INFRA: Verification

Statistical tests against external simulators

scripts/simulate_AlphaSimR.R

@@ -0,0 +1,106 @@
+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)
+
+# 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
+
+# The commandline input has 8 elements
+# [num_causal, temporary_directory_name, 
+# corA, num_trait, h2, h2_2, num_rep, random_seed]
+
+myArgs <- commandArgs(trailingOnly = TRUE)
+# Convert to numerics
+num_causal <- as.numeric(myArgs[1])
+directory_name <- myArgs[2]
+corA <- as.numeric(myArgs[3])
+num_trait <- as.numeric(myArgs[4])
+h2 <- as.numeric(myArgs[5])
+h2_2 <- as.numeric(myArgs[6])
+num_rep <- as.numeric(myArgs[7])
+random_seed <- as.numeric(myArgs[8])
+
+set.seed(random_seed)
+
+tskit <- import("tskit")
+
+tree_filename <- paste0(directory_name,"/tree_seq.tree")
+ts <- tskit$load(tree_filename)
+
+sites <- ts$tables$sites$asdict()
+pos <- sites$position * 1e-8 # Convert to Morgans
+pos <- pos - pos[1] # Set first position to zero
+
+# Extract haplotypes
+haplo <- t(ts$genotype_matrix())
+
+# Create an AlphaSimR founder population
+founderPop <- newMapPop(genMap=list(pos), haplotypes=list(haplo))
+
+num_ind <- nrow(haplo) / 2
+
+if (num_trait == 1){
+  mean <- 0
+  var <- 1
+  corA <- NULL
+  H2 <- h2
+} 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(h2,h2_2)
+}
+
+phenotype_result <- c()
+trait_result <- c()
+
+for (i in 1:num_rep) {
+  SP <- SimParam$
+    new(founderPop)$
+    addTraitA(
+      nQtlPerChr=num_causal,
+      mean=mean,
+      var=var,
+      corA=corA
+    )$
+    setVarE(H2=H2)
+
+  individuals <- newPop(founderPop)
+
+  trait_df <- c()
+  phenotype_df <- c()
+
+  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)
+  }
+  phenotype_result <- rbind(phenotype_result, phenotype_df)
+  trait_result <- rbind(trait_result, trait_df)
+}
+
+phenotype_filename <- paste0(directory_name,"/phenotype_alphasimr.csv")
+write.csv(phenotype_result, phenotype_filename, row.names=FALSE)
+
+trait_filename <- paste0(directory_name,"/trait_alphasimr.csv")
+write.csv(trait_result, trait_filename, row.names=FALSE)

data/simulate_simplePHENOTYPES.R → scripts/simulate_simplePHENOTYPES_exact.R

@@ -1,25 +1,22 @@
 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)
 
 # This code is used from verification.py to simulate quantitative traits
 # by using simplePHENOTYPES.
 
 # This code loads the vcf file and simulates quantitative traits
 
-# The commandline input has 7 elements
+# The commandline input has 6 elements
 # [num_causal, num_trait, add_effect, add_effect_2, directory_name,
-# vcf_filename, random_seed]
+# random_seed]
 myArgs <- commandArgs(trailingOnly = TRUE)
 
 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])
+random_seed <- as.numeric(myArgs[6])
 
 if (num_trait == 1){
   effect <- add_effect
@@ -32,7 +29,7 @@ if (num_trait == 1){
 }
 
 suppressMessages(create_phenotypes(
-  geno_file = paste0(directory_name, "/", vcf_filename, ".vcf"),
+  geno_file = paste0(directory_name, "/tree_seq.vcf"),
   add_QTN_num = num_causal,
   add_effect = effect,
   rep = 1,

scripts/simulate_simplePHENOTYPES_qqplot.R

@@ -0,0 +1,70 @@
+if (!require("simplePHENOTYPES")) install.packages("simplePHENOTYPES", repos='http://cran.us.r-project.org')
+library(simplePHENOTYPES)
+
+# This code is used from verification.py to simulate quantitative traits
+# by using simplePHENOTYPES.
+
+# This code loads the vcf file that is generated in `verification.py`
+# and uses the effect size from a normal distribution to simulate
+# additive traits.
+
+# The commandline input has 7 elements
+# [num_causal, h2, directory_name,
+# num_rep, mean, var, random_seed]
+myArgs <- commandArgs(trailingOnly = TRUE)
+
+num_causal <- as.numeric(myArgs[1])
+h2 <- as.numeric(myArgs[2])
+directory_name <- myArgs[3]
+num_rep <- myArgs[4]
+mean <- as.numeric(myArgs[5])
+var <- as.numeric(myArgs[6])
+random_seed <- as.numeric(myArgs[7])
+
+set.seed(random_seed)
+
+sd <- sqrt(var)
+
+# Function to simulate phenotypes from simplePHENOTYPES
+# The effect sizes are simulated from a normal distribution,
+# as the geometric series is the only effect size distribution
+# supported in simplePHENOTYPES.
+simulate_simplePHENOTYPE <- function(
+    num_causal, random_seed
+    ) {
+  effect_size <- list(rnorm(n=num_causal, mean=mean, sd=sd))
+  phenotypes <- suppressMessages(create_phenotypes(
+    geno_file = paste0(directory_name, "/tree_seq.vcf"),
+    add_QTN_num = num_causal,
+    add_effect = effect_size,
+    rep = 1,
+    h2 = h2,
+    model = "A",
+    seed = random_seed,
+    vary_QTN = FALSE,
+    to_r = TRUE,
+    sim_method = "custom",
+    quiet = TRUE,
+    home_dir = directory_name,
+    verbose = FALSE,
+    mean = 0
+  ))
+  # The mean is centered at 0 from simplePHENOTYPES simulation
+  # so we will divide it by the standard deviation to normalise
+  # the data
+  phenotypes[,2] <- phenotypes[,2] / sd(phenotypes[,2])
+  names(phenotypes)[1:2] <- c("individual_id", "phenotype")
+  return(phenotypes)
+}
+
+phenotype_result <- c()
+
+for (i in 1:num_rep) {
+  simulated_result <- simulate_simplePHENOTYPE(
+    num_causal=num_causal, random_seed=random_seed+i
+  )
+  phenotype_result <- rbind(phenotype_result, simulated_result)
+}
+
+filename = paste0(directory_name, "/simplePHENOTYPES.csv")
+write.csv(phenotype_result, filename, row.names=FALSE)

scripts/simulation_codes.md

@@ -0,0 +1,10 @@
+# Simulation Codes
+
+This 
+This directory is used to store the R simulation codes.
+
+R simulation codes:
+
+- simulate_AlphaSimR.R: This R code uses AlphaSimR to simulate quantitative traits based on a simulated tree sequence data.
+- simulate_simplePHENOTYPES_exact.R: This R code uses simplePHENOTYPES to simulate quantitative traits based on a VCF file. This is used for exact tests.
+- simulate_simplePHENOTYPES_qqplot.R: This R code uses simplePHENOTYPES to simulate quantitative traits. This is used in qqplot construction.