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Description:

Compare results for susie and susierss on real genotype data from UKBiobank with \(n=50000\) and \(p=1000\). Run SuSIE for 10 iterations, and SuSIE-RSS for similar amount of time (100 iterations).


calculate_tpr_vs_fdr <- function(pip, is_effect, ts){
  res <- matrix(NA, nrow = length(ts), ncol = 2)
  colnames(res) = c("tpr", "fdr")
  for (i in 1:length(ts)){
    pred_pos = pip >= ts[i]
    tp = pip >= ts[i] & is_effect == 1
    fp = pip >= ts[i] & is_effect == 0
    tpr = sum(tp)/sum(is_effect)
    fdr = sum(fp)/sum(pred_pos)
    res[i, ] = c(tpr, fdr)
  }
  return(res)
}

# coverage: the proportion of CSs that contain an effect variable
# @param dat_indx: the indx for the data from dsc
# @param res.cs: credible sets from dsc
calculate_cs_coverage = function(res.cs, res.is_effect, dat_indx){
  contain_status = c()
  for (indx in dat_indx){
     cs = res.cs[[indx]]$cs
     true_effect = which(res.is_effect[[indx]] >= 1)
     if (!is.null(cs)){
       for (j in 1:length(cs)){
         res = ifelse(sum(true_effect %in% unlist(cs[j])) ==  1, 1, 0)
         contain_status = c(contain_status, res)
        }
     }
  }
  coverage = sum(contain_status)/length(contain_status)
  return(coverage)
}

# @param res.cs: credible sets from dsc
# @param dat_indx: the indx for the data from dsc
# @p: number of variables in each simulation replicate. 
get_cs_effect = function(res.cs, dat_indx, p){
  cs_effect = c()
  for (indx in dat_indx){
    effect = rep(0, p)
    cs_effect_indx = c(unlist(res.cs[[indx]]$cs))
    effect[cs_effect_indx] = 1
    cs_effect = c(cs_effect, effect)
  }
  return(cs_effect)
}
susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie_rss.rds")

1. Results using real correlation structure from data

t95 = 951
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$susie.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$susie_rss.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 0.99, by = 0.001)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, 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.rss[,2], res.rss[,1], type = "l", col = 2)
  points(res.susie[t95,2], res.susie[t95, 1])
  points(res.rss[t95,2], res.rss[t95, 1])
  
  legend("topleft", legend = c("susie", "susie.rss.varY=1"), col = c(1,2), lty = 1)
}

Version Author Date
e837b1b yunqi yang 2024-01-30
e6f9c76 yunqi yang 2024-01-29

The dots indicate PIP threshold = 0.95

2. Results using independent X, without 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] & susie$simulate.num_effect != 0)
  pip.susie = unlist(lapply(indx, function(x) susie$susie.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 = 0.99, by = 0.001)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, 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("Independent X, effect 1-3", ",censor=", censor_lvl[i]))
  lines(res.rss[,2], res.rss[,1], type = "l", col = 2)
  
  points(res.susie[t95,2], res.susie[t95, 1])
  points(res.rss[t95,2], res.rss[t95, 1])
  legend("bottomright", legend = c("susie", "susie.rss.varY=1", "susie.rss.varY"), col = c(1,2,3), lty = 1)
}

Version Author Date
736da09 yunqi yang 2024-01-30
25abdc3 yunqi yang 2024-01-30
e837b1b yunqi yang 2024-01-30
e6f9c76 yunqi yang 2024-01-29

The dots indicate PIP threshold = 0.95.

3. 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$susie.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$susie_rss.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 0.99, by = 0.001)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, 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.rss[,2], res.rss[,1], type = "l", col = 2)

  points(res.susie[t95,2], res.susie[t95, 1])
  points(res.rss[t95,2], res.rss[t95, 1])
  
  legend("bottomright", legend = c("susie", "susie.rss.varY=1", "susie.rss.varY"), col = c(1,2,3), lty = 1)
}

Version Author Date
25abdc3 yunqi yang 2024-01-30
e837b1b yunqi yang 2024-01-30

The dots indicate PIP threshold = 0.95.

5. Assess Susie CS

coverage = matrix(NA, ncol = 3, nrow = length(censor_lvl))
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:3){
  for (j in 1:6){
    dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
  }
}

coverage
#              effect:1  effect:2  effect:3
# censor:0    1.0000000 0.8372093 0.9365079
# censor:0.2  1.0000000 0.9090909 0.8591549
# censor:0.4  1.0000000 0.9761905 0.8923077
# censor:0.6  0.9473684 0.9375000 0.8939394
# censor:0.8  1.0000000 0.9142857 0.8428571
# censor:0.99 1.0000000 1.0000000 0.9230769
power_cs = matrix(NA, ncol = 3, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:3){
  for (j in 1:6){
    dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
    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[j, i] = power
  }
}

power_cs
#      effect:1 effect:2  effect:3
# [1,]     0.85    0.900 0.9000000
# [2,]     1.00    0.900 0.9000000
# [3,]     1.00    0.950 0.9000000
# [4,]     0.90    0.775 0.9333333
# [5,]     1.00    0.775 0.8666667
# [6,]     0.70    0.775 0.6000000
coverage = matrix(NA, ncol = 2, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
cor_type = c("real", "independent")
colnames(coverage) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:2){
  for (j in 1:6){
    dat_indx = which(susie$simulate.num_effect != 0 & susie$simulate.cor_type == cor_type[i] & susie$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
  }
}

coverage
#             real correlation independent
# censor:0           0.8281250           1
# censor:0.2         0.8181818           1
# censor:0.4         0.8840580           1
# censor:0.6         0.8412698           1
# censor:0.8         0.8000000           1
# censor:0.99        0.9268293           1
power_cs = matrix(NA, ncol = 2, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(power_cs) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:2){
  for (j in 1:6){
    dat_indx = which(susie$simulate.num_effect != 0 & susie$simulate.cor_type == cor_type[i] & susie$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(susie$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[j, i] = power
  }
}

power_cs
#      real correlation independent
# [1,]        0.8000000   0.9833333
# [2,]        0.8666667   0.9666667
# [3,]        0.9000000   0.9666667
# [4,]        0.8500000   0.9000000
# [5,]        0.8000000   0.9166667
# [6,]        0.6333333   0.7166667

6. Assess Susie.rss cs

coverage = matrix(NA, ncol = 3, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:3){
  for (j in 1:6){
    dat_indx = which(rss$simulate.num_effect == i & rss$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
  }
}

coverage
#             effect:1 effect:2 effect:3
# censor:0         NaN      NaN      NaN
# censor:0.2       NaN      NaN      NaN
# censor:0.4       NaN      NaN      NaN
# censor:0.6       NaN      NaN      NaN
# censor:0.8       NaN      NaN      NaN
# censor:0.99      NaN      NaN      NaN
power_cs = matrix(NA, ncol = 3, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:3){
  for (j in 1:6){
    dat_indx = which(rss$simulate.num_effect == i & rss$simulate.censor_lvl == censoring[j])
    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[j, i] = power
  }
}

power_cs
#      effect:1 effect:2 effect:3
# [1,]        0        0        0
# [2,]        0        0        0
# [3,]        0        0        0
# [4,]        0        0        0
# [5,]        0        0        0
# [6,]        0        0        0
coverage = matrix(NA, ncol = 2, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
cor_type = c("real", "independent")
colnames(coverage) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:2){
  for (j in 1:6){
    dat_indx = which(rss$simulate.num_effect != 0 & rss$simulate.cor_type == cor_type[i] & rss$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
  }
}
coverage
#             real correlation independent
# censor:0                 NaN         NaN
# censor:0.2               NaN         NaN
# censor:0.4               NaN         NaN
# censor:0.6               NaN         NaN
# censor:0.8               NaN         NaN
# censor:0.99              NaN         NaN
power_cs = matrix(NA, ncol = 2, nrow = 6)
censoring = c(0, 0.2, 0.4, 0.6, 0.8, 0.99)
colnames(power_cs) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8", "censor:0.99")
for (i in 1:2){
  for (j in 1:6){
    dat_indx = which(rss$simulate.num_effect != 0 & rss$simulate.cor_type == cor_type[i] & rss$simulate.censor_lvl == censoring[j])
    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[j, i] = power
  }
}

power_cs
#      real correlation independent
# [1,]                0           0
# [2,]                0           0
# [3,]                0           0
# [4,]                0           0
# [5,]                0           0
# [6,]                0           0

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