Method comparison: Power vs. fdr
Yunqi Yang
01/29/2024
Last updated: 2024-03-25
Checks: 6 1
Knit directory: survival-susie/
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Description:
Comparing power vs. FDR across 4 methods. I vary the threshold for claiming effect variables based on marginal PIP value.
Data details:
\(n=50000\) and \(p=1000\).
SNPs with minor allele frequency < 1% were removed.
Fitting details:
SuSIE with laplace BF.
See https://yunqiyang0215.github.io/survival-susie/pip_comparison_large_sample.html
source("/project2/mstephens/yunqiyang/surv-susie/survival-susie/code/post_summary.R")susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie.rds")
survsvb = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/survsvb.rds")
bvsnlp = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/bvsnlp.rds")
r2b = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/r2b.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie_rss.rds")t95 = 9511. Results using real correlation structure from data
par(mfrow = c(2,3), cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
for (i in 1:length(censor_lvl)){
indx = which(susie$simulate.cor_type == "real" & susie$simulate.censor_lvl == censor_lvl[i])
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$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$susie_rss.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
ts = seq(from = 0, to = 1, by = 0.001)
res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
res.svb = calculate_tpr_vs_fdr(pip.survsvb, is_effect, ts)
res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
res.r2b = calculate_tpr_vs_fdr(pip.r2b, is_effect, ts)
plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
lines(res.svb[,2], res.svb[,1], type = "l", col = 2)
lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
lines(res.rss[,2], res.rss[,1], type = "l", col = 4)
lines(res.r2b[,2], res.r2b[,1], type = "l", col = 5)
points(res.susie[t95,2], res.susie[t95, 1])
points(res.svb[t95,2], res.svb[t95, 1])
points(res.bvsnlp[t95,2], res.bvsnlp[t95, 1])
points(res.rss[t95,2], res.rss[t95, 1])
points(res.r2b[t95,2], res.r2b[t95, 1])
legend("topleft", legend = c("susie", "survival.svb", "bvsnlp", "r2b"), col = c(1,2,3, 4), lty = 1)
}
The dots indicate PIP threshold = 0.95
2. Results using independent X, with data from null model.
par(mfrow = c(2,3), cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
for (i in 1:length(censor_lvl)){
indx = which(susie$simulate.cor_type == "independent" & susie$simulate.censor_lvl == censor_lvl[i])
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$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$susie_rss.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
ts = seq(from = 0, to = 1, by = 0.001)
res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
res.svb = calculate_tpr_vs_fdr(pip.survsvb, is_effect, ts)
res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
res.r2b = calculate_tpr_vs_fdr(pip.r2b, is_effect, ts)
plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
lines(res.svb[,2], res.svb[,1], type = "l", col = 2)
lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
lines(res.rss[,2], res.rss[,1], type = "l", col = 4)
lines(res.r2b[,2], res.r2b[,1], type = "l", col = 5)
points(res.susie[t95,2], res.susie[t95, 1])
points(res.svb[t95,2], res.svb[t95, 1])
points(res.bvsnlp[t95,2], res.bvsnlp[t95, 1])
points(res.rss[t95,2], res.rss[t95, 1])
points(res.r2b[t95,2], res.r2b[t95, 1])
legend("bottomright", legend = c("susie", "survival.svb", "bvsnlp", "r2b"), col = c(1,2,3, 4), lty = 1)
}
The dots indicate PIP threshold = 0.95.
3. Time comparison
res = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/time_comparison.rds")
t = unlist(lapply(1:length(res), function(i) mean(res[[i]][,5], , na.rm = TRUE)))
names(t) = c("susie", "bvsnlp", "susie_rss", "svb", "r2b")
print(t)
# susie bvsnlp susie_rss svb r2b
# 7982.7163 3727.9983 239.3761 2577.9607 11610.1981
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