Last updated: 2024-09-15
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Knit directory: survival-susie/
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get_mean_abs_corr <- function(res.cs, dat_indx){
res.mac <- sapply(dat_indx, function(i) {
if (!is.null(res.cs[[i]])) {
return(res.cs[[i]]$purity$mean.abs.corr)
} else {
return(NULL)
}
})
return(unlist(res.mac))
}source("./code/post_summary.R")susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/susie.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/rss.rds")pdf("/project2/mstephens/yunqiyang/surv-susie/survival-susie/output/cs_gtex.pdf", width = 10, height = 6)
par(mfrow = c(2,3))
# 1. coverage of cs
coverage = matrix(NA, nrow = 2, ncol = 3)
rownames(coverage) = c("susie", "susie.rss")
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3) {
dat_indx = which(susie$simulate.num_effect == i)
coverage[1, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
dat_indx = which(rss$simulate.num_effect == i)
coverage[2, i] = calculate_cs_coverage(rss$rss.cs, rss$simulate.is_effect, dat_indx)
}
# 2. power of cs
power_cs = matrix(NA, ncol = 3, nrow = 2)
rownames(power_cs) = c("susie", "susie.rss")
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3){
dat_indx = which(susie$simulate.num_effect == i)
cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[1, i] = power
cs_effect = get_cs_effect(rss$rss.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) rss$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[2, i] = power
}
# 3. mean abs correlation
# 1. coverage of cs
mac = matrix(NA, nrow = 2, ncol = 3)
rownames(mac) = c("susie", "susie.rss")
colnames(mac) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3) {
dat_indx = which(susie$simulate.num_effect == i)
mac[1, i] = mean(get_mean_abs_corr(susie$susie.cs, dat_indx), na.rm = TRUE)
mac[2, i] = mean(get_mean_abs_corr(rss$rss.cs, dat_indx), na.rm = TRUE)
}
plot(coverage[1, ], pch = 20, ylim = c(min(coverage) - 0.1, 1), col = "darkred", xlab = "number of effect", ylab = "coverage")
points(coverage[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(a)", side = 1, line = 4)
plot(power_cs[1, ], pch = 20, ylim = c(min(power_cs) - 0.1, max(power_cs) + 0.1), col = "darkred", xlab = "number of effect", ylab = "power")
points(power_cs[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(b)", side = 1, line = 4)
plot(mac[1, ], pch = 20, ylim = c(0.97, 1), col = "darkred", xlab = "number of effect", ylab = "mean absolute correlation")
points(mac[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(c)", side = 1, line = 4)
#### Plot against different censoring levels
censor_lvls = unique(susie$simulate.censor_lvl)
coverage = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(coverage) = c("susie", "susie.rss")
colnames(coverage) = censor_lvls
power_cs = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(power_cs) = c("susie", "susie.rss")
colnames(power_cs) = censor_lvls
mac = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(mac) = c("susie", "susie.rss")
colnames(mac) = censor_lvls
for (i in 1:length(censor_lvls)) {
# susie
dat_indx = which(susie$simulate.censor_lvl == censor_lvls[i])
coverage[1, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[1, i] = power
mac[1, i] = mean(get_mean_abs_corr(susie$susie.cs, dat_indx), na.rm = TRUE)
#susie rss
dat_indx = which(rss$simulate.censor_lvl == censor_lvls[i])
coverage[2, i] = calculate_cs_coverage(rss$rss.cs, rss$simulate.is_effect, dat_indx)
cs_effect = get_cs_effect(rss$rss.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) rss$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[2, i] = power
mac[2, i] = mean(get_mean_abs_corr(rss$rss.cs, dat_indx), na.rm = TRUE)
}
plot(censor_lvls, coverage[1, ], pch = 20, ylim = c(min(coverage) - 0.1, 1), col = "darkred", xlab = "censor level", ylab = "coverage")
points(censor_lvls, coverage[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(d)", side = 1, line = 4)
plot(censor_lvls, power_cs[1, ], pch = 20, ylim = c(min(power_cs) - 0.1, max(power_cs) + 0.1), col = "darkred", xlab = "censor level", ylab = "power")
points(censor_lvls, power_cs[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(e)", side = 1, line = 4)
plot(censor_lvls, mac[1, ], pch = 20, ylim = c(0.97, 1), col = "darkred", xlab = "censor level", ylab = "mean absolute correlation")
points(censor_lvls, mac[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(f)", side = 1, line = 4)susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie_rss.rds")
censor_lvls = unique(susie$simulate.censor_lvl)pdf("/project2/mstephens/yunqiyang/surv-susie/survival-susie/output/cs_ukb.pdf", width = 10, height = 6)
par(mfrow = c(2,3))
# 1. coverage of cs
coverage = matrix(NA, nrow = 2, ncol = 3)
rownames(coverage) = c("susie", "susie.rss")
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3) {
dat_indx = which(susie$simulate.num_effect == i)
coverage[1, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
dat_indx = which(rss$simulate.num_effect == i)
coverage[2, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
}
# 2. power of cs
power_cs = matrix(NA, ncol = 3, nrow = 2)
rownames(power_cs) = c("susie", "susie.rss")
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3){
dat_indx = which(susie$simulate.num_effect == i)
cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[1, i] = power
cs_effect = get_cs_effect(rss$susie_rss.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) rss$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[2, i] = power
}
# 3. mean abs correlation
# 1. coverage of cs
mac = matrix(NA, nrow = 2, ncol = 3)
rownames(mac) = c("susie", "susie.rss")
colnames(mac) = c("effect:1", "effect:2", "effect:3")
for (i in 1:3) {
dat_indx = which(susie$simulate.num_effect == i)
mac[1, i] = mean(get_mean_abs_corr(susie$susie.cs, dat_indx), na.rm = TRUE)
mac[2, i] = mean(get_mean_abs_corr(rss$susie_rss.cs, dat_indx), na.rm = TRUE)
}
plot(coverage[1, ], pch = 20, ylim = c(min(coverage) - 0.1, 1), col = "darkred", xlab = "number of effect", ylab = "coverage")
points(coverage[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(a)", side = 1, line = 4)
plot(power_cs[1, ], pch = 20, ylim = c(min(power_cs) - 0.1, max(power_cs) + 0.1), col = "darkred", xlab = "number of effect", ylab = "power")
points(power_cs[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(b)", side = 1, line = 4)
plot(mac[1, ], pch = 20, ylim = c(0.97, 1), col = "darkred", xlab = "number of effect", ylab = "mean absolute correlation")
points(mac[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(c)", side = 1, line = 4)
#### Plot against different censoring levels
censor_lvls = unique(susie$simulate.censor_lvl)
coverage = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(coverage) = c("susie", "susie.rss")
colnames(coverage) = censor_lvls
power_cs = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(power_cs) = c("susie", "susie.rss")
colnames(power_cs) = censor_lvls
mac = matrix(NA, nrow = 2, ncol = length(censor_lvls))
rownames(mac) = c("susie", "susie.rss")
colnames(mac) = censor_lvls
for (i in 1:length(censor_lvls)) {
# susie
dat_indx = which(susie$simulate.censor_lvl == censor_lvls[i])
coverage[1, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[1, i] = power
mac[1, i] = mean(get_mean_abs_corr(susie$susie.cs, dat_indx), na.rm = TRUE)
#susie rss
dat_indx = which(rss$simulate.censor_lvl == censor_lvls[i])
coverage[2, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
cs_effect = get_cs_effect(rss$susie_rss.cs, dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) rss$simulate.is_effect[[x]]))
power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
power_cs[2, i] = power
mac[2, i] = mean(get_mean_abs_corr(rss$susie_rss.cs, dat_indx), na.rm = TRUE)
}
plot(censor_lvls, coverage[1, ], pch = 20, ylim = c(min(coverage) - 0.1, 1), col = "darkred", xlab = "censor level", ylab = "coverage")
points(censor_lvls, coverage[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(d)", side = 1, line = 4)
plot(censor_lvls, power_cs[1, ], pch = 20, ylim = c(min(power_cs) - 0.1, max(power_cs) + 0.1), col = "darkred", xlab = "censor level", ylab = "power")
points(censor_lvls, power_cs[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(e)", side = 1, line = 4)
plot(censor_lvls, mac[1, ], pch = 20, ylim = c(0.97, 1), col = "darkred", xlab = "censor level", ylab = "mean absolute correlation")
points(censor_lvls, mac[2, ], pch = 20, col = "blue")
legend("bottomleft", legend = c("susie", "susie.rss"), pch = c(20, 20), col = c("darkred", "blue"))
mtext("(f)", side = 1, line = 4)
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_1.0.12 highr_0.9 compiler_4.2.0 pillar_1.9.0
[5] bslib_0.3.1 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 lifecycle_1.0.4 tibble_3.2.1 pkgconfig_2.0.3
[17] rlang_1.1.3 cli_3.6.2 rstudioapi_0.13 yaml_2.3.5
[21] xfun_0.30 fastmap_1.1.0 httr_1.4.3 stringr_1.5.1
[25] knitr_1.39 fs_1.5.2 vctrs_0.6.5 sass_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] httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6