Initial method evaluation

Introduction/summary
Extract dsc results
Analyze

Last updated: 2019-04-22

Checks: 6 0

Knit directory: dsc-log-fold-change/

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Untracked files:
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Unstaged changes:
    Modified:   analysis/index.Rmd
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    Modified:   dsc/benchmark.sh

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File	Version	Author	Date	Message
html	94f122f	Joyce Hsiao	2019-04-22	Build site.
Rmd	a9cd8b5	Joyce Hsiao	2019-04-22	power, sample size and effect size
Rmd	1cd1ce9	John Blischak	2019-02-21	Try using `conda install` instead of `conda env create`.
html	1cd1ce9	John Blischak	2019-02-21	Try using `conda install` instead of `conda env create`.

Introduction/summary

Determine relationship between effect size, sample size and power. Choose parameters of the true effect distribution accordingly.
Experimental data: PBMC of 2,683 samples and ~ 11,000 genes, including 7+ cell types. This data has large number of zeros (93% zeros in the count matrix).
Simulation parameters
- number of genes: 1,000 randomly sampled from experimental data
- number of samples per group: (50, 50), (100, 100), (250, 250); draw n1+n2 from experimental data, then randomly assigned to group 1 or group 2
- fraction of true effects: .1
- distribution of true effects: normal distribution with mean 0 and sd .5, 1, 2, 4

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_power_choose_betasd"

dsc_res_edger <- dscquery(dir_dsc, 
                    targets=c("data_poisthin_choose_betasd",
                              "data_poisthin_choose_betasd.seed", 
                              "data_poisthin_choose_betasd.n1",
                              "data_poisthin_choose_betasd.betasd",
                              "edger", "pval_rank"), 
                    ignore.missing.file = T)

dsc_res_limma_voom <- dscquery(dir_dsc, 
                    targets=c("data_poisthin_choose_betasd",
                              "data_poisthin_choose_betasd.seed", 
                              "data_poisthin_choose_betasd.n1",
                              "data_poisthin_choose_betasd.betasd",
                              "limma_voom", "pval_rank"), 
                    ignore.missing.file = T)


dsc_res_edger$output.file <- dsc_res_edger$edger.output.file
dsc_res_limma_voom$output.file <- dsc_res_limma_voom$limma_voom.output.file

dsc_res_edger <- subset(dsc_res_edger, select = -c(edger.output.file))
dsc_res_limma_voom <- subset(dsc_res_limma_voom, select = -c(limma_voom.output.file))
  
dsc_res <- rbind(data.frame(dsc_res_edger, method = rep("edger", nrow(dsc_res_edger))),
                 data.frame(dsc_res_limma_voom, method = rep("limma_voom", nrow(dsc_res_limma_voom))) )


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

res <- vector("list",n_methods)
for (i in 1:nrow(dsc_res)) {
#  if (verbose) {print(i)}
  fl_pval <- readRDS(file.path(dir_dsc,
                       paste0(as.character(dsc_res$output.file[i]), ".rds")))
  fl_beta <- readRDS(file.path(dir_dsc,
                   paste0(as.character(dsc_res$data_poisthin_choose_betasd.output.file[i]), ".rds")))
  seed <- dsc_res$data_poisthin_choose_betasd.seed[i]
  n1 <- dsc_res$data_poisthin_choose_betasd.n1[i]
  betasd <- dsc_res$data_poisthin_choose_betasd.betasd[i]
  fl_qval <- readRDS(file.path(dir_dsc,
                      paste0(as.character(dsc_res$pval_rank.output.file[i]), ".rds")))
  res[[i]] <- data.frame(method = as.character(dsc_res$method)[i],
                         seed = seed,
                         betasd=betasd,
                         n1=n1,
                         truth_vec = fl_beta$beta != 0,
                         pval = fl_pval$pval,
                         qval = fl_qval$qval,
                         stringsAsFactors = F)
}
res_merge <- do.call(rbind, res)

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

Analyze

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

fdr_thres <- .1

p1 <- res_merge %>%
  filter(method == "limma_voom") %>%
  group_by(seed, betasd, n1) %>%
  summarise(power = sum(truth_vec==TRUE & qval<fdr_thres)/sum(truth_vec==TRUE)) %>%
  ggplot(., aes(x=factor(n1), y=power, col=factor(betasd))) + ylim(0,1) +
    geom_boxplot() +
    ylab("Power") + xlab("n1") +
    ggtitle("limma_voom")

p2 <- res_merge %>%
  filter(method == "edger") %>%
  group_by(seed, betasd, n1) %>%
  summarise(power = sum(truth_vec==TRUE & qval<fdr_thres)/sum(truth_vec==TRUE)) %>%
  ggplot(., aes(x=factor(n1), y=power, col=factor(betasd))) + ylim(0,1) +
    geom_boxplot() +
    ylab("Power") + xlab("n1") +
    ggtitle("edger")

library(cowplot)
plot_grid(p1,p2)

Version	Author	Date
94f122f	Joyce Hsiao	2019-04-22

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] cowplot_0.9.4   forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1 dscrutils_0.3.8

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

Initial method evaluation

Joyce Hsiao

2019-04-22

Introduction/summary

Extract dsc results

Analyze