Last updated: 2026-04-14

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Knit directory: dge-analysis/

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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 (dge-analysis/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.

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This site presents a differential gene expression analysis from CGDS on bulk RNA-seq from 23 individuals diagnosed with ME/CFS. DGE means testing which genes change in mean expression between defined groups while adjusting for batch and other model terms in the statistical design.

Counts come from the nf-core/rnaseq pipeline with Salmon quantification; we interpret results in the context of immune and metabolic readouts described in the linked pages.

workflowr ties each HTML page to a specific Git commit so the rendered report is the same code and data the authors used.

Study Design and Differential Expression Results

  1. Background: Study Overview and Methods
  2. Subsetted Differential Gene Expression (DGE) Analysis
  3. Deconvolution Analysis
  4. Subgroup vs Unaffected DGE Analysis NOTE: this is in the Supplemental Figures.

Results and Data Availability

Download publication-analysis.RData

Reproducibility

To rerun this analysis, follow the README here.

Citation

If you use this analysis or the associated data, please cite:

Birch, C.L., Wilk, B.M., Gajapathy, M. et al. Uncovering the genetic architecture of ME/CFS: a precision approach reveals impact of rare monogenic variation. J Transl Med 24, 168 (2026). https://doi.org/10.1186/s12967-025-07586-w

License

This project is licensed under the GNU General Public License v3.0.