Last updated: 2018-05-12

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: ac438b4

    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/BH_robustness_cache/
    Ignored:    analysis/FDR_Null_cache/
    Ignored:    analysis/FDR_null_betahat_cache/
    Ignored:    analysis/Rmosek_cache/
    Ignored:    analysis/StepDown_cache/
    Ignored:    analysis/alternative2_cache/
    Ignored:    analysis/alternative_cache/
    Ignored:    analysis/ash_gd_cache/
    Ignored:    analysis/average_cor_gtex_2_cache/
    Ignored:    analysis/average_cor_gtex_cache/
    Ignored:    analysis/brca_cache/
    Ignored:    analysis/cash_deconv_cache/
    Ignored:    analysis/cash_fdr_1_cache/
    Ignored:    analysis/cash_fdr_2_cache/
    Ignored:    analysis/cash_fdr_3_cache/
    Ignored:    analysis/cash_fdr_4_cache/
    Ignored:    analysis/cash_fdr_5_cache/
    Ignored:    analysis/cash_fdr_6_cache/
    Ignored:    analysis/cash_plots_cache/
    Ignored:    analysis/cash_sim_1_cache/
    Ignored:    analysis/cash_sim_2_cache/
    Ignored:    analysis/cash_sim_3_cache/
    Ignored:    analysis/cash_sim_4_cache/
    Ignored:    analysis/cash_sim_5_cache/
    Ignored:    analysis/cash_sim_6_cache/
    Ignored:    analysis/cash_sim_7_cache/
    Ignored:    analysis/correlated_z_2_cache/
    Ignored:    analysis/correlated_z_3_cache/
    Ignored:    analysis/correlated_z_cache/
    Ignored:    analysis/create_null_cache/
    Ignored:    analysis/cutoff_null_cache/
    Ignored:    analysis/design_matrix_2_cache/
    Ignored:    analysis/design_matrix_cache/
    Ignored:    analysis/diagnostic_ash_cache/
    Ignored:    analysis/diagnostic_correlated_z_2_cache/
    Ignored:    analysis/diagnostic_correlated_z_3_cache/
    Ignored:    analysis/diagnostic_correlated_z_cache/
    Ignored:    analysis/diagnostic_plot_2_cache/
    Ignored:    analysis/diagnostic_plot_cache/
    Ignored:    analysis/efron_leukemia_cache/
    Ignored:    analysis/fitting_normal_cache/
    Ignored:    analysis/gaussian_derivatives_2_cache/
    Ignored:    analysis/gaussian_derivatives_3_cache/
    Ignored:    analysis/gaussian_derivatives_4_cache/
    Ignored:    analysis/gaussian_derivatives_5_cache/
    Ignored:    analysis/gaussian_derivatives_cache/
    Ignored:    analysis/gd-ash_cache/
    Ignored:    analysis/gd_delta_cache/
    Ignored:    analysis/gd_lik_2_cache/
    Ignored:    analysis/gd_lik_cache/
    Ignored:    analysis/gd_w_cache/
    Ignored:    analysis/knockoff_10_cache/
    Ignored:    analysis/knockoff_2_cache/
    Ignored:    analysis/knockoff_3_cache/
    Ignored:    analysis/knockoff_4_cache/
    Ignored:    analysis/knockoff_5_cache/
    Ignored:    analysis/knockoff_6_cache/
    Ignored:    analysis/knockoff_7_cache/
    Ignored:    analysis/knockoff_8_cache/
    Ignored:    analysis/knockoff_9_cache/
    Ignored:    analysis/knockoff_cache/
    Ignored:    analysis/knockoff_var_cache/
    Ignored:    analysis/marginal_z_alternative_cache/
    Ignored:    analysis/marginal_z_cache/
    Ignored:    analysis/mosek_reg_2_cache/
    Ignored:    analysis/mosek_reg_4_cache/
    Ignored:    analysis/mosek_reg_5_cache/
    Ignored:    analysis/mosek_reg_6_cache/
    Ignored:    analysis/mosek_reg_cache/
    Ignored:    analysis/pihat0_null_cache/
    Ignored:    analysis/plot_diagnostic_cache/
    Ignored:    analysis/poster_obayes17_cache/
    Ignored:    analysis/real_data_simulation_2_cache/
    Ignored:    analysis/real_data_simulation_3_cache/
    Ignored:    analysis/real_data_simulation_4_cache/
    Ignored:    analysis/real_data_simulation_5_cache/
    Ignored:    analysis/real_data_simulation_cache/
    Ignored:    analysis/rmosek_primal_dual_2_cache/
    Ignored:    analysis/rmosek_primal_dual_cache/
    Ignored:    analysis/seqgendiff_cache/
    Ignored:    analysis/simulated_correlated_null_2_cache/
    Ignored:    analysis/simulated_correlated_null_3_cache/
    Ignored:    analysis/simulated_correlated_null_cache/
    Ignored:    analysis/simulation_real_se_2_cache/
    Ignored:    analysis/simulation_real_se_cache/
    Ignored:    analysis/smemo_2_cache/
    Ignored:    data/LSI/
    Ignored:    docs/.DS_Store
    Ignored:    docs/figure/.DS_Store
    Ignored:    output/fig/
    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 ac438b4 LSun 2018-05-12 Update to 1.0
    html ac438b4 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 59fd661 LSun 2017-02-03 Build site.
    html 36c1e4c LSun 2017-02-03 Build site.
    html d616c3d LSun 2017-02-03 occurrence
    html c21d808 LSun 2017-02-02 Build site.
    Rmd 858f0e4 LSun 2017-02-01 background
    html 858f0e4 LSun 2017-02-01 background

Last updated: 2018-05-12

Code version: ac438b4697e1e3025d37654d1430ba1c44be19ba

Introduction

This document simply simulates some null data by randomly sampling two groups of 5 samples from some RNA-seq data (GTEx liver samples). We plot \(p\) value histograms and see the effects of inflation: some distributions are inflated near 0 and others are inflated near 1. However, when we look at the qqplots (here of the z scores, but should be same for p values) we see something that is interesting, although obvious in hindsight: the most extreme p values (z scores) are never “too extreme” (although they are sometimes not extreme enough). The inflation comes from the “not quite so extreme” p values and z scores. This makes sense: when you have positively correlated variables, the most extreme values will tend to be less extreme than when you have independent samples, because you have “effectively” fewer independent samples.

It seems likely this can be exploited to help avoid false positives under positive correlation.

Load in the gtex liver data

library(limma)
library(edgeR)
library(qvalue)
library(ashr)
r = read.csv("../data/Liver.csv")
r = r[,-(1:2)] # remove outliers
#extract top g genes from G by n matrix X of expression
top_genes_index=function(g,X){return(order(rowSums(X),decreasing =TRUE)[1:g])}
lcpm = function(r){R = colSums(r); t(log2(((t(r)+0.5)/(R+1))* 10^6))}
Y=lcpm(r)
subset = top_genes_index(10000,Y)
Y = Y[subset,]
r = r[subset,]

Define voom transform (using code from Mengyin Lu)

voom_transform = function(counts, condition, W=NULL){
  dgecounts = calcNormFactors(DGEList(counts=counts,group=condition))
  #dgecounts = DGEList(counts=counts,group=condition)
  if (is.null(W)){
    design = model.matrix(~condition)
  }else{
    design = model.matrix(~condition+W)
  }
  
  v = voom(dgecounts,design,plot=FALSE)
  lim = lmFit(v)
  betahat.voom = lim$coefficients[,2]
  sebetahat.voom = lim$stdev.unscaled[,2]*lim$sigma
  df.voom = length(condition)-2-!is.null(W)
  
  return(list(v=v,lim=lim,betahat=betahat.voom, sebetahat=sebetahat.voom, df=df.voom, v=v))
}

Make 2 groups of size n, and repeat random sampling.

set.seed(101) 
n = 5 # number in each group
p = list()
z = list()
tscore =list()

for(i in 1:10){
  counts = r[,sample(1:ncol(r),2*n)]
  condition = c(rep(0,n),rep(1,n))
  r.voom = voom_transform(counts,condition)
  r.ebayes = eBayes(r.voom$lim)
  p[[i]] = r.ebayes$p.value[,2]
  tscore[[i]] = r.ebayes$t[,2]
  z[[i]] = sign(r.ebayes$t[,2]) * qnorm(p[[i]]/2)
  hist(p[[i]],main="histogram of effect tests")
  qqnorm(z[[i]])
  abline(a=0,b=1,col=1)
}

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-2.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-3.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-4.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-5.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-6.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-7.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-8.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-9.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-10.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-11.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-12.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-13.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-14.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-15.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-16.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-17.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-18.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-19.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01

Expand here to see past versions of unnamed-chunk-3-20.png:
Version Author Date
0f36d99 LSun 2017-12-21
858f0e4 LSun 2017-02-01 
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    qvalue_2.10.0 edgeR_3.20.2  limma_3.34.4 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.16      compiler_3.4.3    pillar_1.0.1     
 [4] git2r_0.21.0      plyr_1.8.4        workflowr_1.0.1  
 [7] iterators_1.0.9   R.methodsS3_1.7.1 R.utils_2.6.0    
[10] tools_3.4.3       digest_0.6.15     evaluate_0.10.1  
[13] tibble_1.4.1      gtable_0.2.0      lattice_0.20-35  
[16] rlang_0.1.6       foreach_1.4.4     Matrix_1.2-12    
[19] parallel_3.4.3    yaml_2.1.18       stringr_1.3.0    
[22] knitr_1.20        locfit_1.5-9.1    rprojroot_1.3-2  
[25] grid_3.4.3        rmarkdown_1.9     ggplot2_2.2.1    
[28] reshape2_1.4.3    magrittr_1.5      whisker_0.3-2    
[31] MASS_7.3-47       codetools_0.2-15  backports_1.1.2  
[34] scales_0.5.0      htmltools_0.3.6   splines_3.4.3    
[37] colorspace_1.3-2  stringi_1.1.6     pscl_1.5.2       
[40] lazyeval_0.2.1    munsell_0.4.3     doParallel_1.0.11
[43] truncnorm_1.0-7   SQUAREM_2017.10-1 R.oo_1.21.0

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    qvalue_2.10.0 edgeR_3.20.2  limma_3.34.4 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.16      compiler_3.4.3    pillar_1.0.1     
 [4] git2r_0.21.0      plyr_1.8.4        workflowr_1.0.1  
 [7] iterators_1.0.9   R.methodsS3_1.7.1 R.utils_2.6.0    
[10] tools_3.4.3       digest_0.6.15     evaluate_0.10.1  
[13] tibble_1.4.1      gtable_0.2.0      lattice_0.20-35  
[16] rlang_0.1.6       foreach_1.4.4     Matrix_1.2-12    
[19] parallel_3.4.3    yaml_2.1.18       stringr_1.3.0    
[22] knitr_1.20        locfit_1.5-9.1    rprojroot_1.3-2  
[25] grid_3.4.3        rmarkdown_1.9     ggplot2_2.2.1    
[28] reshape2_1.4.3    magrittr_1.5      whisker_0.3-2    
[31] MASS_7.3-47       codetools_0.2-15  backports_1.1.2  
[34] scales_0.5.0      htmltools_0.3.6   splines_3.4.3    
[37] colorspace_1.3-2  stringi_1.1.6     pscl_1.5.2       
[40] lazyeval_0.2.1    munsell_0.4.3     doParallel_1.0.11
[43] truncnorm_1.0-7   SQUAREM_2017.10-1 R.oo_1.21.0

This reproducible R Markdown analysis was created with workflowr 1.0.1