Last updated: 2018-05-12

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Unstaged changes:
    Modified:   analysis/smemo.rmd
    Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
Expand here to see past versions:
    File Version Author Date Message
    rmd 0720bc6 LSun 2018-05-12 Update to 1.0
    html 0720bc6 LSun 2018-05-12 Update to 1.0
    rmd cc0ab83 Lei Sun 2018-05-11 update
    html 0f36d99 LSun 2017-12-21 Build site.
    html 853a484 LSun 2017-11-07 Build site.
    html 1ea081a LSun 2017-07-03 sites
    html 86fd092 LSun 2017-06-18 mouse hearts
    rmd 7e779ed LSun 2017-06-18 smemo
    rmd 8ecbed7 LSun 2017-06-18 mouse hearts
    rmd f2fdaf0 LSun 2017-06-18 smemo
    html f2fdaf0 LSun 2017-06-18 smemo

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 : 0.011943001812549 ; 2 : 1.61071078428794 ; 3 : 0.366170906280825 ; 4 : 1.70110410088397 ; 5 : 0.676196157714041 ; 6 : 0.938754567207026 ; 7 : 0.550191966320357 ; 8 : 0.238942600377754 ; 9 : 0.161306266268357 ; 10 : 0.0430996146901972 ;

Expand here to see past versions of fitting gaussian derivatives-1.png:
Version Author Date
0f36d99 LSun 2017-12-21
f2fdaf0 LSun 2017-06-18

Expand here to see past versions of fitting gaussian derivatives-2.png:
Version Author Date
0f36d99 LSun 2017-12-21
f2fdaf0 LSun 2017-06-18

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 ;

Expand here to see past versions of GD-ASH discoveries histogram-1.png:
Version Author Date
0f36d99 LSun 2017-12-21
f2fdaf0 LSun 2017-06-18

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] ashr_2.2-2        Rmosek_8.0.69     PolynomF_1.0-1    CVXR_0.95        
[5] REBayes_1.2       Matrix_1.2-12     SQUAREM_2017.10-1 EQL_1.0-0        
[9] ttutils_1.0-1    

loaded via a namespace (and not attached):
 [1] gmp_0.5-13.1      Rcpp_0.12.16      compiler_3.4.3   
 [4] git2r_0.21.0      workflowr_1.0.1   R.methodsS3_1.7.1
 [7] R.utils_2.6.0     iterators_1.0.9   tools_3.4.3      
[10] digest_0.6.15     bit_1.1-12        evaluate_0.10.1  
[13] lattice_0.20-35   foreach_1.4.4     parallel_3.4.3   
[16] yaml_2.1.18       Rmpfr_0.6-1       ECOSolveR_0.4    
[19] stringr_1.3.0     knitr_1.20        locfit_1.5-9.1   
[22] rprojroot_1.3-2   bit64_0.9-7       grid_3.4.3       
[25] R6_2.2.2          rmarkdown_1.9     limma_3.34.4     
[28] edgeR_3.20.2      magrittr_1.5      whisker_0.3-2    
[31] MASS_7.3-47       codetools_0.2-15  backports_1.1.2  
[34] htmltools_0.3.6   scs_1.1-1         assertthat_0.2.0 
[37] stringi_1.1.6     pscl_1.5.2        doParallel_1.0.11
[40] truncnorm_1.0-7   R.oo_1.21.0

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