Compare PIPs: small sample + 10 iterations
Yunqi Yang
4/6/2023
Last updated: 2024-03-11
Checks: 6 1
Knit directory: survival-susie/
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| html | d7d95b5 | yunqi yang | 2024-03-11 | Build site. |
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Description:
Simulation results based on real genotype data from GTEx. I took the SNPs for a certain gene, Thyroid.ENSG00000132855. Therefore, sample size \(n=584\), and I choose variable number \(p=1000\).
Steps for simulating survival data:
Select a random point on the genome, indx_start. Then the predictors are from [indx_start:(indx_start + p -1)].
Two correlation types: real correlation and independent. In independent, I simply permute each variable values. The max correlation can be 0.3, but a lot of correlation will be close to 0.
Effect size \(b\sim N(0,1)\).
Simulate survival time using exponential survival model.
Fitting:
susie: \(L=5\) and estimate prior covariance
r2b was set to default number of iterations
source("/project2/mstephens/yunqiyang/surv-susie/survival-susie/code/pip.R")susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_niter10/susie.rds")
svb = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_niter10/svb.rds")
bvsnlp = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_niter10/bvsnlp.rds")
r2b = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_niter10/r2b.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_niter10/rss.rds")censor_lvls = unique(susie$simulate.censor_lvl)1. Use the real correlation of SNPs
par(mfrow = c(5,4))
for (i in 1:length(censor_lvls)){
indx = which(susie$simulate.cor_type == "real" & susie$simulate.censor_lvl == censor_lvls[i])
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.svb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
res.pip = compare_pip(pip.susie, pip.svb, is_effect)
plot_pip(res.pip, labs = c("susie", "survival.svb"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.bvsnlp, is_effect)
plot_pip(res.pip, labs = c("susie", "bvsnlp"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.r2b, is_effect)
plot_pip(res.pip, labs = c("susie", "r2b"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.rss, is_effect)
plot_pip(res.pip, labs = c("susie", "rss"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
}
par(mfrow = c(5,4))
for (i in 1:length(censor_lvls)){
indx = which(bvsnlp$simulate.cor_type == "real" & bvsnlp$simulate.censor_lvl == censor_lvls[i])
is_effect = unlist(lapply(indx, function(x) bvsnlp$simulate.is_effect[[x]]))
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
# Calibration plot for pip.susie
calibration.susie = pip_calibration(pip.susie, is_effect)
plot_calibration(calibration.susie, main = paste0("susie: censor=", censor_lvls[i]))
# Calibration plot for pip.svb
calibration.survsvb = pip_calibration(pip.survsvb, is_effect)
plot_calibration(calibration.survsvb, main = paste0("svb: censor=", censor_lvls[i]))
# Calibration plot for pip.bvsnlp
calibration.bvsnlp = pip_calibration(pip.bvsnlp, is_effect)
plot_calibration(calibration.bvsnlp, main = paste0("bvsnlp: censor=", censor_lvls[i]))
# Calibration plot for pip.rss
calibration.rss = pip_calibration(pip.rss, is_effect)
plot_calibration(calibration.rss, main = paste0("rss: censor=", censor_lvls[i]))
# Calibration plot for pip.r2b
calibration.r2b = pip_calibration(pip.r2b, is_effect)
plot_calibration(calibration.r2b, main = paste0("r2b: censor=", censor_lvls[i]))
}
2. Near independent SNPs
par(mfrow = c(5,4))
for (i in 1:length(censor_lvls)){
indx = which(susie$simulate.cor_type == "independent" & susie$simulate.censor_lvl == censor_lvls[i])
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.svb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
res.pip = compare_pip(pip.susie, pip.svb, is_effect)
plot_pip(res.pip, labs = c("susie", "survival.svb"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.bvsnlp, is_effect)
plot_pip(res.pip, labs = c("susie", "bvsnlp"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.r2b, is_effect)
plot_pip(res.pip, labs = c("susie", "r2b"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
res.pip = compare_pip(pip.susie, pip.rss, is_effect)
plot_pip(res.pip, labs = c("susie", "rss"), main = paste0("censor=",censor_lvls[i]))
abline(a = 0, b = 1, lty = 2, col = "blue")
}
par(mfrow = c(5,4))
for (i in 1:length(censor_lvls)){
indx = which(bvsnlp$simulate.cor_type == "independent" & bvsnlp$simulate.censor_lvl == censor_lvls[i])
is_effect = unlist(lapply(indx, function(x) bvsnlp$simulate.is_effect[[x]]))
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
# Calibration plot for pip.susie
calibration.susie = pip_calibration(pip.susie, is_effect)
plot_calibration(calibration.susie, main = paste0("susie: censor=", censor_lvls[i]))
# Calibration plot for pip.svb
calibration.survsvb = pip_calibration(pip.survsvb, is_effect)
plot_calibration(calibration.survsvb, main = paste0("svb: censor=", censor_lvls[i]))
# Calibration plot for pip.bvsnlp
calibration.bvsnlp = pip_calibration(pip.bvsnlp, is_effect)
plot_calibration(calibration.bvsnlp, main = paste0("bvsnlp: censor=", censor_lvls[i]))
# Calibration plot for pip.rss
calibration.rss = pip_calibration(pip.rss, is_effect)
plot_calibration(calibration.rss, main = paste0("rss: censor=", censor_lvls[i]))
# Calibration plot for pip.r2b
calibration.r2b = pip_calibration(pip.r2b, is_effect)
plot_calibration(calibration.r2b, main = paste0("r2b: censor=", censor_lvls[i]))
}
| Version | Author | Date |
|---|---|---|
| d7d95b5 | yunqi yang | 2024-03-11 |
| Version | Author | Date |
|---|---|---|
| d7d95b5 | yunqi yang | 2024-03-11 |
sessionInfo()
# R version 4.2.0 (2022-04-22)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: CentOS Linux 7 (Core)
#
# 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 LC_TIME=C
# [4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
# [7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
# [10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] workflowr_1.7.0
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.8.3 highr_0.9 bslib_0.3.1 compiler_4.2.0
# [5] pillar_1.7.0 later_1.3.0 git2r_0.30.1 jquerylib_0.1.4
# [9] tools_4.2.0 getPass_0.2-2 digest_0.6.29 jsonlite_1.8.0
# [13] evaluate_0.15 tibble_3.1.7 lifecycle_1.0.1 pkgconfig_2.0.3
# [17] rlang_1.0.2 cli_3.3.0 rstudioapi_0.13 yaml_2.3.5
# [21] xfun_0.30 fastmap_1.1.0 httr_1.4.3 stringr_1.4.0
# [25] knitr_1.39 sass_0.4.1 fs_1.5.2 vctrs_0.4.1
# [29] rprojroot_2.0.3 glue_1.6.2 R6_2.5.1 processx_3.8.0
# [33] fansi_1.0.3 rmarkdown_2.14 callr_3.7.3 magrittr_2.0.3
# [37] whisker_0.4 ps_1.7.0 promises_1.2.0.1 htmltools_0.5.2
# [41] ellipsis_0.3.2 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6
# [45] crayon_1.5.1