Last updated: 2022-07-18
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Knit directory: mmbr-rss-dsc/
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Canonical patterns of sharing:
R = 20
prior = mvsusieR:::create_cov_canonical(R)Paired sharing:
paired = matrix(0,R,R)
paired[1:2,1:2] = 1
prior[['paired_1']] = paired
paired = matrix(0,R,R)
paired[10:11,10:11] = 1
prior[['paired_2']] = pairedBlock sharing:
block = matrix(0,R,R)
block[1:R/2, 1:R/2] = 1
block[(R/2+1):R, (R/2+1):R] = 1
prior[['blocked_1']] = blockWe assign weights to priors:
singleton total 25%
singleton_1 has 15%
singleton_2 to singleton_11 has 10% (1% each)
shared total 25%
paired 30% (15% each)
blocked 20%
w = c(0.15, rep(0.01, 10), rep(0, 9), rep(0.05, 5), rep(0.15,2), 0.2)prior = prior[which(w>0)]
w = w[which(w>0)]
artificial_mixture_20 = list(U=prior,w=w)Canonical patterns of sharing:
R = 4
prior = mvsusieR:::create_cov_canonical(R)Paired sharing:
paired = matrix(0,R,R)
paired[1:2,1:2] = 1
prior[['paired_1']] = pairedBlock sharing:
block = matrix(0,R,R)
block[1:R/2, 1:R/2] = 1
block[(R/2+1):R, (R/2+1):R] = 1
prior[['blocked_1']] = blockWe assign weights to priors:
singleton total 30%
shared total 50%
paired 10%
blocked 10%
w = c(0.15, 0.15, 0, 0, rep(0.5/5,5), 0.1, 0.1)prior = prior[which(w>0)]
names(prior)[1] "singleton_1" "singleton_2" "shared_1" "shared_2" "shared_3"
[6] "shared_4" "shared_5" "paired_1" "blocked_1" w = w[which(w>0)]
artificial_mixture_4 = list(U=prior,w=w)Canonical patterns of sharing:
R = 2
prior = mvsusieR:::create_cov_canonical(R)We assign weights to priors:
singleton total 40%
shared total 60%
w = c(0.2, 0.2, rep(0.6/5,5))prior = prior[which(w>0)]
names(prior)[1] "singleton_1" "singleton_2" "shared_1" "shared_2" "shared_3"
[6] "shared_4" "shared_5" w = w[which(w>0)]
artificial_mixture_2 = list(U=prior,w=w)Canonical patterns of sharing:
R = 2
prior = list(independent=diag(2))
w = c(1)names(prior)[1] "independent"artificial_mixture_2_indep = list(U=prior,w=w)Canonical patterns of sharing:
R = 3
prior = mvsusieR:::create_cov_canonical(R)Paired sharing:
paired = matrix(0,R,R)
paired[1:2,1:2] = 1
prior[['paired_1']] = pairedWe assign weights to priors:
singleton total 30%
singleton_1 has 15%
singleton_2 has 5%
singleton_3 has 10%
shared total 50% (10% each)
paired 20%
w = c(0.15, 0.05, 0.1, rep(0.1, 5), 0.2)prior = prior[which(w>0)]
w = w[which(w>0)]
artificial_mixture_3 = list(U=prior,w=w)The priors are from this workflow.
prior = readRDS('/project2/mstephens/yuxin/ukb-bloodcells/analysis_20220619/ukbbloodcells_prepare.ed_bovy.ukbbloodcells_prepare.Ycor.rds')tol=1E-10
U = prior$U[which(prior$w>tol)]
w = prior$w[which(prior$w>tol)]
bloodcells_mixture = list(U=U,w=w)for(i in 1:length(U)){
bloodcells_mixture$U[[i]] = (bloodcells_mixture$U[[i]] + t(bloodcells_mixture$U[[i]]))/2
eigenU = eigen(bloodcells_mixture$U[[i]], symmetric = T)
if(any(eigenU$values<0)){
eigenU$values[eigenU$values < 0] = 0
bloodcells_mixture$U[[i]] = eigenU$vectors %*% (t(eigenU$vectors) * eigenU$values)
}
}saveRDS(list(bloodcells_mixture=bloodcells_mixture, artificial_mixture_20=artificial_mixture_20, artificial_mixture_4=artificial_mixture_4, artificial_mixture_2 = artificial_mixture_2, artificial_mixture_2_indep = artificial_mixture_2_indep, artificial_mixture_3 = artificial_mixture_3), 'output/ukb_prior_simulation_20220619.rds')
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] 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_1.4-1 tidyselect_1.1.2
[4] xfun_0.24 bslib_0.2.5.1 ashr_2.2-54
[7] purrr_0.3.4 lattice_0.20-44 colorspace_2.0-3
[10] vctrs_0.4.1 generics_0.1.2 htmltools_0.5.1.1
[13] yaml_2.3.5 utf8_1.2.2 rlang_1.0.2
[16] mixsqp_0.3-43 jquerylib_0.1.4 later_1.2.0
[19] pillar_1.7.0 DBI_1.1.1 glue_1.6.2
[22] matrixStats_0.62.0 plyr_1.8.7 mashr_0.2.59
[25] lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
[28] gtable_0.3.0 mvtnorm_1.1-3 evaluate_0.14
[31] knitr_1.33 httpuv_1.6.1 invgamma_1.1
[34] irlba_2.3.5 fansi_1.0.3 Rcpp_1.0.8.3
[37] susieR_0.12.12 promises_1.2.0.1 scales_1.2.0
[40] rmeta_3.0 jsonlite_1.8.0 truncnorm_1.0-8
[43] abind_1.4-5 fs_1.5.0 hms_1.1.1
[46] ggplot2_3.3.6 digest_0.6.29 stringi_1.7.6
[49] dplyr_1.0.9 grid_4.1.0 rprojroot_2.0.2
[52] cowplot_1.1.1 cli_3.3.0 tools_4.1.0
[55] magrittr_2.0.3 sass_0.4.0 tibble_3.1.7
[58] crayon_1.5.1 whisker_0.4 pkgconfig_2.0.3
[61] ellipsis_0.3.2 Matrix_1.3-3 prettyunits_1.1.1
[64] SQUAREM_2021.1 reshape_0.8.9 assertthat_0.2.1
[67] rmarkdown_2.9 rstudioapi_0.13 R6_2.5.1
[70] mvsusieR_0.0.3.0518 git2r_0.28.0 compiler_4.1.0