mvSuSiE RSS ukb Random vs Default
Yuxin Zou
03/16/2021
Last updated: 2021-04-09
Checks: 7 0
Knit directory: mmbr-rss-dsc/
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The data is simulated using UKB bloodcells patterns.
dat = readRDS('output/ukb_rss_20210313/data_ukb_14.rds')
simu = readRDS('output/ukb_rss_20210313/data_ukb_14_ukb_bloodcells_mixture_1.rds')
ld <- readRDS('output/ukb_rss_20210313/bloodcells_chr1.36507801.37193916.ld.rds')
meta <- simu$meta
suffstats <- simu$suffstats
sumstats <- simu$sumstats
L <- 10
maxiter <- 1000The true cofficients are
true_coef = meta$true_coef
beta_idx = which(rowSums(true_coef!=0)>0) # 631 2877
par(mfrow=c(1,2))
barplot(true_coef[beta_idx[1],])
barplot(true_coef[beta_idx[2],])
The z scores for true signals:
resid_Z = meta$residual_variance
Z = as.matrix(sumstats$bhat/sumstats$sbhat)
Z[is.na(Z)] = 0
par(mfrow=c(1,2))
barplot(Z[beta_idx[1],], las=2, cex.names = 0.7)
barplot(Z[beta_idx[2],], las=2, cex.names = 0.7)
Using Default prior,
priorUn = meta$prior[['naive']]
m_initn = mvsusieR::create_mash_prior(mixture_prior = list(matrices=priorUn$xUlist, weights=priorUn$pi),
null_weight=priorUn$null_weight, max_mixture_len=-1)
result = mvsusieR::mvsusie_rss(Z, ld, L=2, prior_variance=m_initn,
residual_variance=resid_Z,compute_objective=T,
estimate_prior_variance=T, estimate_prior_method='EM',
precompute_covariances=T, n_thread=1,
max_iter=100, track_fit = T)Iteration 1 delta = InfIteration 2 delta = 9.57970887208648Iteration 3 delta = 7.61937417403533Iteration 4 delta = 2.55643740653613Iteration 5 delta = 0.485033667078824Iteration 6 delta = 0.101214589871233Iteration 7 delta = 0.0354468342775363Iteration 8 delta = 0.0215204256164725Iteration 9 delta = 0.0174761971356929Iteration 10 delta = 0.015664506048779Iteration 11 delta = 0.0144726800062926Iteration 12 delta = 0.137843744654674susieR::susie_plot(result, y='PIP', b=true_coef, main=paste0('ELBO = ', round(susieR::susie_get_objective(result), 2)))
Using identity prior,
priorUi = meta$prior[['identity']]
m_initi = mvsusieR::create_mash_prior(mixture_prior = list(matrices=priorUi$xUlist, weights=priorUi$pi),
null_weight=priorUi$null_weight, max_mixture_len=-1)
result_ide = mvsusieR::mvsusie_rss(Z, ld, L=2, prior_variance=m_initi, residual_variance=resid_Z,
compute_objective=T, estimate_prior_variance=T, estimate_prior_method='EM',
precompute_covariances=T, n_thread=1, max_iter=100, track_fit = T)Iteration 1 delta = InfIteration 2 delta = 10.086834519534Iteration 3 delta = 8.64865685154655Iteration 4 delta = 3.6689518900821Iteration 5 delta = 1.1973131689374Iteration 6 delta = 0.425353791688394Iteration 7 delta = 0.134743233851623Iteration 8 delta = 0.0360138489340898Iteration 9 delta = 0.00858377601252869Iteration 10 delta = 0.00192239973694086susieR::susie_plot(result_ide, y='PIP', b=true_coef, main=paste0('ELBO = ', round(susieR::susie_get_objective(result_ide), 2)))
I set estimate prior variance = FALSE, and run with 1 iteration.
result1 = mvsusieR::mvsusie_rss(Z, ld, L=2, prior_variance=m_initn,
residual_variance=resid_Z,compute_objective=T,
estimate_prior_variance=F, estimate_prior_method='EM',
precompute_covariances=T, n_thread=1,
max_iter=1, track_fit = T)Warning in SuSiE_model$fit(data, prior_weights, estimate_prior_method,
check_null_threshold, : IBSS failed to converge after 1 iterations. Perhaps you
should increase max_iter and try again.Iteration 1 delta = InfsusieR::susie_plot(result1, y='PIP', b=true_coef, main=paste0('ELBO = ', round(susieR::susie_get_objective(result1), 2)))
result_ide1 = mvsusieR::mvsusie_rss(Z, ld, L=2, prior_variance=m_initi, residual_variance=resid_Z,
compute_objective=T, estimate_prior_variance=F, estimate_prior_method='EM',
precompute_covariances=T, n_thread=1, max_iter=1, track_fit = T)Warning in SuSiE_model$fit(data, prior_weights, estimate_prior_method,
check_null_threshold, : IBSS failed to converge after 1 iterations. Perhaps you
should increase max_iter and try again.Iteration 1 delta = InfsusieR::susie_plot(result_ide1, y='PIP', b=true_coef, main=paste0('ELBO = ', round(susieR::susie_get_objective(result_ide1), 2)))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] progress_1.2.2 softImpute_1.4 tidyselect_1.1.0
[4] xfun_0.22 reshape2_1.4.4 purrr_0.3.4
[7] ashr_2.2-51 lattice_0.20-41 colorspace_2.0-0
[10] vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[13] yaml_2.2.1 utf8_1.2.1 rlang_0.4.10
[16] mixsqp_0.3-46 later_1.1.0.1 pillar_1.5.1
[19] DBI_1.1.1 glue_1.4.2 mashr_0.2.43
[22] plyr_1.8.6 matrixStats_0.58.0 lifecycle_1.0.0
[25] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0
[28] mvtnorm_1.1-1 evaluate_0.14 knitr_1.31
[31] httpuv_1.5.5 invgamma_1.1 irlba_2.3.3
[34] fansi_0.4.2 highr_0.8 Rcpp_1.0.6
[37] promises_1.2.0.1 scales_1.1.1 susieR_0.10.1
[40] rmeta_3.0 truncnorm_1.0-8 abind_1.4-5
[43] fs_1.5.0 flashr_0.6-7 ggplot2_3.3.3
[46] hms_1.0.0 digest_0.6.27 stringi_1.5.3
[49] dplyr_1.0.5 grid_4.0.3 rprojroot_2.0.2
[52] tools_4.0.3 magrittr_2.0.1 tibble_3.1.0
[55] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[58] ellipsis_0.3.1 Matrix_1.3-2 SQUAREM_2021.1
[61] prettyunits_1.1.1 reshape_0.8.8 assertthat_0.2.1
[64] rmarkdown_2.7 R6_2.5.0 mvsusieR_0.0.3.0436
[67] git2r_0.28.0 compiler_4.0.3