Last updated: 2018-08-30

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(1)

    The command set.seed(1) 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: 2b58dca

    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/.Rhistory
    Ignored:    analysis/include/.DS_Store
    Ignored:    code/.DS_Store
    Ignored:    data/.DS_Store
    Ignored:    docs/.DS_Store
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  analysis/Classify.Rmd
    Untracked:  analysis/EstimateCorMaxEM.Rmd
    Untracked:  analysis/EstimateCorMaxEMGD.Rmd
    Untracked:  analysis/EstimateCorOptimEM.Rmd
    Untracked:  analysis/EstimateCorPrior.Rmd
    Untracked:  analysis/EstimateCorSol.Rmd
    Untracked:  analysis/HierarchicalFlashSim.Rmd
    Untracked:  analysis/MashLowSignalGTEx.Rmd
    Untracked:  analysis/MashLowSignalGTExPerm.Rmd
    Untracked:  analysis/MashLowSignalIndex.Rmd
    Untracked:  analysis/Mash_GTEx.Rmd
    Untracked:  analysis/MeanAsh.Rmd
    Untracked:  analysis/OutlierDetection.Rmd
    Untracked:  analysis/OutlierDetection2.Rmd
    Untracked:  analysis/OutlierDetection3.Rmd
    Untracked:  analysis/OutlierDetection4.Rmd
    Untracked:  analysis/Test.Rmd
    Untracked:  analysis/mash_missing_row.Rmd
    Untracked:  code/GTExNullModel.R
    Untracked:  code/MashClassify.R
    Untracked:  code/MashCorResult.R
    Untracked:  code/MashNULLCorResult.R
    Untracked:  code/MashSource.R
    Untracked:  code/Weight_plot.R
    Untracked:  code/addemV.R
    Untracked:  code/estimate_cor.R
    Untracked:  code/generateDataV.R
    Untracked:  code/johnprocess.R
    Untracked:  code/sim_mean_sig.R
    Untracked:  code/summary.R
    Untracked:  data/Blischak_et_al_2015/
    Untracked:  data/scale_data.rds
    Untracked:  docs/figure/Classify.Rmd/
    Untracked:  docs/figure/MashLowSignalGTEx.Rmd/
    Untracked:  docs/figure/MashLowSignalGTExPerm.Rmd/
    Untracked:  docs/figure/OutlierDetection.Rmd/
    Untracked:  docs/figure/OutlierDetection2.Rmd/
    Untracked:  docs/figure/OutlierDetection3.Rmd/
    Untracked:  docs/figure/Test.Rmd/
    Untracked:  docs/figure/mash_missing_whole_row_5.Rmd/
    Untracked:  docs/include/
    Untracked:  output/AddEMV/
    Untracked:  output/CovED_UKBio_strong.rds
    Untracked:  output/CovED_UKBio_strong_Z.rds
    Untracked:  output/Flash_UKBio_strong.rds
    Untracked:  output/GTExNULLres/
    Untracked:  output/GTEx_2.5_nullData.rds
    Untracked:  output/GTEx_2.5_nullModel.rds
    Untracked:  output/GTEx_2.5_nullPermData.rds
    Untracked:  output/GTEx_2.5_nullPermModel.rds
    Untracked:  output/GTEx_3.5_nullData.rds
    Untracked:  output/GTEx_3.5_nullModel.rds
    Untracked:  output/GTEx_3.5_nullPermData.rds
    Untracked:  output/GTEx_3.5_nullPermModel.rds
    Untracked:  output/GTEx_3_nullData.rds
    Untracked:  output/GTEx_3_nullModel.rds
    Untracked:  output/GTEx_3_nullPermData.rds
    Untracked:  output/GTEx_3_nullPermModel.rds
    Untracked:  output/GTEx_4.5_nullData.rds
    Untracked:  output/GTEx_4.5_nullModel.rds
    Untracked:  output/GTEx_4.5_nullPermData.rds
    Untracked:  output/GTEx_4.5_nullPermModel.rds
    Untracked:  output/GTEx_4_nullData.rds
    Untracked:  output/GTEx_4_nullModel.rds
    Untracked:  output/GTEx_4_nullPermData.rds
    Untracked:  output/GTEx_4_nullPermModel.rds
    Untracked:  output/MASH.10.em2.result.rds
    Untracked:  output/MASH.10.mle.result.rds
    Untracked:  output/MASHNULL.V.result.1.rds
    Untracked:  output/MASHNULL.V.result.10.rds
    Untracked:  output/MASHNULL.V.result.11.rds
    Untracked:  output/MASHNULL.V.result.12.rds
    Untracked:  output/MASHNULL.V.result.13.rds
    Untracked:  output/MASHNULL.V.result.14.rds
    Untracked:  output/MASHNULL.V.result.15.rds
    Untracked:  output/MASHNULL.V.result.16.rds
    Untracked:  output/MASHNULL.V.result.17.rds
    Untracked:  output/MASHNULL.V.result.18.rds
    Untracked:  output/MASHNULL.V.result.19.rds
    Untracked:  output/MASHNULL.V.result.2.rds
    Untracked:  output/MASHNULL.V.result.20.rds
    Untracked:  output/MASHNULL.V.result.3.rds
    Untracked:  output/MASHNULL.V.result.4.rds
    Untracked:  output/MASHNULL.V.result.5.rds
    Untracked:  output/MASHNULL.V.result.6.rds
    Untracked:  output/MASHNULL.V.result.7.rds
    Untracked:  output/MASHNULL.V.result.8.rds
    Untracked:  output/MASHNULL.V.result.9.rds
    Untracked:  output/MashCorSim--midway/
    Untracked:  output/Mash_EE_Cov_0_plusR1.rds
    Untracked:  output/UKBio_mash_model.rds

Unstaged changes:
    Modified:   analysis/Mash_UKBio.Rmd
    Modified:   analysis/mash_missing_samplesize.Rmd
    Modified:   output/Flash_T2_0.rds
    Modified:   output/Flash_T2_0_mclust.rds
    Modified:   output/Mash_model_0_plusR1.rds
    Modified:   output/PresiAddVarCol.rds
    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 2b58dca zouyuxin 2018-08-30 wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”)
    html a36b08e zouyuxin 2018-08-30 Build site.
    Rmd 5dfd60c zouyuxin 2018-08-30 wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”)
    html e9f5a34 zouyuxin 2018-08-30 Build site.
    Rmd 8cb0635 zouyuxin 2018-08-30 wflow_publish(“analysis/MashLowSignalGTEx3.5.Rmd”) 

library(mashr)
Loading required package: ashr
library(knitr)
library(kableExtra)
get_estimated_pi = function(m, dimension = c("cov", "grid", "all"), thres = NULL){
  dimension = match.arg(dimension)
  if (dimension == "all") {
      get_estimated_pi_no_collapse(m)
  }
  else {
      g = get_fitted_g(m)
      pihat = g$pi
      pihat_names = NULL
      pi_null = NULL
      if (g$usepointmass) {
        pihat_names = c("null", pihat_names)
        pi_null = pihat[1]
        pihat = pihat[-1]
      }
      pihat = matrix(pihat, nrow = length(g$Ulist))
      if(!is.null(thres)){
        pi_null = sum(pihat[, g$grid <= thres]) + pi_null
        pihat = pihat[, g$grid > thres]
      }
      if (dimension == "cov"){
        pihat = rowSums(pihat)
        pihat_names = c(pihat_names, names(g$Ulist))
      }
      else if (dimension == "grid") {
        pihat = colSums(pihat)
        pihat_names = c(pihat_names, 1:length(g$grid))
      }
      pihat = c(pi_null, pihat)
      names(pihat) = pihat_names
      return(pihat)
  }
}

There are two random sets in the GTEx summary data set. Using qvalues 0.05 as the threshold, the corresponding non-significant |z| value is around 3.1. Since lfsr and lfdr are both more conservative than q values, we select the samples with \(\max_{r} |Z_{jr}| < 3.5\) from each data set as the null set. We estimate data driven covariance matrices from data 1, estimate noise correlation from data 2, fit mash model on data 2 and calculate posterior on data 1.

data = readRDS('../output/GTEx_3.5_nullData.rds')
model = readRDS('../output/GTEx_3.5_nullModel.rds')

Sample size:

samplesize = matrix(c(nrow(data$m.data1.null$Bhat), nrow(data$m.data2.null$Bhat)))
row.names(samplesize) = c('data 1', 'data 2')
samplesize %>% kable() %>% kable_styling()
data 1 18189
data 2 25718 
barplot(get_estimated_pi(model), las=2, cex.names = 0.7)

Expand here to see past versions of unnamed-chunk-5-1.png:
Version Author Date
e9f5a34 zouyuxin 2018-08-30

The estimated weights \(\hat{\pi}\) on null part is not large. The weight on the other covariance structures may concentrate on the small grid (small \(\omega_{l}\)). So they are very close to null, but we cannot view it in the plot. I modified the get_estimated_pi function to have a threshold for grid. The weights for the grid less than the threshold are merged into null part.

barplot(get_estimated_pi(model, thres = 0.01), las=2, cex.names = 0.7)

Expand here to see past versions of unnamed-chunk-6-1.png:
Version Author Date
e9f5a34 zouyuxin 2018-08-30

The correlation for the ED_tPCA is

corrplot::corrplot(cov2cor(model$fitted_g$Ulist[['ED_tPCA']]))

Expand here to see past versions of unnamed-chunk-7-1.png:
Version Author Date
e9f5a34 zouyuxin 2018-08-30

There are 483 significant samples in data 1.

Permute samples in each condition to break the sharing

data = readRDS('../output/GTEx_3.5_nullPermData.rds')
model = readRDS('../output/GTEx_3.5_nullPermModel.rds')

Sample size:

samplesize = matrix(c(nrow(data$m.data1.p.null$Bhat), nrow(data$m.data2.p.null$Bhat)))
row.names(samplesize) = c('data 1', 'data 2')
samplesize %>% kable() %>% kable_styling()
data 1 18189
data 2 25718 
barplot(get_estimated_pi(model), las=2, cex.names = 0.7)

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
e9f5a34 zouyuxin 2018-08-30 

barplot(get_estimated_pi(model, thres = 0.01), las=2, cex.names = 0.7)

Expand here to see past versions of unnamed-chunk-11-1.png:
Version Author Date
e9f5a34 zouyuxin 2018-08-30

There are 44 significant samples in data 1.

There is no overfitting issue.

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
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/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] kableExtra_0.9.0 knitr_1.20       mashr_0.2-11     ashr_2.2-10     

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18      highr_0.7         pillar_1.3.0     
 [4] compiler_3.5.1    git2r_0.23.0      plyr_1.8.4       
 [7] workflowr_1.1.1   R.methodsS3_1.7.1 R.utils_2.6.0    
[10] iterators_1.0.10  tools_3.5.1       corrplot_0.84    
[13] digest_0.6.15     viridisLite_0.3.0 tibble_1.4.2     
[16] evaluate_0.11     lattice_0.20-35   pkgconfig_2.0.2  
[19] rlang_0.2.2       Matrix_1.2-14     foreach_1.4.4    
[22] rstudioapi_0.7    yaml_2.2.0        parallel_3.5.1   
[25] mvtnorm_1.0-8     xml2_1.2.0        httr_1.3.1       
[28] stringr_1.3.1     hms_0.4.2         rprojroot_1.3-2  
[31] grid_3.5.1        R6_2.2.2          rmarkdown_1.10   
[34] rmeta_3.0         readr_1.1.1       magrittr_1.5     
[37] whisker_0.3-2     scales_1.0.0      backports_1.1.2  
[40] codetools_0.2-15  htmltools_0.3.6   MASS_7.3-50      
[43] rvest_0.3.2       assertthat_0.2.0  colorspace_1.3-2 
[46] stringi_1.2.4     munsell_0.5.0     doParallel_1.0.11
[49] pscl_1.5.2        truncnorm_1.0-8   SQUAREM_2017.10-1
[52] crayon_1.3.4      R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.1.1