Last updated: 2020-06-01

Checks: 2 0

Knit directory: methyl-geneset-testing/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 4e77103. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    code/.job/
    Ignored:    code/old/
    Ignored:    data/
    Ignored:    output/.DS_Store
    Ignored:    output/450K.rds
    Ignored:    output/CD4vCD8.GO.csv
    Ignored:    output/CD4vCD8.KEGG.csv
    Ignored:    output/EPIC.rds
    Ignored:    output/FDR-analysis/
    Ignored:    output/compare-methods/
    Ignored:    output/random-cpg-sims/

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.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 4e77103 JovMaksimovic 2020-06-01 wflow_publish(c(“analysis/index.Rmd”, “analysis/gomethByFeature.Rmd”))
html 9224966 Jovana Maksimovic 2020-05-29 Build site.
Rmd 49cf167 Jovana Maksimovic 2020-05-29 wflow_publish(c(“analysis/methylGSAParamSweep.Rmd”, “analysis/index.Rmd”))
html c381d87 Jovana Maksimovic 2020-05-19 Build site.
Rmd ff86a78 Jovana Maksimovic 2020-05-19 wflow_publish(“analysis/index.Rmd”)
html 2c18577 Jovana Maksimovic 2020-05-19 Build site.
Rmd b7daadd Jovana Maksimovic 2020-05-19 wflow_publish(“analysis/index.Rmd”)
html f2da7f9 Jovana Maksimovic 2020-05-15 Build site.
Rmd 68a0f24 Jovana Maksimovic 2020-05-15 wflow_publish(c(“analysis/index.Rmd”, “analysis/runTimeComparison.Rmd”))
html 06648a4 JovMaksimovic 2020-04-27 Build site.
Rmd 89af323 JovMaksimovic 2020-04-27 wflow_publish(c(“analysis/index.Rmd”, “analysis/exploreData.Rmd”,
html 64432de Jovana Maksimovic 2020-04-17 Build site.
Rmd 90b90ef Jovana Maksimovic 2020-04-17 Updated home link to FDR analysis
html 244474d Jovana Maksimovic 2020-03-02 Build site.
Rmd d7cd66e Jovana Maksimovic 2020-03-02 Initial Commit
Rmd 1840409 Jovana Maksimovic 2020-03-02 Start workflowr project.

Gene set testing for methylation arrays

This site contains the development and evaluation of various methylation array gene set testing methods available in the Bioconductor missMethyl package. Follow the links below to explore the different parts of the project.

Analysis

  1. Explore EPIC array bias
    • Explore the various array biases on the EPIC array that affect gene set testing.
  2. Explore 450k array bias
    • Explore the various array biases on the 450k array that affect gene set testing.
  3. Generate a blood cell RNAseq “truth” set
    • Analyse an RNAseq sorted blood cell dataset and identify the top ranked gene sets for each cell type comparison.
  4. Compare FDR of different methods
    • Analyse the normal samples from a 450k array KIRC TCGA dataset using various genset testing methods to estimate their false discovery rate control.
  5. Compare performance of different methods
    • Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare how well the different methods perform using several metrics.
  6. Compare run-time of different methods
    • Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare the run-time of the different methods.
  7. Effect of gene set size parameters on methylGSA
    • Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare the run-time of the different methods.
  8. Evaluate GOregion
    • Evalulate GOregion, our extension of gometh for geneset testing of differentially methylated regions (DMRs) identified by DMR finding software.
  9. Restricting Sig. CpGs in GOmeth by genomic region
    • Evalulate the impact of on gene set testing using GOmeth of restricting significant CpGs based on genomic feature using the EPIC sorted blood cell dataset.

Licenses

The code in this analysis is covered by the MIT license and the written content on this website is covered by a Creative Commons CC-BY license.

Citations