Last updated: 2018-10-21

workflowr checks: (Click a bullet for more information)

  • ✔ R Markdown file: up-to-date

    Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

  • ✔ Environment: empty

    Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

  • ✔ Seed: set.seed(12345)

    The command set.seed(12345) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

  • ✔ Session information: recorded

    Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

  • ✔ Repository version: 7b6b664

    Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

    Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 
    
Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/cash_paper_fig_leukemia_cache/
    Ignored:    code/.Rapp.history
    Ignored:    data/LSI/
    Ignored:    docs/.DS_Store
    Ignored:    docs/figure/.DS_Store
    Ignored:    output/fig/
    Ignored:    output/paper/
    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
    html f655dcb LSun 2018-10-17 Build site.
    rmd 2bc9fa8 LSun 2018-10-17 wflow_publish(“cash_paper_fig_mouseheart.rmd”)

Introduction

Analysis of a mouse heart gene expression data set with 2 vs 2 samples.

source("../code/gdash_lik.R")
Loading required package: EQL
Loading required package: ttutils
Loading required package: SQUAREM
Loading required package: REBayes
Loading required package: Matrix
Loading required package: CVXR

Attaching package: 'CVXR'
The following object is masked from 'package:stats':

    power
Loading required package: PolynomF
Warning: package 'PolynomF' was built under R version 3.4.4
Loading required package: Rmosek
Loading required package: ashr

Attaching package: 'ashr'
The following object is masked from 'package:CVXR':

    get_np
counts.mat = read.table("../data/smemo.txt", header = T, row.name = 1)
counts.mat = counts.mat[, -5]
counts = counts.mat[rowSums(counts.mat) >= 5, ]
design = model.matrix(~c(0, 0, 1, 1))
dgecounts = edgeR::calcNormFactors(edgeR::DGEList(counts = counts, group = design[, 2]))
v = limma::voom(dgecounts, design, plot = FALSE)
lim = limma::lmFit(v)
r.ebayes = limma::eBayes(lim)
p = r.ebayes$p.value[, 2]
t = r.ebayes$t[, 2]
z = -sign(t) * qnorm(p/2)
fit.locfdr <- locfdr::locfdr(z)

Expand here to see past versions of analysis-1.png:
Version Author Date
f655dcb LSun 2018-10-17 
fit.qvalue <- qvalue::qvalue(p)
x = lim$coefficients[, 2]
s = x / z
fit.cash <- gdash(x, s)
fit.ash <- ashr::ash(x, s, mixcompdist = "normal", method = "fdr")
x.plot <- seq(-10, 10, length = 1000)
gd.ord <- 10
hermite = Hermite(gd.ord)
gd0.std = dnorm(x.plot)
matrix_lik_plot = cbind(gd0.std)
for (i in 1 : gd.ord) {
  gd.std = (-1)^i * hermite[[i]](x.plot) * gd0.std / sqrt(factorial(i))
  matrix_lik_plot = cbind(matrix_lik_plot, gd.std)
}
y.plot = matrix_lik_plot %*% fit.cash$w * fit.cash$fitted_g$pi[1]

method.col <- scales::hue_pal()(5)
setEPS()
postscript("../output/paper/mouseheart.eps", height = 5, width = 7)
#pdf("../output/paper/mouseheart.pdf", height = 5, width = 7)

hist(z, prob = TRUE, main = "", xlab = expression(paste(z, "-scores")), cex.lab = 1.25, xlim = c(-max(abs(z)), max(abs(z))))
lines(x.plot, y.plot, col = method.col[5], lwd = 2)
lines(x.plot, dnorm(x.plot), col = "orange", lty = 2, lwd = 2)
lines(x.plot, dnorm(x.plot, fit.locfdr$fp0[3, 1], fit.locfdr$fp0[3, 2]) * fit.locfdr$fp0[3, 3], col = method.col[3], lty = 2, lwd = 2)

text(-2.3, 0.2, "N(0,1)", col = "orange")
arrows(-1.7, 0.2, -1.2, 0.195, length = 0.1, angle = 20, col = "orange")

text(-4.2, 0.13, bquote(atop(" locfdr empirical null:", .(round(fit.locfdr$fp0[3, 3], 2)) %*% N(.(round(fit.locfdr$fp0[3, 1], 2)), .(round(fit.locfdr$fp0[3, 2], 2))^2))), col = method.col[3])
arrows(-2.5, 0.13, -2, 0.125, length = 0.1, angle = 20, col = method.col[3])

text(4.6, 0.085,
     bquote(paste("cashr: ", .(round(fit.cash$fitted_g$pi[1], 2)) %*% hat(f))),
     col = method.col[5])
arrows(3.3, 0.08, 2.8, 0.075, length = 0.1, angle = 20, col = method.col[5])

dev.off()
quartz_off_screen 
                2 
fit.BH <- p.adjust(p, method = "BH")
sum(fit.cash$qvalue <= 0.1)
[1] 0
sum(fit.BH <= 0.1)
[1] 4130
sum(fit.qvalue$qvalues <= 0.1)
[1] 6502
sum(ashr::get_qvalue(fit.ash) <= 0.1)
[1] 17191
sum(ashr::qval.from.lfdr(fit.locfdr$fdr) <= 0.1)
[1] 0
1 - sum(pnorm(-log2(1.2), fit.cash$fitted_g$mean[-1], fit.cash$fitted_g$sd[-1]) * 2 * fit.cash$fitted_g$pi[-1]) / (1 - fit.cash$fitted_g$pi[1])
[1] 0.9981602

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 10.13.6

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_2.2-3        Rmosek_8.0.69     PolynomF_1.0-2    CVXR_0.99        
[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] qvalue_2.10.0     locfit_1.5-9.1    reshape2_1.4.3   
 [4] splines_3.4.3     lattice_0.20-35   colorspace_1.3-2 
 [7] htmltools_0.3.6   yaml_2.1.18       gmp_0.5-13.2     
[10] rlang_0.1.6       R.oo_1.22.0       pillar_1.1.0     
[13] Rmpfr_0.7-1       R.utils_2.7.0     bit64_0.9-7      
[16] scs_1.1-1         foreach_1.4.4     plyr_1.8.4       
[19] stringr_1.3.0     munsell_0.4.3     gtable_0.2.0     
[22] workflowr_1.1.1   R.methodsS3_1.7.1 codetools_0.2-15 
[25] evaluate_0.10.1   knitr_1.20        doParallel_1.0.11
[28] pscl_1.5.2        parallel_3.4.3    Rcpp_0.12.18     
[31] edgeR_3.20.8      backports_1.1.2   scales_0.5.0     
[34] limma_3.34.7      locfdr_1.1-8      truncnorm_1.0-7  
[37] bit_1.1-12        ggplot2_2.2.1     digest_0.6.15    
[40] stringi_1.1.6     grid_3.4.3        rprojroot_1.3-2  
[43] ECOSolveR_0.4     tools_3.4.3       magrittr_1.5     
[46] lazyeval_0.2.1    tibble_1.4.2      whisker_0.3-2    
[49] MASS_7.3-50       assertthat_0.2.0  rmarkdown_1.9    
[52] iterators_1.0.9   R6_2.2.2          git2r_0.21.0     
[55] compiler_3.4.3

This reproducible R Markdown analysis was created with workflowr 1.1.1