Last updated: 2018-05-12

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    rmd 700fba8 LSun 2017-05-20 real data analysis 
library(ashr)
library(edgeR)
library(limma)
library(qvalue)
library(seqgendiff)
library(sva)
library(cate)
source("../code/gdash.R")

Introduction

Using David’s package seqgendiff, we are adding artefactual signals to the real GTEx Liver RNA-seq data.

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

The true signal comes from a mixture distribution

\[ g\left(\beta\right) = \pi_0\delta_0 + \left(1 - \pi_0\right)N\left(0, \sigma^2\right) \] The simulated data matrices are then fed into edgeR, limma pipeline. In the following simulations, we always use \(5\) vs \(5\).

Case 1: \(\pi_0 = 0.9\), \(\sigma^2 = 1\).

N = 100
nsamp = 10
pi0 = 0.9
sd = 1
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))
     user    system   elapsed 
 6854.603   627.845 12855.081

Case 2: \(\pi_0 = 0.9\), \(\sigma^2 = 4\)

N = 100
nsamp = 10
pi0 = 0.9
sd = 2
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))
    user   system  elapsed 
5877.223  621.433 5256.693

Case 3: \(\pi_0 = 0.5\), \(\sigma^2 = 4\)

N = 100
nsamp = 10
pi0 = 0.5
sd = 2
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))
     user    system   elapsed 
 5269.488   574.588 15042.479

Case 4: \(\pi_0 = 0.9\), \(\sigma^2 = 9\)

N = 100
nsamp = 10
pi0 = 0.9
sd = 3
system.time(ashvgdash <- N_simulations(N, mat, nsamp, pi0, sd))
     user    system   elapsed 
 5321.625   558.205 15356.345

Session information

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     

other attached packages:
 [1] Rmosek_8.0.69       PolynomF_1.0-1      CVXR_0.95          
 [4] REBayes_1.2         Matrix_1.2-12       SQUAREM_2017.10-1  
 [7] EQL_1.0-0           ttutils_1.0-1       cate_1.0.4         
[10] sva_3.26.0          BiocParallel_1.12.0 genefilter_1.60.0  
[13] mgcv_1.8-22         nlme_3.1-131        seqgendiff_0.1.0   
[16] qvalue_2.10.0       edgeR_3.20.2        limma_3.34.4       
[19] ashr_2.2-2         

loaded via a namespace (and not attached):
 [1] Biobase_2.38.0       svd_0.4.1            bit64_0.9-7         
 [4] splines_3.4.3        foreach_1.4.4        ECOSolveR_0.4       
 [7] R.utils_2.6.0        stats4_3.4.3         blob_1.1.0          
[10] yaml_2.1.18          pillar_1.0.1         RSQLite_2.0         
[13] backports_1.1.2      lattice_0.20-35      digest_0.6.15       
[16] colorspace_1.3-2     htmltools_0.3.6      R.oo_1.21.0         
[19] plyr_1.8.4           XML_3.98-1.9         esaBcv_1.2.1        
[22] xtable_1.8-2         corpcor_1.6.9        scales_0.5.0        
[25] whisker_0.3-2        scs_1.1-1            git2r_0.21.0        
[28] tibble_1.4.1         annotate_1.56.1      gmp_0.5-13.1        
[31] IRanges_2.12.0       ggplot2_2.2.1        BiocGenerics_0.24.0 
[34] lazyeval_0.2.1       Rmpfr_0.6-1          survival_2.41-3     
[37] magrittr_1.5         memoise_1.1.0        evaluate_0.10.1     
[40] R.methodsS3_1.7.1    doParallel_1.0.11    MASS_7.3-47         
[43] truncnorm_1.0-7      tools_3.4.3          matrixStats_0.52.2  
[46] stringr_1.3.0        S4Vectors_0.16.0     munsell_0.4.3       
[49] locfit_1.5-9.1       AnnotationDbi_1.40.0 compiler_3.4.3      
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[55] RCurl_1.95-4.8       iterators_1.0.9      bitops_1.0-6        
[58] rmarkdown_1.9        gtable_0.2.0         codetools_0.2-15    
[61] DBI_0.7              R6_2.2.2             reshape2_1.4.3      
[64] ruv_0.9.6            knitr_1.20           bit_1.1-12          
[67] workflowr_1.0.1      rprojroot_1.3-2      stringi_1.1.6       
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