Real Data with Simulated Signals: Part I

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 3.3.3 (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     

other attached packages:
 [1] Rmosek_7.1.2      PolynomF_0.94     cvxr_0.0.0.9009  
 [4] REBayes_0.85      Matrix_1.2-10     SQUAREM_2016.10-1
 [7] EQL_1.0-0         ttutils_1.0-1     cate_1.0.4       
[10] sva_3.20.0        genefilter_1.54.2 mgcv_1.8-17      
[13] nlme_3.1-131      seqgendiff_0.1.0  qvalue_2.4.2     
[16] edgeR_3.14.0      limma_3.28.21     ashr_2.1-13      

loaded via a namespace (and not attached):
 [1] reshape2_1.4.2       splines_3.3.3        lattice_0.20-35     
 [4] colorspace_1.3-2     htmltools_0.3.6      stats4_3.3.3        
 [7] yaml_2.1.14          XML_3.98-1.7         survival_2.41-3     
[10] rlang_0.1.1          DBI_0.6-1            BiocGenerics_0.18.0 
[13] foreach_1.4.3        plyr_1.8.4           stringr_1.2.0       
[16] leapp_1.2            munsell_0.4.3        gtable_0.2.0        
[19] svd_0.4              codetools_0.2-15     evaluate_0.10       
[22] memoise_1.1.0        Biobase_2.32.0       knitr_1.16          
[25] IRanges_2.6.1        doParallel_1.0.10    pscl_1.4.9          
[28] parallel_3.3.3       AnnotationDbi_1.34.4 esaBcv_1.2.1        
[31] Rcpp_0.12.10         corpcor_1.6.9        xtable_1.8-2        
[34] scales_0.4.1         backports_1.0.5      S4Vectors_0.10.3    
[37] annotate_1.50.1      truncnorm_1.0-7      ggplot2_2.2.1       
[40] digest_0.6.12        stringi_1.1.5        grid_3.3.3          
[43] rprojroot_1.2        tools_3.3.3          bitops_1.0-6        
[46] magrittr_1.5         lazyeval_0.2.0       RCurl_1.95-4.8      
[49] tibble_1.3.1         RSQLite_1.1-2        MASS_7.3-47         
[52] ruv_0.9.6            rmarkdown_1.5        iterators_1.0.8     
[55] git2r_0.18.0