Last updated: 2018-05-12
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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")
We are using \(10K\) genes, \(5\) v \(5\) experimental design, and \(100\) independent simulation trials.
ngene = 1e4
nsamp = 10
N = 100
pi0 = 0.9
sd = 1
set.seed(777)
system.time(gdash.comp <- N_simulations(N, mat, nsamp, ngene, pi0, sd))
user system elapsed
18421.805 1175.855 3889.120
pi0 = 0.9
sd = sqrt(2)
set.seed(777)
system.time(gdash.comp <- N_simulations(N, mat, nsamp, ngene, pi0, sd))
user system elapsed
19523.364 1271.100 4140.141
pi0 = 0.9
sd = sqrt(3)
set.seed(777)
system.time(gdash.comp <- N_simulations(N, mat, nsamp, ngene, pi0, sd))
user system elapsed
17770.699 1169.506 3854.622
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
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] workflowr_1.0.1 Rcpp_0.12.16 digest_0.6.15
[4] rprojroot_1.3-2 R.methodsS3_1.7.1 backports_1.1.2
[7] git2r_0.21.0 magrittr_1.5 evaluate_0.10.1
[10] stringi_1.1.6 whisker_0.3-2 R.oo_1.21.0
[13] R.utils_2.6.0 rmarkdown_1.9 tools_3.4.3
[16] stringr_1.3.0 yaml_2.1.18 compiler_3.4.3
[19] htmltools_0.3.6 knitr_1.20
This reproducible R Markdown analysis was created with workflowr 1.0.1