v2.0.1 - Enable module usage using `github()` directive

• improve DEA statistics bar plot
• improve continuity by enabling end-to-end compatibility with downstream modules
• to enable module usage using github() directive
  • source utils.R via paramsinstead ofsnakemake@source`
  • comment global.yaml (now requires full snakemake installation, not minimal)
• add nodefaults to all env YAML

What's Changed

• Fix typo in heatmap.R by @burtonjake in #23
• Make clear that annotation.csv can contain multiple rows per analysis by @burtonjake in #25
• Protect aggregate.R from crashing when there are no filtered features. by @burtonjake in #24

New Contributors

• @burtonjake made their first contribution in #23

Full Changelog: v2.0.0...v2.0.1

v2.0.0 - Snakemake 8 compatible

Breaking change: Requires Snakemake >= v8.20.1

Full Changelog: v1.0.3...v2.0.0

v1.0.3 - bug fixes and performance improvements

Full Changelog: v1.0.2...v1.0.3

v1.0.2 add CITATION.cff file

Full Changelog: v1.0.1...v1.0.2

v1.0.1 add last author and Zenodo DOI generation

Full Changelog: v1.0.0...v1.0.1

v1.0.0 - stable version with new features and complete documentation

enhancements

• volcano plots are generated with raw p-values (rawp) and adjusted p-values (adjp) on the y-axis
• changed full-rank check of the design matrix/model to a faster and more robust solution

new features (causing breaking changes in the config.yaml)

• volcano plots: configurable feature lists for highlighting features if interest
• hierarchically clustered heatmaps of effect size: configurable feature lists

documentation

• update documentation accordingly
• update report to include new features and enhance readability
• update rulegraph

Bug fixes and performance improvements are not mentioned.

Full Changelog: v0.1.0...v1.0.0

v0.1.0 - stable version with complete docs

A Snakemake workflow for performing and visualizing differential expression analyses (DEA) on NGS data powered by the R package limma.

features

• Differential Expression Analysis (DEA)
  • (optional) calculation of normalization factors.
  • (optional) calculation of precision weights to model the mean-variance relationship in order to make linear models "applicable" to count data (weighted least squares) using voom.
  • (optional) block on a "group" factor in case you have repeated measurements (generalized least squares).
  • fit linear models (ordinary least squares) with the design derived from the configured formula (expects "normal" data) using lmFit.
  • the fitted model object is saved (lmfit_object.rds) for alternative downstream analyses or manual inspection e.g., contrasts.
  • (optional) estimate variance "better" using eBayes, with the robustness flag (robust=TRUE), by looking across all genes (i.e. shrunk towards a common value) and compute moderated t-statistics.
    • (optional) eBayes with limma-trend (trend=TRUE)
  • extract all statistics for variables of interest (=configured comparisons) using topTable (eg coefficients/effect size, statistical significance,...).
  • save a feature list per comparison group and direction of change (up/down) for downstream analyses (e.g., ORA enrichment analysis).
    • (optional) annotated feature list with feature names (e.g., gene symbols).
    • (optional) save feature score tables (with two columns: "feature" and "score") per comparison group for downstream analyses (e.g., preranked enrichment analysis).
    • (optional) annotated feature scores tables.
• DEA result statistics: total number of statistically significant features and split by positive (up) and negative (down) change.
• DEA result filtering of features (e.g., genes) by statistical significance, effect size, and average expression in the data.
• Log Fold Change (LFC) matrix of filtered features by comparison groups.
  • (optional) annotated LFC matrix.
• Visualizations
  • filtered DEA result statistics, i.e., number of features and direction, as stacked bar plots.
  • volanco plot per comparison with configured cut-offs for statistical significance and effect size
  • clustered heatmap of the LFC matrix
  • quality control plots
    • (optional) voom mean-variance trend
    • (optional) intermediate mean-variance trend, in case of blocking and vooming
    • post-fitting mean-variance trend
    • raw p-value distributions

documentation

• complete documentation of used software, all features, and methods
• common configuration scenarios
• external resources