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Conduct exact tests against AlphaSimR, simplePHENOTYPES and the simulation framework described in ARG-Needle paper.
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if (!require("AlphaSimR")) install.packages("AlphaSimR", repos='http://cran.us.r-project.org') | ||
library(AlphaSimR) | ||
if (!require("reticulate")) install.packages("reticulate", repos='http://cran.us.r-project.org') | ||
library(reticulate) | ||
if (!require("tidyr")) install.packages("tidyr", repos='http://cran.us.r-project.org') | ||
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# This code is used from verification.py to simulate quantitative traits | ||
# by using AlphaSimR. | ||
# | ||
# The basic simulation step is the following: | ||
# 1. Use the tskit Python package through the R package tskit and load the tree | ||
# sequence data as a founder population in AlphaSimR. The codes of this step are | ||
# largely adapted from | ||
# https://github.com/ ynorr/AlphaSimR_Examples/blob/master/misc/msprime.R | ||
# 2. Simulate quantitative traits of the founder population in AlphaSimR | ||
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# The commandline input has 8 elements | ||
# [num_causal, temporary_directory_name, tree_filename, phenotype_filename, | ||
# trait_filename, corA, num_trait, random_seed] | ||
myArgs <- commandArgs(trailingOnly = TRUE) | ||
# Convert to numerics | ||
num_causal <- as.numeric(myArgs[1]) | ||
directory_name <- myArgs[2] | ||
tree_filename <- myArgs[3] | ||
phenotype_filename <- myArgs[4] | ||
trait_filename <- myArgs[5] | ||
corA <- as.numeric(myArgs[6]) | ||
num_trait <- as.numeric(myArgs[7]) | ||
random_seed <- as.numeric(myArgs[8]) | ||
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set.seed(random_seed) | ||
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tskit <- import("tskit") | ||
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tree_filename <- paste0(directory_name,"/", tree_filename,".tree") | ||
ts <- tskit$load(tree_filename) | ||
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sites <- ts$tables$sites$asdict() | ||
pos <- sites$position * 1e-8 # Convert to Morgans | ||
pos <- pos - pos[1] # Set first position to zero | ||
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# Extract haplotypes | ||
haplo <- t(ts$genotype_matrix()) | ||
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# Create an AlphaSimR founder population | ||
founderPop <- newMapPop(genMap=list(pos), haplotypes=list(haplo)) | ||
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num_ind <- nrow(haplo) / 2 | ||
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if (num_trait == 1){ | ||
mean <- 0 | ||
var <- 1 | ||
corA <- NULL | ||
H2 <- 1 | ||
} else if (num_trait == 2){ | ||
mean <- c(0,0) | ||
var <- c(1,1) | ||
corA <- matrix(c(1,corA,corA,1),nrow=2,ncol=2) | ||
H2 <- c(1,1) | ||
} | ||
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SP <- SimParam$ | ||
new(founderPop)$ | ||
addTraitA( | ||
nQtlPerChr=num_causal, | ||
mean=mean, | ||
var=var, | ||
corA=corA | ||
)$ | ||
setVarE(H2=H2) | ||
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individuals <- newPop(founderPop) | ||
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trait_df <- c() | ||
phenotype_df <- c() | ||
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for (trait_id in 1:num_trait){ | ||
qtl_site <- SP$traits[[trait_id]]@lociLoc - 1 | ||
effect_size <- SP$traits[[trait_id]]@addEff | ||
trait_id_df <- data.frame( | ||
effect_size = effect_size, | ||
site_id = qtl_site, | ||
trait_id = rep(trait_id-1, length(effect_size)) | ||
) | ||
trait_df <- rbind(trait_df, trait_id_df) | ||
phenotype <- individuals@pheno[,trait_id] | ||
phenotype_id_df <- data.frame( | ||
phenotype=phenotype, | ||
individual_id = 0:(num_ind-1), | ||
trait_id = rep(trait_id-1, num_ind) | ||
) | ||
phenotype_df <- rbind(phenotype_df, phenotype_id_df) | ||
} | ||
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phenotype_filename <- paste0(directory_name,"/",phenotype_filename,".csv") | ||
write.csv(phenotype_df, phenotype_filename, row.names=FALSE) | ||
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trait_filename <- paste0(directory_name,"/",trait_filename,".csv") | ||
write.csv(trait_df, trait_filename, row.names=FALSE) |
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if (!require("simplePHENOTYPES")) install.packages("simplePHENOTYPES", repos='http://cran.us.r-project.org') | ||
library(simplePHENOTYPES) | ||
if (!require("reticulate")) install.packages("reticulate", repos='http://cran.us.r-project.org') | ||
library(reticulate) | ||
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# This code is used from verification.py to simulate quantitative traits | ||
# by using simplePHENOTYPES. | ||
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# This code loads the vcf file and simulates quantitative traits | ||
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# The commandline input has 7 elements | ||
# [num_causal, num_trait, add_effect, add_effect_2, directory_name, | ||
# vcf_filename, random_seed] | ||
myArgs <- commandArgs(trailingOnly = TRUE) | ||
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num_causal <- as.numeric(myArgs[1]) | ||
num_trait <- as.numeric(myArgs[2]) | ||
add_effect <- as.numeric(myArgs[3]) | ||
add_effect_2 <- as.numeric(myArgs[4]) | ||
directory_name <- myArgs[5] | ||
vcf_filename <- myArgs[6] | ||
random_seed <- as.numeric(myArgs[7]) | ||
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if (num_trait == 1){ | ||
effect <- add_effect | ||
mean <- 0 | ||
h2 <- 1 | ||
} else if (num_trait == 2){ | ||
effect <- c(add_effect, add_effect_2) | ||
mean <- c(0,0) | ||
h2 <- c(1,1) | ||
} | ||
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suppressMessages(create_phenotypes( | ||
geno_file = paste0(directory_name, "/", vcf_filename, ".vcf"), | ||
add_QTN_num = num_causal, | ||
add_effect = effect, | ||
rep = 1, | ||
h2 = h2, | ||
model = "A", | ||
seed = random_seed, | ||
vary_QTN = FALSE, | ||
to_r = FALSE, | ||
sim_method = "geometric", | ||
quiet = TRUE, | ||
home_dir = directory_name, | ||
verbose = FALSE, | ||
mean = mean, | ||
ntraits = num_trait | ||
)) |
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