Last updated: 2019-05-01

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File Version Author Date Message
Rmd e99e849 Joyce Hsiao 2019-05-01 edit description of simulation parameters

Introduction

Extract dsc results

knitr::opts_chunk$set(warning=F, message=F)

library(dscrutils)
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ─────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

extract dsc output and get p-values, q-values, true signals, etc.

dir_dsc <- "/scratch/midway2/joycehsiao/dsc-log-fold-change/pipe_gtex"

dsc_res <- dscquery(dir_dsc, 
                    targets=c("data_poisthin_gtex",
                              "data_poisthin_gtex.seed", 
                              "data_poisthin_gtex.n1",
                              "data_poisthin_gtex.prop_null",
                              "method", "pval_rank"), 
                    ignore.missing.file = T)

method_vec <- as.factor(dsc_res$method)
n_methods <- nlevels(method_vec)

dsc_res <- dsc_res[dsc_res$method != "sva_limma_voom" & dsc_res$data_poisthin_gtex.prop_null != 1,]

res <- list()
for (i in 1:nrow(dsc_res)) {
  print(i)
  fl_pval <- readRDS(file.path(dir_dsc,
                       paste0(as.character(dsc_res$method.output.file[i]), ".rds")))
  fl_beta <- readRDS(file.path(dir_dsc,
                    paste0(as.character(dsc_res$data_poisthin_gtex.output.file[i]), ".rds")))
  prop_null <- dsc_res$data_poisthin_gtex.prop_null[i]
  seed <- dsc_res$data_poisthin_gtex.seed[i]
  n1 <- dsc_res$data_poisthin_gtex.n1[i]
  fl_qval <- readRDS(file.path(dir_dsc,
                     paste0(as.character(dsc_res$pval_rank.output.file[i]), ".rds")))
  res[[i]] <- data.frame(method = as.character(dsc_res$method)[i],
                         seed = seed,
                         n1=n1,
                         prop_null=prop_null,
                         pval = fl_pval$pval,
                         truth_vec = fl_beta$beta != 0,
                         qval = fl_qval$qval,
                         stringsAsFactors = F)
  roc_output <- pROC::roc(truth_vec ~ pval, data=res[[i]])
  res[[i]]$auc <- roc_output$auc
}
res_merge <- do.call(rbind, res)

saveRDS(res_merge, file = "output/gtex_power.Rmd/res_merge.rds")

False discovery rate control

res_merge <- readRDS(file = "output/gtex_power.Rmd/res_merge.rds")

plot_power <- function(res) {
  fdr_thres <- .1
  n_methods <- length(unique(res$method))
  cols <- RColorBrewer::brewer.pal(n_methods,name="Dark2")
  # library(cowplot)
  # title <- ggdraw() + draw_label(title, fontface='bold')
  res_plot <- res %>% group_by(n1, method, seed) %>%
    summarise(power = sum(qval < fdr_thres & truth_vec==TRUE, 
                          na.rm=T)/sum(truth_vec==TRUE)) 
  res_plot$n1 <- factor(res_plot$n1)
  res_plot$method <- factor(res_plot$method)
  
  ggplot(res_plot, aes(x=n1, y=power, col=method)) +
        #geom_point(aes(col=method),cex=.7) + geom_boxplot(aes(col=method)) + 
        geom_point(cex=.7) + geom_boxplot() + 
        facet_wrap(~method) +
        xlab("Sample size per group") + ylab("Power") +
        scale_color_manual(values=cols) +
        ggtitle(paste("Power at q-value < ", fdr_thres, "(total 1K)")) +
        stat_summary(fun.y=mean, geom="point", shape=4, size=2, col="black") +
        stat_summary(fun.y=median, geom="point", shape=18, size=2, col="black")
}

plot_fdr <- function(res) {
  fdr_thres <- .1
  n_methods <- length(unique(res$method))
  cols <- RColorBrewer::brewer.pal(n_methods,name="Dark2")
  # library(cowplot)
  # title <- ggdraw() + draw_label(title, fontface='bold')
  res_plot <- res %>% group_by(n1, method, seed) %>%
        summarise(false_pos_rate = sum(qval < fdr_thres & truth_vec==F, 
                                       na.rm=T)/sum(qval < fdr_thres,na.rm=T)) 
  res_plot$n1 <- factor(res_plot$n1)
  res_plot$method <- factor(res_plot$method)

  ggplot(res_plot, aes(x=n1, y=false_pos_rate, col=method)) +
      geom_point(cex=.7) + geom_boxplot() + 
      facet_wrap(~method) +
      xlab("Sample size per group") + ylab("Power") +
      scale_color_manual(values=cols) +
      ggtitle(paste("FDR at q-value < ", fdr_thres, "(total 1K)")) +
      stat_summary(fun.y=mean, geom="point", shape=4, size=2, col="black") +
      stat_summary(fun.y=median, geom="point", shape=18, size=2, col="black")
}




levels(factor(res_merge$method))
[1] "deseq2"               "edger"                "limma_voom"          
[4] "sva_ttest"            "t_test"               "t_test_log2cpm_quant"
[7] "wilcoxon"            
labels <- c("deseq2", "edger", "limma_v", "sva_ttest", "t_test", "t_log2cpm_q", "wilcoxon")

plot_power(subset(res_merge, prop_null==.9))

plot_fdr(subset(res_merge, prop_null==.9)) + ylim(0,1)

AUC

res_merge <- readRDS(file = "output/gtex_power.Rmd/res_merge.rds")

library(dplyr)
res_merge_auc <- res_merge %>% group_by(method, seed, n1) %>% slice(1)

plot_auc <- function(res) {
  n_methods <- length(unique(res$method))
  cols <- RColorBrewer::brewer.pal(n_methods,name="Dark2")
  res %>% group_by(n1,method) %>%
      ggplot(., aes(x=factor(n1), y=auc, col=method)) +
          geom_boxplot() + geom_point(cex=.7) +
          facet_wrap(~method) +
          xlab("Sample size per group") + ylab("Area under the ROC curve") +
          scale_color_manual(values=cols) +
      ggtitle("AUC") + 
      stat_summary(fun.y=mean, geom="point", shape=4, size=2, col="black") +
      stat_summary(fun.y=median, geom="point", shape=18, size=2, col="black")
}

levels(factor(res_merge_auc$method))
[1] "deseq2"               "edger"                "limma_voom"          
[4] "sva_ttest"            "t_test"               "t_test_log2cpm_quant"
[7] "wilcoxon"            
labels <- c("deseq2", "edger", "limma_v", "t_test", "t_log2cpm_q", "wilcoxon")
plot_auc(res_merge_auc)

ROC

# n1=150
# res=subset(res_merge, n1==15)
get_roc_est <- function(res, fpr_nbin=500) {
    method_list <- levels(factor(res$method))
    seed_list <- unique(res$seed)

    out_roc_est <- lapply(1:length(method_list), function(i) {
      df_sub <- res %>% filter(method==method_list[i])
      roc_est_seed <- lapply(1:length(seed_list), function(j) {
        print(j)
        roc_set_seed_one <- with(df_sub[df_sub$seed==seed_list[j],],
                                 pROC::auc(response=truth_vec, predictor=pval))
        fpr <- 1-attr(roc_set_seed_one, "roc")$specificities
        tpr <- attr(roc_set_seed_one, "roc")$sensitivities
        data.frame(fpr=fpr,tpr=tpr,seed=seed_list[j])
      })
      roc_est_seed <- do.call(rbind, roc_est_seed)
      fpr_range <- range(roc_est_seed$fpr)
      fpr_seq <- seq.int(from=fpr_range[1], to = fpr_range[2], length.out = fpr_nbin+1)
      tpr_est_mean <- rep(NA, fpr_nbin)
      for (index in 1:fpr_nbin) {
        tpr_est_mean[index] <- mean( roc_est_seed$tpr[which(roc_est_seed$fpr >= fpr_seq[index] & roc_est_seed$fpr < fpr_seq[index+1])], na.rm=T) 
      }
      fpr_bin_mean <- fpr_seq[-length(fpr_seq)]+(diff(fpr_seq)/2)
      roc_bin_est <- data.frame(fpr_bin_mean=fpr_bin_mean,tpr_est_mean=tpr_est_mean)
      roc_bin_est <- roc_bin_est[!is.na(roc_bin_est$tpr_est_mean),]
      roc_bin_est$method <- method_list[i]
      return(roc_bin_est)
    }) 
    out <- do.call(rbind, out_roc_est)
    out$method <- factor(out$method)
    return(out)
}


n1_seq <- c(5,10,50,150)
roc_est <- lapply(1:length(n1_seq), function(i) {
  roc_est <- get_roc_est(subset(res_merge, n1==n1_seq[i]), fpr_nbin=200)
  roc_est$method <- factor(roc_est$method)
  roc_est$n1 <- n1_seq[i]
  return(roc_est)
})
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roc_est_all <- do.call(rbind, roc_est)
roc_est_all$n1 <- factor(roc_est_all$n1)

saveRDS(roc_est_all, file = "output/gtex_power.Rmd/roc_est_all.rds")


n_methods <- length(unique(res_merge$method))
cols <- RColorBrewer::brewer.pal(n_methods,name="Dark2")
ggplot(subset(roc_est_all, fpr_bin_mean < .15), 
       aes(x=fpr_bin_mean, y=tpr_est_mean, col=method)) +
  geom_step() + 
  scale_color_manual(values=cols) +
  facet_wrap(~n1) + xlab("False discovery rate") + ylab("Sensitivity") +
  ggtitle("Sensitivity and false discovery rate (ROC curve)")


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.0  
 [9] tidyverse_1.2.1 dscrutils_0.3.8

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         compiler_3.5.1     pillar_1.3.1      
 [7] git2r_0.23.0       workflowr_1.3.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.3.4       
[19] cli_1.0.1          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         pROC_1.13.0       
[28] hms_0.4.2          generics_0.0.2     fs_1.2.6          
[31] rprojroot_1.3-2    grid_3.5.1         tidyselect_0.2.5  
[34] glue_1.3.0         R6_2.4.0           readxl_1.1.0      
[37] rmarkdown_1.10     modelr_0.1.2       magrittr_1.5      
[40] whisker_0.3-2      backports_1.1.2    scales_1.0.0      
[43] htmltools_0.3.6    rvest_0.3.2        assertthat_0.2.0  
[46] colorspace_1.3-2   labeling_0.3       stringi_1.2.4     
[49] lazyeval_0.2.1     munsell_0.5.0      broom_0.5.1       
[52] crayon_1.3.4