Loading [MathJax]/jax/output/HTML-CSS/jax.js
  • Input data
  • Results summary
  • Session information

Last updated: 2018-09-03

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(20180719)

    The command set.seed(20180719) 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: 44df4dc

    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:    .Rhistory
    Ignored:    .Rproj.user/
    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 44df4dc Xiang Zhu 2018-09-03 wflow_publish(“analysis/load2013_liver.Rmd”)
    html acd345c Xiang Zhu 2018-08-31 Build site.
    Rmd 4af3002 Xiang Zhu 2018-08-31 wflow_publish(“analysis/load2013_liver.Rmd”)
    html 5a3c37b Xiang Zhu 2018-08-31 Build site.
    Rmd 86e52f5 Xiang Zhu 2018-08-31 wflow_publish(“analysis/load2013_liver.Rmd”)
    html 1734dec Xiang Zhu 2018-08-30 Build site.
    Rmd 6b3b280 Xiang Zhu 2018-08-30 wflow_publish(“analysis/load2013_liver.Rmd”)
    Rmd dbd04eb Xiang Zhu 2018-08-30 minor update chunk option
    html 8843641 Xiang Zhu 2018-08-30 Build site.
    Rmd e5fe6e0 Xiang Zhu 2018-08-30 wflow_publish(“analysis/load2013_liver.Rmd”)

Input data

  • Processed GWAS file: /project/mstephens/test_rss/data/load2013/load2013_sumstat.mat
  • PECA2 liver-specific network: /scratch/PI/whwong/zduren/share/PECA_human/PECA2/Liver_network.txt
  • L0: 40 kb; L1: 100 kb
  • SNP-to-gene window: 10 Mb
  • SNP-to-RE window: exact zero
  • Hyperparameter grid: h=(0.05:0.05:0.75); piva=10.^(-(0.25:0.25:8))

Results summary

First look at the approximated log marginal likelihoods (elbo column below).

log10.piva sigb elbo time posp
-8.00 8.157307 1.851319e+18 137.01999 1
-8.00 7.880707 1.717878e+18 137.02130 0
-8.00 7.594040 1.513736e+18 119.69971 0
-8.00 7.296117 1.342198e+18 119.79866 0
-8.00 6.985500 2.469418e+17 119.69905 0
-7.50 3.745426 5.556589e+16 50.52394 0
-8.00 4.212415 1.167404e+16 67.85548 0
-7.75 4.178787 1.149902e+16 67.82137 0
-7.50 4.102908 9.131801e+15 67.77857 0
-8.00 3.648059 9.095235e+15 50.59115 0

Next look at the gene-level posterior statistics when the marginal likelihood is maximized.

hgnc_symbol chromosome_name start_position end_position gene_nid vb_weight vb_mean vb_var
OR11H1 22 16448824 16449805 17914 1 14725050758 66.5223548
POTEH 22 16256441 16287937 17913 1 5885504253 66.5409021
ARL17B 17 44352150 44439130 15107 1 3778441639 0.4774076
LRRC37A 17 44370099 44415160 15108 1 2912077495 0.8078529
STH 17 44076616 44077060 15105 1 2348272452 0.0911898
CBWD6 9 69204538 69269662 8588 1 953083761 66.5416603
FAM27A 9 45727107 45728274 8579 1 307269557 66.5416603
FOXD4L5 9 70175707 70178815 8590 1 237973730 66.5412604
FAM27E2 9 45733559 45734896 8580 1 168180809 66.5416603
FOXD4L6 9 69199480 69202204 8587 1 157571587 66.5416603

Session information

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] bindrcpp_0.2.2 DT_0.4         knitr_1.20     dplyr_0.7.5   
[5] R.matlab_3.6.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      bindr_0.1.1       whisker_0.3-2    
 [4] magrittr_1.5      workflowr_1.1.1   tidyselect_0.2.4 
 [7] R6_2.2.2          rlang_0.2.1       highr_0.7        
[10] stringr_1.3.1     tools_3.5.1       R.oo_1.22.0      
[13] git2r_0.21.0      htmltools_0.3.6   yaml_2.1.19      
[16] rprojroot_1.3-2   digest_0.6.15     assertthat_0.2.0 
[19] tibble_1.4.2      purrr_0.2.5       htmlwidgets_1.2  
[22] R.utils_2.6.0     glue_1.2.0        evaluate_0.10.1  
[25] rmarkdown_1.10    stringi_1.2.3     pillar_1.2.3     
[28] compiler_3.5.1    backports_1.1.2   R.methodsS3_1.7.1
[31] pkgconfig_2.0.1

This reproducible R Markdown analysis was created with workflowr 1.1.1