub_seq_num
}
## exclude large coding masks
if(arrayid==57)
{
sub_seq_id <- setdiff(sub_seq_id,840)
}
if(arrayid==112)
{
sub_seq_id <- setdiff(sub_seq_id,c(543,544))
}
if(arrayid==113)
{
sub_seq_id <- setdiff(sub_seq_id,c(575,576,577,578,579,580,582))
}
## aGDS file
agds.path <- agds_dir[chr]
genofile <- seqOpen(agds.path)
genes <- genes_info
results_coding <- c()
for(kk in sub_seq_id)
{
print(kk)
gene_name <- genes_info_chr[kk,1]
results <- Gene_Centric_Coding(chr=chr,gene_name=gene_name,genofile=genofile,obj_nullmodel=obj_nullmodel,
rare_maf_cutoff=0.01,rv_num_cutoff=2,
QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name)
results_coding <- append(results_coding,results)
}
save(results_coding,file=paste0(output_path,output_file_name,"_",arrayid,".Rdata"))
seqClose(genofile)