Last updated: 2017-07-03

Code version: ce86d60

Introduction

Re-analyze Smemo et al 2014’s mouse heart RNA-seq data after discussion with Matthew.

counts.mat = read.table("../data/smemo.txt", header = T, row.name = 1)
counts.mat = counts.mat[, -5]

Gene selection

Only use genes with total counts of \(4\) samples \(\geq 5\).

counts = counts.mat[rowSums(counts.mat) >= 5, ]
design = model.matrix(~c(0, 0, 1, 1))
Number of selected genes: 17191

Summary statistics

source("../code/count_to_summary.R")
summary <- count_to_summary(counts, design)
betahat <- summary$betahat
sebetahat <- summary$sebetahat
z <- summary$z

Fitting \(z\) with Gaussian derivatives

With stretch GD can fit \(z\) scores, but it seems there should be signals.

GD Coefficients:
0 : 1 ; 1 : 0.0112765123515512 ; 2 : 1.60751487400088 ; 3 : 0.361378958092869 ; 4 : 1.65789257614746 ; 5 : 0.670189379060472 ; 6 : 0.75997503879673 ; 7 : 0.557659272292024 ; 8 : -0.0586994517219462 ; 9 : 0.175073181131849 ; 10 : -0.132350826272713 ;

Discovered by BH and ASH

Feeding summary statistics to BH and ASH, both give thousands of discoveries.

fit.BH = p.adjust((1 - pnorm(abs(z))) * 2, method = "BH")
## Number of discoveries by BH
sum(fit.BH <= 0.05)
[1] 2541
fit.ash = ashr::ash(betahat, sebetahat, method = "fdr")
## Number of discoveries by ASH
sum(get_svalue(fit.ash) <= 0.05)
[1] 6440

Fitting ASH first or Gaussian derivatives first

Using default setting \(L = 10\), \(\lambda = 10\), \(\rho = 0.5\), compare the GD-ASH results by fitting ASH first vs fitting GD first. They indeed arrive at different local minima.

fit.gdash.ASH <- gdash(betahat, sebetahat,
                       gd.priority = FALSE)
## Regularized log-likelihood by fitting ASH first
fit.gdash.ASH$loglik
[1] -12483.86
fit.gdash.GD <- gdash(betahat, sebetahat)
## Regularized log-likelihood by fitting GD first
fit.gdash.GD$loglik
[1] -22136.92

GD-ASH with larger penalties on \(w\)

Using \(\lambda = 50\), \(\rho = 0.1\), fitting ASH first and GD first give the same result, and produce 1400+ discoveries with \(q\) values \(\leq 0.05\), all of which are discovered by BH.

L = 10
lambda = 50
rho = 0.1
fit.gdash.ASH <- gdash(betahat, sebetahat,
                       gd.ord = L, w.lambda = lambda, w.rho = rho,
                       gd.priority = FALSE)
## Regularized log-likelihood by fitting ASH first
fit.gdash.ASH$loglik
[1] -13651.59
## Number of discoveries
sum(fit.gdash.ASH$qvalue <= 0.05)
[1] 1431
fit.gdash.GD <- gdash(betahat, sebetahat,
                      gd.ord = L, w.lambda = lambda, w.rho = rho,
                      gd.priority = TRUE)
## Regularized log-likelihood by fitting GD first
fit.gdash.GD$loglik
[1] -13651.59
## Number of discoveries
sum(fit.gdash.GD$qvalue <= 0.05)
[1] 1431
GD Coefficients:
0 : 1 ; 1 : -0.0475544308510135 ; 2 : 0.707888470469342 ; 3 : 0.149489828947119 ; 4 : -8.97499076623316e-14 ; 5 : 0.109281416075664 ; 6 : -3.00530934822662e-13 ; 7 : 0.0783545592042359 ; 8 : -2.99572304462426e-13 ; 9 : 0.0911488252640105 ; 10 : -2.99578347875936e-13 ;

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] ashr_2.1-13       Rmosek_7.1.2      PolynomF_0.94     cvxr_0.0.0.9009  
[5] REBayes_0.85      Matrix_1.2-10     SQUAREM_2016.10-1 EQL_1.0-0        
[9] ttutils_1.0-1    

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.10      knitr_1.16        magrittr_1.5     
 [4] edgeR_3.14.0      MASS_7.3-47       pscl_1.4.9       
 [7] doParallel_1.0.10 lattice_0.20-35   foreach_1.4.3    
[10] stringr_1.2.0     tools_3.3.3       parallel_3.3.3   
[13] grid_3.3.3        git2r_0.18.0      iterators_1.0.8  
[16] htmltools_0.3.6   assertthat_0.2.0  yaml_2.1.14      
[19] rprojroot_1.2     digest_0.6.12     codetools_0.2-15 
[22] evaluate_0.10.1   rmarkdown_1.6     limma_3.28.21    
[25] stringi_1.1.5     backports_1.0.5   truncnorm_1.0-7

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