Real Data with Simulated Signals: Part II

Introduction

We are comparing our method with ASH and SVA.

library(ashr)
library(edgeR)
library(limma)
library(seqgendiff)
library(sva)
library(pROC)

source("../code/gdash.R")

Artefactual effects \(\pi_0\delta_0 + \left(1 - \pi_0\right)N\left(0, \sigma^2\right)\) are added to the real GTEx data.

mat = read.csv("../data/liver.csv")

The setting: \(\pi_0 = 0.5\), \(\sigma^2 = 9\)

N = 200
nsamp = 10
ngene = 1e4
pi0 = 0.5
sd = 3
set.seed(777)
system.time(gdash.comp <- N_simulations(N, mat, nsamp, ngene, pi0, sd))

     user    system   elapsed 
12622.459  1340.169 49686.171 

Session information

sessionInfo()

R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.5

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] backports_1.0.5 magrittr_1.5    rprojroot_1.2   tools_3.3.3    
 [5] htmltools_0.3.6 yaml_2.1.14     Rcpp_0.12.10    stringi_1.1.5  
 [9] rmarkdown_1.5   knitr_1.16      git2r_0.18.0    stringr_1.2.0  
[13] digest_0.6.12   evaluate_0.10