GTEx real data

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Last updated: 2020-11-06

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Knit directory: mmbr-rss-dsc/

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---

Here is one gene identified in MASH paper that have different signs for brain vs non brain tissues.

# processing code
compute_maf <- function(geno){
  f <- mean(geno,na.rm = TRUE)/2
  return(min(f, 1-f))
}

compute_missing <- function(geno){
  miss <- sum(is.na(geno))/length(geno)
  return(miss)
}

mean_impute <- function(geno){
  f <- apply(geno, 2, function(x) mean(x,na.rm = TRUE))
  for (i in 1:length(f)) geno[,i][which(is.na(geno[,i]))] <- f[i]
  return(geno)
}

is_zero_variance <- function(x) {
  if (length(unique(x))==1) return(T)
  else return(F)
}

filter_X <- function(X, missing_rate_thresh, maf_thresh) {
  rm_col <- which(apply(X, 2, compute_missing) > missing_rate_thresh)
  if (length(rm_col)) X <- X[, -rm_col]
  rm_col <- which(apply(X, 2, compute_maf) < maf_thresh)
  if (length(rm_col)) X <- X[, -rm_col]
  rm_col <- which(apply(X, 2, is_zero_variance))
  if (length(rm_col)) X <- X[, -rm_col]
  return(mean_impute(X))
}

compute_cov_flash <- function(Y, error_cache = NULL){
  covar <- diag(ncol(Y))
  tryCatch({
    fl <- flashier::flash(Y, var.type = 2, prior.family = c(flashier::prior.normal(), flashier::prior.normal.scale.mix()), backfit = TRUE, verbose.lvl=0)
    if(fl$n.factors==0){
      covar <- diag(fl$residuals.sd^2)
    } else {
      fsd <- sapply(fl$fitted.g[[1]], '[[', "sd")
      covar <- diag(fl$residuals.sd^2) + crossprod(t(fl$flash.fit$EF[[2]]) * fsd)
    }
    if (nrow(covar) == 0) {
      covar <- diag(ncol(Y))
      stop("Computed covariance matrix has zero rows")
    }
  }, error = function(e) {
    if (!is.null(error_cache)) {
      saveRDS(list(data=Y, message=warning(e)), error_cache)
      warning("FLASH failed. Using Identity matrix instead.")
      warning(e)
    } else {
      stop(e)
    }
  })
  s <- apply(Y, 2, sd, na.rm=T)
  if (length(s)>1) s = diag(s)
  else s = matrix(s,1,1)
  covar <- s%*%cov2cor(covar)%*%s
  return(covar)
}

get_center <- function(k,n) {
  ## For given number k, get the range k surrounding n/2
  ## but have to make sure it does not go over the bounds
  if (is.null(k)) {
    return(1:n)
  }
  start = floor(n/2 - k/2)
  end = floor(n/2 + k/2)
  if (start<1) start = 1
  if (end>n) end = n
  return(start:end)
}

dat = readRDS('data/ENSG00000140265.12.Multi_Tissues.rds')
prior = 'data/FastQTLSumStats.mash.FL_PC3.rds'
cis = 500
U = readRDS(prior)$Ulist
weights = rep(1/length(U), length(U))
prior = mmbr::create_mash_prior(mixture_prior=list(weights=weights, matrices=U))
resid_Y = compute_cov_flash(dat$y_res)
X = filter_X(dat$X, 0.1, 0.05)
X = X[,get_center(cis, ncol(X))]
Y = dat$y_res

The covariance/correlation matrix of Y using pairwise complete observations:

library(corrplot)

corrplot 0.84 loaded

par(mfrow=c(1,2))
corrplot(cov(Y, use='pairwise.complete.obs'), method='color', type='upper', tl.col="black", tl.srt=45, is.corr = FALSE)
corrplot(cor(Y, use='pairwise.complete.obs'), method='color', type='upper', tl.col="black", tl.srt=45, is.corr = TRUE)

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
2c1cd2e	zouyuxin	2020-10-25
c935589	zouyuxin	2020-10-25
048c109	zouyuxin	2020-10-25

The covarince/correlation matrix of Y using FLASH:

colnames(resid_Y) = rownames(resid_Y) = colnames(Y)
par(mfrow=c(1,2))
corrplot(resid_Y, method='color', type='upper', tl.col="black", tl.srt=45, is.corr = FALSE)
corrplot(cov2cor(resid_Y), method='color', type='upper', tl.col="black", tl.srt=45, is.corr = TRUE)

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
2c1cd2e	zouyuxin	2020-10-25
c935589	zouyuxin	2020-10-25
048c109	zouyuxin	2020-10-25

Models with L = 10

We fit 4 models with L = 10:

1. model with exact computation

2. model with exact computation using diagonal residual variance

3. model with approximate computation

4. model with approximate computation using diagonal residual variance

We expect model 2 and 4 have same results.

library(profvis)
prof1 = profvis({
  stime <- proc.time()
  res <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=FALSE)
  etime <- proc.time()
  time_res <- etime - stime
  },prof_output='output/mmbr_gtex_res.Rprof')
saveRDS(list(result = res, result_time = time_res, result_profile = prof1), 'output/GTExprofile_res.rds')
rm(res)
rm(prof1)

prof2 = profvis({
  stime <- proc.time()
  res_diag <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=diag(diag(resid_Y)), approximate=FALSE)
  etime <- proc.time()
  time_res_diag <- etime - stime
  },prof_output='output/mmbr_gtex_res_diag.Rprof')
saveRDS(list(result = res_diag, result_time = time_res_diag, result_profile = prof2), 'output/GTExprofile_resdiag.rds')
rm(res_diag)
rm(prof2)

prof3 = profvis({
  stime <- proc.time()
  res_approx <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=TRUE,, L=3)
  etime <- proc.time()
  time_res_approx <- etime - stime
  },prof_output='output/mmbr_gtex_res_approx.Rprof')
saveRDS(list(result = res_approx, result_time = time_res_approx, result_profile = prof3), 'output/GTExprofile_resapprox.rds')
rm(res_approx)
rm(prof3)

prof4 = profvis({
  stime <- proc.time()
  res_approx_diag <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=diag(diag(resid_Y)), approximate=TRUE, L=3)
  etime <- proc.time()
  time_res_approx_diag <- etime - stime
  },prof_output='output/mmbr_gtex_res_approx_diag.Rprof')
saveRDS(list(result = res_approx_diag, result_time = time_res_approx_diag, result_profile = prof4), 'output/GTExprofile_resapproxdiag.rds')
rm(res_approx_diag)
rm(prof4)

Load models:

library(mmbr)

Loading required package: mashr

Loading required package: ashr

Loading required package: susieR

res1 = readRDS('output/GTExprofile_res.rds')
res2 = readRDS('output/GTExprofile_resdiag.rds')
res3 = readRDS('output/GTExprofile_resapprox.rds')
res4 = readRDS('output/GTExprofile_resapproxdiag.rds')

As we expected, model 2 and 4 are same.

all.equal(res2$result$pip, res4$result$pip)

[1] TRUE

We check results from model 1, 3 and 4.

Model 1 credible sets:

susie_plot(res1$result, y='PIP')

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25
1ff70cd	zouyuxin	2020-10-21

Model 3 credible sets:

susie_plot(res3$result, y='PIP')

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25

Model 4 credible sets:

susie_plot(res4$result, y='PIP')

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25

Model 1 and 3 both give 5 same Credible Sets. There are 3 CSs have overlapped SNPs.

	Total Time	Algorithm Time	# iterations
model 1	23097.391	22887.729	10
model 2	39748.754	39524.412	18
model 3	2116.505	2077.43	10
model 4	3165.6	3129.651	18

univariate_res = lapply(1:ncol(Y), function(i) susieR:::univariate_regression(X,Y[,i]))
res1$result$bhat = do.call(cbind, lapply(1:ncol(Y), function(i) univariate_res[[i]]$betahat))
res1$result$shat = do.call(cbind, lapply(1:ncol(Y), function(i) univariate_res[[i]]$sebetahat))
rownames(res1$result$bhat) = 1:501
p = mmbr::mmbr_plot(res1$result, original_sumstat = TRUE, cs_only = FALSE)
pdf('docs/assets/GRExProfile/GTExprofile_univ.pdf', width = 100, height = 15)
print(p$plot)
dev.off()

Univariate Effects

p = mmbr::mmbr_plot(res1$result)
pdf('docs/assets/GRExProfile/GTExprofile_res.pdf', width = 60, height = 15)
print(p$plot)
dev.off()

Model 1 Effects

p = mmbr::mmbr_plot(res3$result)
pdf('docs/assets/GRExProfile/GTExprofile_resapprox.pdf', width = 60, height = 15)
print(p$plot)
dev.off()

Model 3 Effects

p = mmbr::mmbr_plot(res4$result)
pdf('docs/assets/GRExProfile/GTExprofile_resapproxdiag.pdf', width = 5, height = 15)
print(p$plot)
dev.off()

Model 4 result

Models with L = 3

stime <- proc.time()
res <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=FALSE, L=3)
etime <- proc.time()
time_res <- etime - stime
saveRDS(list(result = res, result_time = time_res), 'output/GTExprofile_resL3.rds')

stime <- proc.time()
res_approx <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=TRUE, L=3)
etime <- proc.time()
time_res_approx <- etime - stime
saveRDS(list(result = res_approx, result_time = time_res_approx), 'output/GTExprofile_resapproxL3.rds')

stime <- proc.time()
res_approx_diag <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=diag(diag(resid_Y)), approximate=TRUE, L=3)
etime <- proc.time()
time_res_approx_diag <- etime - stime
saveRDS(list(result = res_approx_diag, result_time = time_res_approx_diag), 'output/GTExprofile_resapproxdiagL3.rds')

res1_L3 = readRDS('output/GTExprofile_resL3.rds')
res3_L3 = readRDS('output/GTExprofile_resapproxL3.rds')
res4_L3 = readRDS('output/GTExprofile_resapproxdiagL3.rds')

Model 1 credible sets:

susie_plot(res1_L3$result, y='PIP')

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25
b88f57a	zouyuxin	2020-10-22

Model 3 credible sets:

susie_plot(res3_L3$result, y='PIP')

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25

Model 4 credible sets:

susie_plot(res4_L3$result, y='PIP')

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
048c109	zouyuxin	2020-10-25

	Total Time	Algorithm Time	# iterations
model 1	5793.07	5558.434	10
model 3	484.594	447.362	10
model 4	1232.319	1196.183	18

p = mmbr::mmbr_plot(res1_L3$result)
pdf('docs/assets/GRExProfile/GTExprofile_resL3.pdf', width = 17, height = 15)
print(p$plot)
dev.off()

Model 1 L3 Effects

Models with L = 1

stime <- proc.time()
res <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=FALSE, L=1)
etime <- proc.time()
time_res <- etime - stime
saveRDS(list(result = res, result_time = time_res), 'output/GTExprofile_resL1.rds')

stime <- proc.time()
res_approx <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=resid_Y, approximate=TRUE, L=1)
etime <- proc.time()
time_res_approx <- etime - stime
saveRDS(list(result = res_approx, result_time = time_res_approx), 'output/GTExprofile_resapproxL1.rds')

stime <- proc.time()
res_approx_diag <- mmbr::msusie(X, Y, prior_variance=prior, residual_variance=diag(diag(resid_Y)), approximate=TRUE, L=1)
etime <- proc.time()
time_res_approx_diag <- etime - stime
saveRDS(list(result = res_approx_diag, result_time = time_res_approx_diag), 'output/GTExprofile_resapproxdiagL1.rds')

res1_L1 = readRDS('output/GTExprofile_resL1.rds')
res3_L1 = readRDS('output/GTExprofile_resapproxL1.rds')
res4_L1 = readRDS('output/GTExprofile_resapproxdiagL1.rds')

Model 1 credible sets:

susie_plot(res1_L1$result, y='PIP')

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
04c585c	zouyuxin	2020-10-26

Model 3 credible sets:

susie_plot(res3_L1$result, y='PIP')

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
04c585c	zouyuxin	2020-10-26

Model 4 credible sets:

susie_plot(res4_L1$result, y='PIP')

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
04c585c	zouyuxin	2020-10-26

Session information

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] mmbr_0.0.1.0305 susieR_0.9.26   mashr_0.2.40    ashr_2.2-51    
[5] corrplot_0.84   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] progress_1.2.2     tidyselect_1.1.0   xfun_0.19          purrr_0.3.4       
 [5] lattice_0.20-41    colorspace_1.4-1   vctrs_0.3.4        generics_0.1.0    
 [9] htmltools_0.5.0    yaml_2.2.1         rlang_0.4.8        mixsqp_0.3-46     
[13] later_1.1.0.1      pillar_1.4.6       glue_1.4.2         plyr_1.8.6        
[17] matrixStats_0.57.0 lifecycle_0.2.0    stringr_1.4.0      munsell_0.5.0     
[21] gtable_0.3.0       flashier_0.2.7     mvtnorm_1.1-1      evaluate_0.14     
[25] knitr_1.30         httpuv_1.5.4       invgamma_1.1       parallel_3.6.3    
[29] irlba_2.3.3        Rcpp_1.0.5         promises_1.1.1     backports_1.2.0   
[33] scales_1.1.1       rmeta_3.0          truncnorm_1.0-8    abind_1.4-5       
[37] fs_1.5.0           ggplot2_3.3.2      hms_0.5.3          digest_0.6.27     
[41] stringi_1.5.3      dplyr_1.0.2        ebnm_0.1-24        grid_3.6.3        
[45] rprojroot_1.3-2    tools_3.6.3        magrittr_1.5       tibble_3.0.4      
[49] crayon_1.3.4       whisker_0.4        pkgconfig_2.0.3    ellipsis_0.3.1    
[53] Matrix_1.2-18      SQUAREM_2020.5     prettyunits_1.1.1  assertthat_0.2.1  
[57] reshape_0.8.8      rmarkdown_2.5      rstudioapi_0.11    R6_2.5.0          
[61] git2r_0.27.1       compiler_3.6.3