Last updated: 2018-09-16

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.

  • ✔ Repository version: ebd4220

    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/

Untracked files:
    Untracked:  analysis/gene_set.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 ebd4220 Xiang Zhu 2018-09-16 wflow_publish(“analysis/index.Rmd”)
    html ecf07e1 Xiang Zhu 2018-09-16 Build site.
    Rmd ec44ee0 Xiang Zhu 2018-09-16 wflow_publish(“analysis/index.Rmd”)
    html 8f27e6d Xiang Zhu 2018-07-02 Build site.
    Rmd c07c276 Xiang Zhu 2018-07-02 wflow_publish(“index.Rmd”)
    html c77f1e1 Xiang Zhu 2018-07-02 Build site.
    Rmd acbdfcb Xiang Zhu 2018-07-02 wflow_publish(“index.Rmd”)
    html c1e0aac Xiang Zhu 2018-07-02 Build site.
    Rmd 16a82f8 Xiang Zhu 2018-07-02 wflow_publish(“index.Rmd”)
    html 835a6be Xiang Zhu 2018-07-02 Build site.
    Rmd 447917e Xiang Zhu 2018-07-02 wflow_publish(“index.Rmd”)
    html 55087ae Xiang Zhu 2018-07-01 Build site.
    Rmd 07473ff Xiang Zhu 2018-07-01 wflow_publish(“index.Rmd”)
    html 2b35739 Xiang Zhu 2018-06-30 Build site.
    Rmd e364292 Xiang Zhu 2018-06-30 wflow_publish(“index.Rmd”)
    html de49400 Xiang Zhu 2018-06-29 Build site.
    Rmd f80d46e Xiang Zhu 2018-06-29 wflow_publish(“index.Rmd”)
    html b30daba Xiang Zhu 2018-06-27 Build site.
    Rmd bc9746a Xiang Zhu 2018-06-27 wflow_publish(“index.Rmd”)
    html c749ac7 Xiang Zhu 2018-06-27 Build site.
    Rmd 6006659 Xiang Zhu 2018-06-27 wflow_publish(“index.Rmd”)
    html 0852676 Xiang Zhu 2018-06-27 Build site.
    Rmd 46603f3 Xiang Zhu 2018-06-27 wflow_publish(“index.Rmd”)
    html ff6afd0 Xiang Zhu 2018-06-26 Build site.
    Rmd bf31edf Xiang Zhu 2018-06-26 wflow_publish(files = c(“analysis/index.Rmd”, “analysis/license.Rmd”))
    html 33aa5b3 Xiang Zhu 2018-06-26 Build site.
    Rmd 02b392f Xiang Zhu 2018-06-26 wflow_publish(“analysis/index.Rmd”)
    Rmd cee1a92 Xiang Zhu 2018-06-26 Start workflowr project.

Overview

This is my online notebook to document and share the full results of genome-wide enrichment analyses described in the manuscript:

Xiang Zhu and Matthew Stephens (2017). A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes. bioRxiv. https://doi.org/10.1101/160770.

The software that generated these results is freely available at stephenslab/rss. We also provide an end-to-end example illustrating how to use this software to perform genome-wide enrichment analyses of GWAS summary statistics. This software can be referenced in a journal’s “Code availability” section as [][zenodo-software].

In addition, all 4,026 pre-processed gene sets used in this study (including 3,913 biological pathways and 113 tissue-based gene sets) are freely available at xiangzhu/rss-gsea. These gene sets can be referenced in a journal’s “Data availability” section as DOI.

If you find the analysis results, the pre-processed gene sets, the statistical methods, and/or the software useful for your work, please kindly cite our manuscript listed above, Zhu and Stephens (2017).

If you have any question about the notebook and/or the manuscript, please feel free to contact me: Xiang Zhu, xiangzhu@uchicago.edu or xiangzhu@stanford.edu.

Additional resources

  • How can I perform similar analyses on a new dataset of GWAS summary statistics?

The software that generated results of this study is available at stephenslab/rss. I also write a step-by-step tutorial illustrating how to use this software to perform genome-wide enrichment and prioritization analyses on GWAS summary statistics.

  • Where can I download all 4,026 pre-processed gene sets?

All 4,026 gene sets used in this study are freely available at xiangzhu/rss-gsea, where the folder biological_pathway contains 3,913 biological pathways, and the folder tissue_set contains 113 GTEx tissue-based gene sets. More details about these gene sets can be found here.

  • Where can I find “baseline” model fitting results of all 31 traits?

You can find summary results of “baseline” model fitting at xiangzhu/rss-gsea-baseline. For me, the baseline model fitting results are merely inferential “bases” for the enrichment model fitting results shown in the “Main results” section above. However, when I was presenting the enrichment results during my Ph.D. thesis defense, Prof. John Novembre and Prof. Xin He both pointed out these baseline results might be useful for other on-going projects on the “fourth floor” (i.e. the great computational space shared with the labs of Matthew Stephens, John Novembre and Xin He). Their comments motivated me to create a separate online notebook xiangzhu/rss-gsea-baseline to share the baseline summary results.

  • Where can I find “Round 1” results of all 3,913 biological pathways?

Currently you need to contact me directly to view our “Round 1” results of all 3913 pathways. When this work was under review, one referee pointed out that our online results, especially our “Round 1” analysis results, were “needlessly complicated” and did not have “any obvious benefit”. Hence, I removed the “Round 1” analysis results from this notebook to simplify the presentation. I hope that this change can address the referee’s comment.

  • Where can I find the gene prioritization results?

Currently you need to contact me directly to view our full prioritization results. Unlike the gene set enrichment results, the gene prioritization results cannot be easily tabulated, and thus I have not displayed them in this notebook yet.

This reproducible R Markdown analysis was created with workflowr 1.1.1