Last updated: 2023-03-05

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Knit directory: dgrp-starve/

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#loop count and data limit
iter <- 48

#Parallel core count
#registerDoParallel(cores = 8)

#ggplot holder list
gg <- vector(mode='list', length=12)

# result storage elements
fit_greml <- vector(mode='list', length=iter)
fit_gbayesC <- vector(mode='list', length=iter)
fit_varbvs <- vector(mode='list', length=iter)
fit_glmnet <- vector(mode='list', length=iter)
dataFlag <- TRUE

if(dataFlag){

 #wolb infection and inversion status data with phenotype adjustment function
load("/data2/morgante_lab/data/dgrp/misc/adjustData.RData")

  
  #expression data matched to line and starvation phenotype
  xp_f <- fread("data/xp-f.txt")
  
 # xp_m <- fread("data/xp-m.txt")
  
  #setwd("C:/Users/noahk/OneDrive/Desktop/amogus")
  #getwd()
  
  #create matrix of only gene expression, trims line and starvation
  X <- as.matrix(xp_f[,3:11340])
  rownames(X) <- xp_f[,line]
  W <- scale(X)
  
  y_temp <- xp_f[,starvation]
  dat <- data.frame(id=xp_f[,line], y=y_temp)
  y <- adjustPheno(dat, "starvation")

} else{
  
  # Toy Data set, 200x100 matrix
  W <- matrix(rnorm(20000), ncol = 100)
  colnames(W) <- paste0("gene", 1:ncol(W))
  rownames(W) <- paste0("line", 1:nrow(W))
  
  #model uses genes 1:5 and 10:20
  y <- rowSums(W[, 1:5]) + rowSums(W[, 10:20]) + rnorm(nrow(W))

}
Type III ANOVA table for covariates: starvation
                 Df Sum of Sq   RSS    AIC F value  Pr(>F)  
<none>                        28767 1009.8                  
factor(wolba)     1    483.10 29250 1011.1  3.1236 0.07881 .
factor(In_2L_t)   2     60.22 28827 1006.2  0.1947 0.82325  
factor(In_2R_NS)  2    300.22 29067 1007.8  0.9706 0.38078  
factor(In_3R_P)   2     85.92 28853 1006.4  0.2778 0.75777  
factor(In_3R_K)   2    959.43 29726 1012.3  3.1017 0.04731 *
factor(In_3R_Mo)  2    416.65 29184 1008.6  1.3470 0.26255  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Estimated effects
                    Estimate Std. Error    t value     Pr(>|t|)
(Intercept)       58.7465186   1.553929 37.8051442 3.103130e-89
factor(wolba)y     3.2450739   1.836101  1.7673724 7.880567e-02
factor(In_2L_t)1  -0.8506378   3.101713 -0.2742477 7.841986e-01
factor(In_2L_t)2  -1.8583916   3.151661 -0.5896547 5.561378e-01
factor(In_2R_NS)1  0.8630221   4.621570  0.1867379 8.520697e-01
factor(In_2R_NS)2  6.7057598   4.825270  1.3897169 1.662760e-01
factor(In_3R_P)1  -1.8513843   5.206148 -0.3556150 7.225319e-01
factor(In_3R_P)2   4.0596759   6.331413  0.6411959 5.221847e-01
factor(In_3R_K)1   7.4870256   4.084124  1.8332024 6.837092e-02
factor(In_3R_K)2  15.5762633   8.948152  1.7407240 8.338564e-02
factor(In_3R_Mo)1 -5.4712766   4.279569 -1.2784644 2.026787e-01
factor(In_3R_Mo)2 -3.6074434   3.224295 -1.1188318 2.646547e-01
### qgg_greml

#model to solve for, vector of ones
mu <- matrix(rep(1, length(y)), ncol=1)
#names(mu) <- paste0("line", 1:length(mu))
rownames(mu) <- xp_f[,line]
TRM <- tcrossprod(W)/ncol(W)

# k-fold parameters
n <- length(y)
fold <- 10

iter <- 48
### sample analysis of gbayesC to show that convergence is working as expected 

  test_IDs <- sample(1:n, as.integer(n / fold))
  
  W_train <- W[-test_IDs,]
  W_test <- W[test_IDs,]
  y_train <- y[-test_IDs]
  y_test <- y[test_IDs]
  

  ### GBAYES-C
  fitC <- qgg::gbayes(y=y_train, W=W_train, method="bayesC", scaleY=FALSE, nit=10000, nburn=5000)
plotCustomBayes <- function(rdsPath){

# data read in is a gBayes fit object from qgg
#fitC <- readRDS("gbayesC-f.Rds")
fitC <- readRDS(rdsPath)

# calculate column narrow heritability
fitC$h2 <- fitC$vgs/(fitC$vgs+fitC$ves)

# data is extracted and stored
fitData <- data.table(iter=1:10000, ves=fitC$ves, vbs=fitC$vbs, vgs=fitC$vgs, h2=fitC$h2)

gg <- vector(mode='list', length=4)

gg[[1]] <- ggplot(fitData, aes(x=iter, y=ves)) +
  geom_point(size=0.5) + 
  labs(x="Iteration", y="Ve") + 
  ggtitle("Posterior Mean for Residual Variance")

gg[[2]] <- ggplot(fitData, aes(x=iter, y=vbs)) +
  geom_point(size=0.5) + 
  labs(x="Iteration", y="Vb") + 
  ggtitle("Posterior Mean for Marker Variance")

gg[[3]] <- ggplot(fitData, aes(x=iter, y=vgs)) +
  geom_point(size=0.5) + 
  labs(x="Iteration", y="Vg") + 
  ggtitle("Posterior Mean for Genomic Variance")

gg[[4]] <- ggplot(fitData, aes(x=iter, y=ves)) +
  geom_point(size=0.5) + 
  labs(x="Iteration", y="h^2") + 
  ggtitle("Posterior Mean for Narrow-sense Heritability")

  return(gg) 
}

    # fit taken from one iteration, restored from Rds using code chunk shown above
    #fitC <- readRDS("data/gbayesC-f.Rds")
    
    plotHold <- plotCustomBayes("data/gbayesC-f.Rds")

    plot_grid(plotHold[[1]],plotHold[[2]], ncol=2)

    plot_grid(plotHold[[3]],plotHold[[4]], ncol=2)

    #plotBayes(fit=fitC, what="trace")
#this runs on the same data sets as greml

### qgg_gBayesC
### https://www.rdocumentation.org/packages/qgg/versions/1.1.1/topics/gbayes

# Bayes C: uses a “rounded spike” (low-variance Gaussian) at origin many small effects can contribute to polygenic component, reduces the dimensionality of the model (makes Gibbs sampling feasible).

#Parallel Header
#tempResult <- 

iter <- 1
corLoop <- foreach(i=1:iter) %dopar% {

#Linear Header
#for(i in 1:iter){
  
  corResult <- (1:4)
  
  
  
  #setup train and test sets with trait vectors
  test_IDs <- sample(1:n, as.integer(n / fold))
  
  W_train <- W[-test_IDs,]
  W_test <- W[test_IDs,]
  y_train <- y[-test_IDs]
  y_test <- y[test_IDs]
  
  
  ### GREML, qgg package
  fitGreml <- qgg::greml(y=y, X=mu, GRM=list(A=TRM), validate = matrix(test_IDs,ncol=1), verbose=FALSE)
  
  #Store coeff directly
  fit_greml[[i]] <- fitGreml$accuracy$Corr
  
  corResult[1] <- fitGreml$accuracy$Corr

  
  
  ### GBAYES-C
  
  
  fitC <- qgg::gbayes(y=y_train, W=W_train, method="bayesC", scaleY=FALSE, nit=10000, nburn=5000)
  
  # expected/calculated value for y_test
  # \hat{y}_test = W_{test} * \hat{b} + \hat{mu}
  y_calc <- W_test %*% fitC$b + mean(y_train)
  
  # store coeff
  fit_gbayesC[[i]] <- cor(y_test, y_calc)
  
  corResult[2] <- cor(y_test, y_calc)
  
  if(i==1){
    print(paste0("gbayesC trace plots from validate set ",i))
    plotBayes(fit=list(fitC), what = "trace")
  }
    
  
  ### VARBVS
  fitVarb <- varbvs::varbvs(X = W_train, NULL, y=y_train, family = "gaussian", logodds=seq(-3.5,-1,0.1), sa = 1, verbose=FALSE)
  
  # \hat{y}_test = W_{test} * \hat{b} + \hat{mu}
  y_calc <- W_test %*% fitVarb$beta + mean(y_train)
  
  fit_varbvs[[i]] <- cor(y_test, y_calc)
  
  corResult[3] <- cor(y_test, y_calc)
  
  
  
  
  ### GLMNET  
  fitlm <- glmnet::cv.glmnet(x=W_train, y=y_train, alpha=1)
  

  b_hat <- glmnet::coef.glmnet(fitlm, s="lambda.min")


  y_int <- b_hat[1]

  b_hat <- b_hat[2:length(b_hat)]

  y_calc <- W_test %*% b_hat + y_int

  fit_glmnet[[i]] <- cor(y_test, y_calc)
  
  corResult[4] <- cor(y_test, y_calc)
  
  
  corResult

}
# results loaded from correlation loop structure
corLoop <- readRDS("data/corLoop-f.rds")



for(i in 1:iter){
  fit_greml[[i]] <- corLoop[[i]][1]
  fit_gbayesC[[i]] <- corLoop[[i]][2]
  fit_varbvs[[i]] <- corLoop[[i]][3]
  fit_glmnet[[i]] <- corLoop[[i]][4]
}
iter <- 48

bint <- 10

### qgg_greml

qgg_greml_data <- as.data.table(fit_greml)
qgg_greml_data <- transpose(qgg_greml_data)
colnames(qgg_greml_data) <- "cor"

#print(paste0("GREML"))
#print(paste0("Mean correlation coefficient: ", mean(qgg_greml_data[,cor])))
#print(paste0("Variance of correlation coefficient: ", var(qgg_greml_data)))

gg[[1]] <- ggplot(qgg_greml_data, aes(x=cor)) +
  geom_histogram(bins=bint, fill='red') +
  labs(x="Corr Coeff") +
  ggtitle("GREML CV Correlations Histogram")


### qgg_gbayesC

qgg_gbayesC_data <- as.data.table(fit_gbayesC)
qgg_gbayesC_data <- transpose(qgg_gbayesC_data)
colnames(qgg_gbayesC_data) <- "cor"

#print(paste0("gBayesC"))
#print(paste0("Mean correlation coefficient: ", mean(qgg_gbayesC_data[,cor])))
#print(paste0("Variance of correlation coefficient: ", var(qgg_gbayesC_data)))

gg[[2]] <- ggplot(qgg_gbayesC_data, aes(x=cor)) +
  geom_histogram(bins=bint, fill='red') +
  labs(x="Corr Coeff") +
  ggtitle("gBayesC Prediction Corr Histogram")


### VARBVS

varbvs_data <- as.data.table(fit_varbvs)
varbvs_data <- transpose(varbvs_data)
colnames(varbvs_data) <- "cor"

#print(paste0("VARBVS"))
#print(paste0("Mean correlation coefficient: ", mean(varbvs_data[,cor])))
#print(paste0("Variance of correlation coefficient: ", var(varbvs_data)))

gg[[3]] <- ggplot(varbvs_data, aes(x=cor)) +
  geom_histogram(bins=bint, fill='red') +
  labs(x="Corr Coeff") +
  ggtitle("varbvs Prediction Corr Histogram")


### GLMNET

glmnet_data <- as.data.table(fit_glmnet)
glmnet_data <- transpose(glmnet_data)
colnames(glmnet_data) <- "cor"

#print(paste0("GLMNET"))
#print(paste0("Mean correlation coefficient: ", mean(glmnet_data[,cor])))
#print(paste0("Variance of correlation coefficient: ", var(glmnet_data)))

gg[[4]] <- ggplot(glmnet_data, aes(x=cor)) +
  geom_histogram(bins=bint, fill='red') +
  labs(x="Corr Coeff") +
  ggtitle("glmnet Prediction Corr Histogram")
result <- data.table(method="greml", meanCoeff=mean(qgg_greml_data[,cor])) 
result[,varCoeff := var(qgg_greml_data)]
                    
temp <- data.table(method="gBayesC", meanCoeff=mean(qgg_gbayesC_data[,cor]))
temp[,varCoeff := var(qgg_gbayesC_data)]
result <- rbind(result, temp)

temp <- data.table(method="varbvs", meanCoeff=mean(varbvs_data[,cor]))
temp[,varCoeff := var(varbvs_data)]
result <- rbind(result, temp)

temp <- data.table(method="glmnet", meanCoeff=mean(glmnet_data[,cor]))
temp[,varCoeff := var(glmnet_data)]
result <- rbind(result, temp)

# Map the time of day to different fill colors
#gg[[5]] <- ggplot(data=result, aes(x=method, y=meanCoeff, fill=method)) +
    #geom_bar(stat="identity")

#boxplot(result$meanCoeff)

gg[[5]] <- ggplot(data=result, aes(x=method, y=meanCoeff, fill=method)) +
    geom_bar(stat="identity") +
    ggtitle("Mean Correlation Coefficient by Method")

gg[[6]] <- ggplot(data=result, aes(x=method, y=varCoeff, fill=method)) +
    geom_bar(stat="identity") +
    ggtitle("Variance of Coefficients by Method")

Correlation Coefficient Histograms

print("Correlation coefficient histograms")
[1] "Correlation coefficient histograms"
plot_grid(gg[[1]],gg[[2]],gg[[3]],gg[[4]], ncol=2)

Method Comparison Summary

plot_grid(gg[[5]],gg[[6]], ncol=2)


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/Software/openblas_0.3.10/lib/libopenblas_haswellp-r0.3.10.dev.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] glmnet_4.1-6      Matrix_1.5-3      varbvs_2.5-16     qgg_1.1.1        
 [5] doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2     qqman_0.1.8      
 [9] cowplot_1.1.1     ggplot2_3.4.1     data.table_1.14.8 dplyr_1.1.0      
[13] workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3        lattice_0.20-45     png_0.1-7          
 [4] getPass_0.2-2       ps_1.6.0            rprojroot_2.0.3    
 [7] digest_0.6.29       utf8_1.2.2          R6_2.5.1           
[10] MatrixModels_0.5-1  evaluate_0.15       coda_0.19-4        
[13] highr_0.9           httr_1.4.2          pillar_1.7.0       
[16] rlang_1.0.6         rstudioapi_0.13     SparseM_1.81       
[19] whisker_0.4         callr_3.7.0         jquerylib_0.1.4    
[22] rmarkdown_2.17      labeling_0.4.2      splines_4.0.3      
[25] statmod_1.5.0       stringr_1.4.0       munsell_0.5.0      
[28] compiler_4.0.3      httpuv_1.6.5        xfun_0.30          
[31] pkgconfig_2.0.3     shape_1.4.6         mcmc_0.9-7         
[34] htmltools_0.5.2     tidyselect_1.2.0    tibble_3.1.6       
[37] codetools_0.2-18    fansi_1.0.3         calibrate_1.7.7    
[40] crayon_1.5.1        withr_2.5.0         later_1.3.0        
[43] MASS_7.3-56         grid_4.0.3          jsonlite_1.8.0     
[46] gtable_0.3.0        lifecycle_1.0.3     git2r_0.30.1       
[49] magrittr_2.0.3      scales_1.2.0        cli_3.6.0          
[52] stringi_1.7.6       farver_2.1.0        fs_1.5.2           
[55] promises_1.2.0.1    latticeExtra_0.6-29 bslib_0.3.1        
[58] ellipsis_0.3.2      generics_0.1.2      vctrs_0.5.2        
[61] nor1mix_1.3-0       RColorBrewer_1.1-3  tools_4.0.3        
[64] glue_1.6.2          jpeg_0.1-9          survival_3.3-1     
[67] processx_3.5.3      fastmap_1.1.0       yaml_2.3.5         
[70] colorspace_2.0-3    knitr_1.38          sass_0.4.1         
[73] quantreg_5.94       MCMCpack_1.6-3