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Copy pathMegaLLM_GK.R
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MegaLLM_GK.R
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#################################################################
path.geno = "../Geno"
# devtools::install_github('deruncie/MegaLMM')
library(MegaLMM)
library(tidyverse)
#################################################################
pred_GK_func <- function(treatment, nFA, bw_mean){
path.output = paste0("../temp/mega/GK/", treatment, "_", bw_mean)
if(!dir.exists(path.output)){
dir.create(path.output, recursive = TRUE)
cat("Directory created successfully:", path.output, "\n")
}
# G matrix
load("../Geno/GKL.RData")
GKL <- GKL[[treatment]]
G = GKL[[bw_mean]]
nCV = 100
name <- c(paste0("a",1:10),paste0("b",1:10),paste0("c",1:10),paste0("d",1:10),paste0("e",1:10),paste0("f",1:10),paste0("g",1:10),paste0("h",1:3))
met2remove=c(13,28,30,33,38,62,67)
name = name[-met2remove]
# predictive correlation
corRL <- list()
met0<-list()
# CV
for (i in 18:18) {
# i=1
RDS_path = file.path(path.output, paste0(treatment, "_mega_cv_",i,".RDS"))
if(file.exists(RDS_path)){
cat(RDS_path," already exists \n")
next
}
cat("Now is running cv", i, "******************************************************************************************************************************************** \n")
# random-sampling to decide testing & reference accessions
met0[[i]] = read.csv(paste0("../Met/CrossValidation/cv_",i,"/met_cv_",i,".csv"))
met.wide0 <- met0[[i]] %>% filter(Treatment == treatment)
met.wide <- dplyr::select(met.wide0, all_of(c(name, "NSFTV_ID", "set")))
met.long <- tidyr::gather(met.wide,
key="met",
value="value",
-c(NSFTV_ID,set),
factor_key = F)
data_matrices = create_data_matrices(
tall_data = met.long,
id_cols = c('NSFTV_ID', "set"),
names_from = 'met',
values_from = 'value'
)
Y = data_matrices$Y
sample_data = data_matrices$data
id_train = sample_data$NSFTV_ID[sample_data$set=="train"]
id_test = sample_data$NSFTV_ID[sample_data$set=="test"]
fold_ID = i
Y_train = Y_testing = Y
Y_train[id_test, ] = NA
Y_testing[id_train, ] = NA
run_parameters = MegaLMM_control(
h2_divisions = 20,
burn = 0,
thin = 1,
K = nFA
)
MegaLMM_state = setup_model_MegaLMM(
Y = Y_train,
formula = ~ (1|NSFTV_ID),
data = sample_data,
relmat = list(NSFTV_ID = G),
run_parameters=run_parameters,
)
Lambda_prior = list(
sampler = sample_Lambda_prec_horseshoe,
prop_0 = 0.1,
delta = list(shape = 3, scale = 1),
delta_iterations_factor = 100
)
priors = MegaLMM_priors(
tot_Y_var = list(V = 0.5, nu = 5),
tot_F_var = list(V = 18/20, nu = 20),
h2_priors_resids_fun = function(h2s,n) 1,
h2_priors_factors_fun = function(h2s,n) 1,
Lambda_prior = Lambda_prior
)
MegaLMM_state = set_priors_MegaLMM(MegaLMM_state,priors)
MegaLMM_state = initialize_variables_MegaLMM(MegaLMM_state)
MegaLMM_state = initialize_MegaLMM(MegaLMM_state,verbose = T)
MegaLMM_state$Posterior$posteriorSample_params = c('Lambda','F_h2','resid_h2','tot_Eta_prec')
MegaLMM_state$Posterior$posteriorMean_params = 'Eta_mean'
MegaLMM_state$Posterior$posteriorFunctions = list(
U = 'U_F %*% Lambda + U_R',
G = 't(Lambda) %*% diag(F_h2[1,]) %*% Lambda + diag(resid_h2[1,]/tot_Eta_prec[1,])',
R = 't(Lambda) %*% diag(1-F_h2[1,]) %*% Lambda + diag((1-resid_h2[1,])/tot_Eta_prec[1,])',
h2 = '(colSums(F_h2[1,]*Lambda^2)+resid_h2[1,]/tot_Eta_prec[1,])/(colSums(Lambda^2)+1/tot_Eta_prec[1,])'
)
MegaLMM_state = clear_Posterior(MegaLMM_state)
n_iter = 250
for(jj in 1:28) {
print(sprintf('Sampling run %d',jj))
MegaLMM_state = sample_MegaLMM(MegaLMM_state,n_iter)
MegaLMM_state = save_posterior_chunk(MegaLMM_state)
print(MegaLMM_state)
}
# Lambda_samples = load_posterior_param(MegaLMM_state,'Lambda')
U_samples = load_posterior_param(MegaLMM_state,'U')
U_hat = apply(U_samples[4000:7000,,], c(2,3), mean)
# Eta_mean = load_posterior_param(MegaLMM_state,'Eta_mean')
MegaLMM_Uhat_accuracy = diag(cor(Y_testing,U_hat,use='p'))
# MegaLMM_Eta_mean_accuracy = diag(cor(Y_testing,Eta_mean,use='p'))
corRL[[i]] = MegaLMM_Uhat_accuracy
saveRDS(MegaLMM_Uhat_accuracy, file=file.path(path.output, paste0(treatment, "_mega_cv_",i,".RDS")))
}
save(corRL, file=file.path(path.output, paste0(treatment, "_MegaLMM.rda")))
}
#################################################################
nFA=5
pred_GK_func(treatment = 'Control', nFA = 5, bw_mean = "GK_0.8")
pred_GK_func(treatment = 'Control', nFA = 5, bw_mean = "GK_0.6")
pred_GK_func(treatment = 'Control', nFA = 5, bw_mean = "GK_0.4")
pred_GK_func(treatment = 'Control', nFA = 5, bw_mean = "GK_0.2")
nFA=5
pred_GK_func(treatment = 'Stress', nFA = 5, bw_mean = "GK_0.8")
pred_GK_func(treatment = 'Stress', nFA = 5, bw_mean = "GK_0.6")
pred_GK_func(treatment = 'Stress', nFA = 5, bw_mean = "GK_0.4")
pred_GK_func(treatment = 'Stress', nFA = 5, bw_mean = "GK_0.2")