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eQTLHap.R
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eQTLHap.R
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rm(list = ls())
#!/usr/bin/envRscript
library("optparse")
cat("\t\t\t****************************************\n")
cat("\t\t\t* eQTLHap *\n")
cat("\t\t\t* V0.1 *\n")
cat("\t\t\t* author: Ziad Al Bkhetan *\n")
cat("\t\t\t* [email protected] *\n")
cat("\t\t\t* https://github.com/ziadbkh/eQTLHap *\n")
cat("\t\t\t****************************************\n")
option_list <- list(
make_option(
c("-f", "--haps"),
type = "character",
default = NULL,
help = "Haplotype/Genotype file path (.haps or .vcf).
\t\tIt can be compressed (.gz).",
metavar = "character"
),
make_option(
c("-g", "--genes"),
type = "character",
default = NULL,
help = "Gene Expression file path.\n\t\tIt should be in bed format.
\t\tFirst four columns should be (Chr, gene_name, start, end).
\t\tIt can be compressed (.gz). See the sample files for format.",
metavar = "character"
),
make_option(
c("-c", "--cov"),
type = "character",
default = "",
help = "Covariates file path.
\t\tSee the sample files for format.",
metavar = "character"
),
make_option(
c("-b", "--blocks"),
type = "character",
default = NULL,
help = "Haplotype block file.",
metavar = "character"
),
make_option(
c("-o", "--out"),
type = "character",
default = NULL,
help = "output file path.",
metavar = "character"
),
make_option(
c("", "--chrm"),
type = "numeric",
default = -1,
help = "chromosome number (1 to 22, X, Y).
\t\tIf not provided the first chromosome in gene expression file will be used.",
metavar = "numeric"
),
make_option(
c("", "--mtc"),
type = "character",
default = "BH",
help = "Multiple test correction method [default=%default].
\t\tAccepts any methods from p.adjust.methods",
metavar = "character"
),
make_option(
c("-p", "--permutation"),
type = "numeric",
default = 1000,
help = "Permutation count [default=%default].",
metavar = "numeric"
),
make_option(
c("-w", "--window"),
type = "numeric",
default = 1000000,
help = "Scanning window [default=%default] in bp to be applied up/downstream of the gene start.",
metavar = "numeric"
),
make_option(
c("-a", "--assessment"),
type = "character",
default = "HSG",
help = "eQTL assessment [default=%default].
\t\tAny combination of S, H and G where S: SNP assessment, G: block's genotype assessment, and H: block's haplotype assessment.",
metavar = "character"
),
make_option(
c("", "--vcf"),
action = "store_true",
default = FALSE,
help = "Phasing/unphased haplotypes input file is in VCF format.
\t\t[default=%default].
\t\tIf False, the input file is consdered .haps/.sample format."
),
make_option(
c("", "--smf"),
type = "numeric",
default = 0.01,
help = "SNP minimum frequency to be included in the analysis.
\t\t[default=%default].",
metavar = "numeric"
),
make_option(
c("", "--hmf"),
type = "numeric",
default = 0.02,
help = "Haplotype minimum frequency to be included in the analysis.
\t\t[default=%default].",
metavar = "numeric"
),
make_option(
c("", "--gmf"),
type = "numeric",
default = 0.02,
help = "Genotype minimum frequency to be included in the analysis.
\t\t[default=%default].",
metavar = "numeric"
),
make_option(
c("", "--maxPval4Perm"),
type = "numeric",
default = 0,
help = "Maximum pvalue in order to apply permutation multiple test correction.
\t\t[default=%default].
\t\tIf not 0, any association with a pvalue less than this threshold will be passed to permutation analysis.",
metavar = "numeric"
),
make_option(
c("", "--rmvIndividuals"),
action = "store_true",
default = FALSE,
help = "[default=%default].
\t\tIf provided, individuals with hablotype whose frequency are less than --hmf and --gmf
\t\twill be eliminated from the assessment.",
metavar = "numeric"
),
make_option(
c("", "--minIndividuals"),
type = "numeric",
default = 50,
help = "If the individual count is less than this threshold, skip the assessment.
\t\t[default=%default]."
),
make_option(
c("", "--outSignifcancePerm"),
type = "numeric",
default = 1,
help = "[default=%default].
\t\tKeep only the associations with permutation pvalue less than this threshold in the output results.",
metavar = "numeric"
),
make_option(
c("", "--outSignifcancePval"),
type = "numeric",
default = 0.05,
help = "[default=%default].
\t\tKeep only the associations with pvalue less than this threshold in the output results.",
metavar = "numeric"
),
make_option(
c("", "--outSignifcanceQval"),
type = "numeric",
default = 1,
help = "[default=%default].
\t\tKeep only the associations with qvalue less than this threshold in the output results.",
metavar = "numeric"
),
#make_option(
# c("", "--customBlockFile"),
# action = "store_true",
# default = FALSE,
# help = "[default=%default].
# \t\tProvide a custom block file.",
# metavar = "numeric"
#),
make_option(
c("", "--customBlocks"),
action = "store_true",
default = FALSE,
help = "[default=%default].
\t\tConsider a subset of the SNPs within each block as defined in teh column SNPS inside the block file instead of all SNPs betwenn the start/end SNPs.",
metavar = "numeric"
),
make_option(
c("", "--unphased"),
action = "store_true",
default = FALSE,
help = "[default=%default].
\t\tReading unphased genotype file",
metavar = "numeric"
)
)
opt_parser <- OptionParser(option_list = option_list)
opt <- parse_args(opt_parser)
start_time <- Sys.time()
print(paste("Analysis started at:", start_time), quote = F)
if (is.null(opt$haps) |
is.null(opt$genes) | is.null(opt$out)) {
print(
"Phased/unphased genotypes and gene expression and output files are mandatory!",
quote = FALSE
)
stop("Exiting..")
}
snp_assessment <- FALSE
block_genotype_assessment <- FALSE
block_haplotype_assessment <- FALSE
if (grepl("S", toupper(opt$assessment), fixed = TRUE))
snp_assessment <- TRUE
if (grepl("G", toupper(opt$assessment), fixed = TRUE))
block_genotype_assessment <- TRUE
if (grepl("H", toupper(opt$assessment), fixed = TRUE))
block_haplotype_assessment <- TRUE
if ((block_haplotype_assessment | block_genotype_assessment) & is.null(opt$blocks))
{
print(
"Block file is mandatory for eQTL based on block's haplotype/genotype!",
quote = FALSE
)
stop("Exiting..")
}
##### Validation ####
gene_expression_file_path <- opt$genes
haplotype_file_path <- opt$haps
haplotype_blocks_file_path <- opt$block
covariate_file_path <- opt$cov
out_put_file_path <- opt$out
if (file.access(gene_expression_file_path, mode = 4) != 0) {
stop("Gene expression file can not be accessed !", call. = FALSE)
}
if (file.access(haplotype_file_path, mode = 4) != 0) {
stop("Haplotype/Genotype file can not be accessed !", call. = FALSE)
}
if (block_haplotype_assessment | block_genotype_assessment)
{
if (file.access(haplotype_blocks_file_path, mode = 4) != 0) {
stop("Haplotype block file can not be accessed!", call. = FALSE)
}
}
if (covariate_file_path != "")
if (file.access(covariate_file_path, mode = 4) != 0) {
stop("covariate file does not exist!", call. = FALSE)
}
##### Libraries ####
suppressMessages(library(dplyr))
#####Parameter configuration#####
chrom <- opt$chrm
chrom <- gsub("chr", "", chrom)
correction_method <- opt$mtc
scanning_window <- opt$window
vcf_input <- opt$vcf
snp_minimal_frequency <- opt$smf
haplotype_minimal_frequency <- opt$hmf
genotype_minimal_frequency <- opt$gmf
remove_individual_rare <- opt$rmvIndividuals
significance_threshold_perm <- opt$outSignifcancePerm
significance_threshold_pval <- opt$outSignifcancePval
significance_threshold_qval <- opt$outSignifcanceQval
minimum_individuals <- opt$minIndividuals
permutation_calculation_threshold <- opt$maxPval4Perm
permutations_num <- opt$permutation
if (permutation_calculation_threshold == 0 | permutations_num == 0)
{
permutation_calculation_threshold <- 0
permutations_num <- 0
}
complete_block <- !opt$customBlocks
unphased_data <- opt$unphased
genes_output <- opt$genesOut
#haplotype file configuration:
gene_expression_info_columns <- c(3, 1, 4, 2)
gene_expression_info_columns <- c(1, 2, 3, 4)
gene_expression_info_columns_name <-
c("Chr", "gene_nm", "start", "end")
vcf_start_snps <- 10
snps_per_iteration <- 1000
########## Functions ######
eQTLHap_block_encoding <- function(fun_block_haplotype_1,
fun_block_haplotype_2,
fun_haplotype_minimal_frequency,
fun_genotype_minimal_frequency,
fun_remove_individual_opt,
fun_minimum_individuals,
fun_block_haplotype_assessment,
fun_block_genotype_assessment) {
fun_bag_of_haplotypes <- list()
fun_bag_of_genotypes <- list()
fun_genotype_individuals <- 1:length(fun_block_haplotype_1)
fun_haplotype_individuals <- 1:length(fun_block_haplotype_1)
if (fun_block_genotype_assessment)
{
fun_block_genotype <-
apply(fun_block_haplotype_1 + fun_block_haplotype_2, 2, function(x)
paste0(x, collapse = ""))
fun_genotype_frequency <-
table(fun_block_genotype) / length(fun_genotype_individuals)
fun_unique_frequent_genotypes <-
names(fun_genotype_frequency[fun_genotype_frequency >= fun_genotype_minimal_frequency])
if (length(fun_unique_frequent_genotypes) >= 2)
{
if (fun_remove_individual_opt)
{
fun_rare_genotypes <-
setdiff(names(fun_genotype_frequency),
fun_unique_frequent_genotypes)
fun_removed_individuals <-
which(fun_block_genotype %in% fun_rare_genotypes)
fun_kept_individuals <-
setdiff(1:length(fun_block_genotype),
fun_removed_individuals)
fun_block_genotype <-
fun_block_genotype[fun_kept_individuals]
fun_genotype_individuals <- fun_kept_individuals
}
if (length(fun_genotype_individuals) > fun_minimum_individuals)
{
for (i in 2:length(fun_unique_frequent_genotypes))
{
fun_bag_of_genotypes[[i - 1]] <-
matrix(ifelse(
fun_block_genotype == fun_unique_frequent_genotypes[[i]],
1,
0
),
nrow = 1)
}
}
}
}
if (fun_block_haplotype_assessment)
{
fun_block_haplotype_1 <-
apply(fun_block_haplotype_1, 2, function(x)
paste0(x, collapse = ""))
fun_block_haplotype_2 <-
apply(fun_block_haplotype_2, 2, function(x)
paste0(x, collapse = ""))
fun_haplotype_frequncy <-
table(c(fun_block_haplotype_1, fun_block_haplotype_2)) / (2 * length(fun_haplotype_individuals))
fun_unique_frequent_haplotypes <-
names(fun_haplotype_frequncy[fun_haplotype_frequncy >= fun_haplotype_minimal_frequency])
if (length(fun_unique_frequent_haplotypes) >= 2)
{
if (fun_remove_individual_opt)
{
fun_rare_haplotypes <-
setdiff(names(fun_haplotype_frequncy),
fun_unique_frequent_haplotypes)
fun_removed_individuals <-
unique(c(
which(fun_block_haplotype_1 %in% fun_rare_haplotypes),
which(fun_block_haplotype_2 %in% fun_rare_haplotypes)
))
fun_kept_individuals <-
setdiff(1:length(fun_block_haplotype_1),
fun_removed_individuals)
fun_block_haplotype_1 <-
fun_block_haplotype_1[fun_kept_individuals]
fun_block_haplotype_2 <-
fun_block_haplotype_2[fun_kept_individuals]
fun_haplotype_individuals <- fun_kept_individuals
}
if (length(fun_haplotype_individuals) > fun_minimum_individuals)
{
for (i in 2:length(fun_unique_frequent_haplotypes))
{
fun_bag_of_haplotypes[[i - 1]] <-
matrix(
ifelse(
fun_block_haplotype_1 == fun_unique_frequent_haplotypes[[i]],
1,
0
) +
ifelse(
fun_block_haplotype_2 == fun_unique_frequent_haplotypes[[i]],
1,
0
),
nrow = 1
)
}
}
}
}
return(
list(
"haplotype" = fun_bag_of_haplotypes,
"genotype" = fun_bag_of_genotypes,
"genotype_individuals" = fun_genotype_individuals,
"haplotype_individuals" = fun_haplotype_individuals
)
)
}
eQTLHAP_block_orthogonalization <- function(fun_bag_of_things,
fun_covariates) {
#Orthogonalize haplotypes
i <- 1
for (i in 1:length(fun_bag_of_things))
{
row_sum_prev <- rowSums(fun_bag_of_things[[i]] ^ 2)
fun_bag_of_things[[i]] <-
fun_bag_of_things[[i]] - tcrossprod(fun_bag_of_things[[i]], fun_covariates) %*%
fun_covariates
if (i > 1)
for (j in 1:(i - 1))
fun_bag_of_things[[i]] <-
fun_bag_of_things[[i]] - rowSums(fun_bag_of_things[[i]] * fun_bag_of_things[[j]]) *
fun_bag_of_things[[j]]
row_sum <- rowSums(fun_bag_of_things[[i]] ^ 2)
delete_indx <- (row_sum <= row_sum_prev * .Machine$double.eps)
fun_bag_of_things[[i]][delete_indx, ] <- 0
div <- sqrt(rowSums(fun_bag_of_things[[i]] ^ 2))
div[delete_indx] <- 1
fun_bag_of_things[[i]] <- fun_bag_of_things[[i]] / div
}
return(fun_bag_of_things)
}
eQTLHAP_block_MR2 <- function(fun_bag_of_things,
fun_gene_expression,
fun_covariates) {
R_squared <- c()
for (i in 1:length(fun_bag_of_things)) {
R_squared[[i]] <-
tcrossprod(fun_gene_expression, fun_bag_of_things[[i]])
}
multiple_R_squared <- R_squared[[1]] ^ 2
if (length(fun_bag_of_things) > 1)
{
for (j in 2:length(R_squared))
multiple_R_squared <- multiple_R_squared + R_squared[[j]] ^ 2
}
return(multiple_R_squared)
}
eQTLHAP_block_pvalue <- function(fun_multiple_R_squared,
fun_predictor_cnt,
fun_covariates_cnt,
fun_individual_cnt) {
corrected_degrees_of_freedom <-
fun_individual_cnt - fun_covariates_cnt - fun_predictor_cnt
f_test <-
fun_multiple_R_squared / (1 - fun_multiple_R_squared) * (corrected_degrees_of_freedom /
fun_predictor_cnt)
pval <-
pf(f_test,
fun_predictor_cnt,
corrected_degrees_of_freedom,
lower.tail = FALSE)
return(pval)
}
eQTL_Block <- function(fun_bag_of_things,
fun_covariates,
fun_gene_expression,
fun_individuals,
fun_permutation_calculation_threshold)
{
fun_bag_of_things <-
eQTLHAP_block_orthogonalization(fun_bag_of_things, fun_covariates[, fun_individuals])
# original gene expression association
b_mr2 <- eQTLHAP_block_MR2(fun_bag_of_things,
fun_gene_expression[nrow(fun_gene_expression) , fun_individuals],
fun_covariates[, fun_individuals])
emprical_pval <-
eQTLHAP_block_pvalue(b_mr2,
length(fun_bag_of_things),
nrow(fun_covariates),
length(fun_individuals))
perm_pval <- 2
if (emprical_pval < fun_permutation_calculation_threshold)
{
mr2 <- eQTLHAP_block_MR2(fun_bag_of_things,
fun_gene_expression[1:(nrow(fun_gene_expression) - 1) , fun_individuals],
fun_covariates[, fun_individuals])
pval_ls <-
eQTLHAP_block_pvalue(mr2,
length(fun_bag_of_things),
nrow(fun_covariates),
length(fun_individuals))
perm_pval <-
length(which(pval_ls <= emprical_pval)) / length(pval_ls)
}
return(
list(
"predictor" = length(fun_bag_of_things),
"individuals" = length(fun_individuals),
"MR2" = b_mr2,
"emprical_pval" = emprical_pval,
"perm_pval" = perm_pval
)
)
}
eQTLHAP_SNP <-
function(fun_block_haplotype_1,
fun_block_haplotype_2,
fun_gene_expression,
fun_covariates,
fun_permutation_calculation_threshold) {
my_snps <- as.matrix(fun_block_haplotype_1 + fun_block_haplotype_2)
row_sum_prev <- rowSums(my_snps ^ 2)
my_snps <-
my_snps - tcrossprod(my_snps, fun_covariates) %*% fun_covariates
row_sum <- rowSums(my_snps ^ 2)
delete_indx <- (row_sum <= row_sum_prev * .Machine$double.eps)
my_snps[delete_indx, ] <- 0
div <- sqrt(row_sum)
div[delete_indx] <- 1
my_snps = my_snps / div
emp_R_st <-
tcrossprod(fun_gene_expression[nrow(fun_gene_expression), ], my_snps)
model_predictors <- 1
corrected_degrees_of_freedom <-
length(fun_block_haplotype_1) - nrow(fun_covariates) - model_predictors
emp_t_test <-
emp_R_st * sqrt(corrected_degrees_of_freedom / (1 - emp_R_st ^ 2))
emp_pval <-
pt(-abs(emp_t_test), corrected_degrees_of_freedom) * 2
kept_snps <-
which(emp_pval < fun_permutation_calculation_threshold)
kept_snps_nm <- row.names(my_snps)[kept_snps]
if (length(kept_snps) > 0)
{
my_snps <- my_snps[kept_snps, ]
if (length(kept_snps) > 1)
R_st <- abs(tcrossprod(fun_gene_expression, my_snps))
else
{
R_st <-
tcrossprod(fun_gene_expression, matrix(my_snps, nrow = 1))
colnames(R_st) <- kept_snps_nm
}
t_test <-
R_st * sqrt(corrected_degrees_of_freedom / (1 - R_st ^ 2))
pval <- pt(-abs(t_test), corrected_degrees_of_freedom) * 2
pval <- apply(pval, 2, function(x) {
(length(which(x <= x[[length(x)]])) - 1) / (length(x) - 1)
})
} else
{
pval <- c()
}
return(list(
"R2" = emp_R_st,
"ttest" = emp_t_test,
"pval" = emp_pval,
"perm_pval" = pval
))
}
########### Start Processing #############
if (vcf_input)
{
haplotype_header_row <- 1
haplotype_data <- file(haplotype_file_path, "r")
while (TRUE) {
line <- readLines(haplotype_data, n = 1)
if (startsWith(line, "#CHROM")) {
break
}
haplotype_header_row <- haplotype_header_row + 1
}
close(haplotype_data)
haplotype_data <-
read.table(
haplotype_file_path,
skip = haplotype_header_row - 1,
comment.char = "",
header = T,
stringsAsFactors = F
)
colnames(haplotype_data)[[1]] <- "CHROM"
if (chrom == -1)
chrom <- as.character(haplotype_data[1, "CHROM"])
haplotype_data <-
filter(haplotype_data, CHROM == chrom |
CHROM == paste0("chr", chrom))
if (nrow(haplotype_data) == 0)
stop (
paste0(
"No SNPs are left after filtration based on chromosome! (chromsome = ",
chrom,
")."
)
)
haplotype_data_h1 <- haplotype_data
phase_sep <- "[|]"
if (unphased_data)
phase_sep <- "/"
for (i in vcf_start_snps:length(haplotype_data_h1))
haplotype_data_h1[, i] <-
as.numeric(as.character(sapply(haplotype_data_h1[, i], function(x)
strsplit(x, phase_sep)[[1]][[1]])))
haplotype_data_h2 <- haplotype_data
rm(list = "haplotype_data")
for (i in vcf_start_snps:length(haplotype_data_h2))
haplotype_data_h2[, i] <-
as.numeric(as.character(sapply(haplotype_data_h2[, i], function(x)
strsplit(x, phase_sep)[[1]][[2]])))
haplotype_data_info <- haplotype_data_h1[, 1:(vcf_start_snps - 1)]
haplotype_data_h1 <-
haplotype_data_h1[, vcf_start_snps:length(haplotype_data_h1)]
haplotype_data_h2 <-
haplotype_data_h2[, vcf_start_snps:length(haplotype_data_h2)]
rownames(haplotype_data_h1) <- haplotype_data_info$ID
rownames(haplotype_data_h2) <- haplotype_data_info$ID
numeric_cols <- unlist(lapply(haplotype_data_h1, is.numeric))
if (length(which(numeric_cols)) != length(haplotype_data_h1))
{
stop("SNP columns are not numeric!")
}
} else
#read haplotypes from .haps and .sample files
{
haplotype_data <-
read.table(haplotype_file_path,
stringsAsFactors = F)
colnames(haplotype_data)[[1]] <- "CHROM"
if (chrom == -1)
chrom <- as.character(haplotype_data[1, 1])
haplotype_data <-
filter(haplotype_data, CHROM == chrom |
CHROM == paste0("chr", chrom))
if (nrow(haplotype_data) == 0)
stop (
paste0(
"No SNPs are left after filtration based on chromosome! (chromsome = ",
chrom,
")."
)
)
haplotype_data_info <- haplotype_data[, 1:5]
colnames(haplotype_data_info)[[2]] <- "ID"
colnames(haplotype_data_info)[[3]] <- "POS"
haplotype_data_h1 <-
haplotype_data[, seq(6, length(haplotype_data), 2)]
haplotype_data_h2 <-
haplotype_data[, seq(7, length(haplotype_data), 2)]
rm(list = "haplotype_data")
rownames(haplotype_data_h1) <- haplotype_data_info$ID
rownames(haplotype_data_h2) <- haplotype_data_info$ID
haplotype_data_sample <-
read.table(gsub(".haps", ".sample", gsub(".gz", "", haplotype_file_path)),
stringsAsFactors = F)
colnames(haplotype_data_h1) <-
haplotype_data_sample$V2[3:nrow(haplotype_data_sample)]
colnames(haplotype_data_h2) <-
haplotype_data_sample$V2[3:nrow(haplotype_data_sample)]
numeric_cols <- unlist(lapply(haplotype_data_h2, is.numeric))
if (length(which(numeric_cols)) != length(haplotype_data_h1))
{
stop("SNP columns are not numeric!")
}
}
haplotype_data_info$indx <- 1:nrow(haplotype_data_info)
haplotype_data_info$ID <- as.character(haplotype_data_info$ID)
print(paste(
nrow(haplotype_data_h1),
"SNPs for",
length(haplotype_data_h1),
"individuals are loaded."
),
quote = F)
#read gene expression file
gene_expression_data <-
read.table(
gene_expression_file_path,
header = T,
comment.char = "",
stringsAsFactors = F
)
print(paste(
nrow(gene_expression_data),
"Genes for",
length(gene_expression_data) - 4,
"individuals are loaded."
),
quote = F)
gene_expression_data <-
gene_expression_data[, c(gene_expression_info_columns,
5:length(gene_expression_data))]
colnames(gene_expression_data)[1:4] <-
gene_expression_info_columns_name
gene_expression_data <-
filter(gene_expression_data, Chr == chrom |
Chr == paste0("chr", chrom))
if (nrow(gene_expression_data) == 0)
stop("No genes left after chromosome filteration!")
gene_expression_data_info <- gene_expression_data[, 1:4]
gene_expression_data <-
gene_expression_data[, 5:length(gene_expression_data)]
row.names(gene_expression_data) <-
gene_expression_data_info$gene_nm
common_individuals <-
intersect(colnames(haplotype_data_h1),
colnames(gene_expression_data))
if (length(common_individuals) == 0)
stop (
"Please make sure that individuals' names are consistent across phased haplytpes, gene expression and covariates files."
)
###Covariants
if (covariate_file_path != "")
{
covariates_data <-
read.table(covariate_file_path, header = T)
covariates_data_indiv <- covariates_data$ID
covariates_data <- covariates_data[, 2:length(covariates_data)]
print(paste(
nrow(covariates_data),
"covariates for",
length(covariates_data),
"individuals are loaded."
),
quote = F)
row.names(covariates_data) <- covariates_data_indiv
common_individuals <-
intersect(common_individuals, colnames(covariates_data))
if (length(common_individuals) == 0)
stop (
"Please make sure that individuals' names are consistent across phased haplytpes, gene expression and covariates files."
)
covariates_data <-
as.matrix(covariates_data[, common_individuals], ncol = length(common_individuals))
cvrt_cnt <- nrow(covariates_data)
} else
{
cvrt_cnt <- 0
}
haplotype_data_h1 <- haplotype_data_h1[, common_individuals]
haplotype_data_h2 <- haplotype_data_h2[, common_individuals]
gene_expression_data <- gene_expression_data[, common_individuals]
for (i in 1:length(haplotype_data_h1))
{
if (colnames(haplotype_data_h1)[[i]] != colnames(gene_expression_data)[[i]])
stop("Individualsarenotsorted!")
if (cvrt_cnt > 0)
if (colnames(haplotype_data_h1)[[i]] != colnames(covariates_data)[[i]])
stop("Individualsarenotsorted!")
}
if (block_haplotype_assessment | block_genotype_assessment)
{
haplotype_blocks <-
read.table(haplotype_blocks_file_path,
header = TRUE,
stringsAsFactors = F)
haplotype_blocks <-
filter(haplotype_blocks, CHR == chrom |
CHR == paste0("chr", chrom))
if (nrow(haplotype_blocks) == 0)
stop (paste0(
"No SNPs are left after filtration based on chromosome! (chromsome = ",
chrom,
")."
))
haplotype_blocks$START_SNP <-
sapply(as.character(haplotype_blocks$SNPS), function(x)
strsplit(x, "[|]")[[1]][[1]])
haplotype_blocks$END_SNP <-
sapply(as.character(haplotype_blocks$SNPS), function(x)
tail(strsplit(x, "[|]")[[1]], n = 1))
haplotype_blocks <-
haplotype_blocks[, c("CHR", "START_SNP", "END_SNP", "SNPS")]
haplotype_blocks <-
left_join(haplotype_blocks,
haplotype_data_info[, c("ID", "POS", "indx")],
by = c("START_SNP" = "ID"))
colnames(haplotype_blocks)[[length(haplotype_blocks) - 1]] <-
"start_pos"
colnames(haplotype_blocks)[[length(haplotype_blocks)]] <- "START_ID"
haplotype_blocks <-
left_join(haplotype_blocks,
haplotype_data_info[, c("ID", "POS", "indx")],
by = c("END_SNP" = "ID"))
colnames(haplotype_blocks)[[length(haplotype_blocks) - 1]] <-
"end_pos"
colnames(haplotype_blocks)[[length(haplotype_blocks)]] <- "END_ID"
haplotype_blocks$original_id <- 1:nrow(haplotype_blocks)
}else
{
haplotype_blocks <- data.frame()
}
print(
paste(
nrow(haplotype_data_h1),
"SNPs,",
nrow(haplotype_blocks),
"Blocks",
nrow(gene_expression_data),
"Genes and",
cvrt_cnt,
"covariates for",
length(haplotype_data_h1),
"individuals are passed filtration and will be included in the analysis"
),
quote = F
)
if (permutation_calculation_threshold > 0 &
permutations_num > 0)
{
print(
paste(
"Permutation based p-value will be caluclated for associations with p-value less than ",
permutation_calculation_threshold,
"(based on",
1000 * ceiling(permutations_num / 1000),
"permutations)"
),
quote = F
)
}
if (remove_individual_rare & block_haplotype_assessment)
{
print(
paste0(
"Individuals with rare haplotypes (freq < ", haplotype_minimal_frequency ,") will be eleiminated from the assessment.",
" If the remaining individuals are less than ", minimum_individuals ," , the block will be skipped."),
quote = F
)
}
if (remove_individual_rare & block_genotype_assessment)
{
print(
paste0(
"Individuals with rare haplotypes (freq < ", genotype_minimal_frequency ,") will be eleiminated from the assessment.",
" If the remaining individuals are less than ", minimum_individuals ," , the block will be skipped."),
quote = F
)
}
###########################
individual_cnt <- length(haplotype_data_h1)
if (cvrt_cnt > 0) {
covariates_data <-
rbind(matrix(1, 1, individual_cnt), covariates_data)
} else{
covariates_data <- matrix(1, 1, individual_cnt)
}