rnaseqDegy

🚀 Unified Bulk RNA‑seq QC, Differential Expression, and Enrichment Pipeline

Author: Dr. Eric Bareke (Majewski Lab, McGill University)
Repository: https://github.com/ebareke/rnaseqDegy

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rnaseqDegy is an R package providing a single, fully automated pipeline for bulk RNA‑seq analysis — covering QC, differential expression, GO ORA, and GSEA using user‑supplied GMT files, for both human and mouse datasets.

The package is designed for research labs, HPC clusters, and automated workflows, offering clean, reproducible, publication‑ready outputs.

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✨ Key Features

🔍 QC & Normalization

• Count filtering (≥10 counts in ≥50% samples)
• PCA (with optional batch correction via limma::removeBatchEffect)
• Sample correlation heatmap (Pearson/Spearman/Kendall)
• Top 500 most variable gene heatmap
• Auto‑dimensioned plots with publication‑ready styling

⚔ Differential Expression

• Choose DESeq2 or edgeR
• Batch included in the design (~ Batch + Condition)
• Export full results + normalized counts

🌋 Volcano Plots

• Highlights top 10 up/down DEGs
• Shows counts of Up/Down genes in subtitle
• Custom colour scheme and bold theme

🧬 Functional Enrichment

ORA (GO BP/MF/CC)

• Uses clusterProfiler
• Simplification by semantic similarity
• Dotplot, barplot, cnetplot, emapplot

GSEA (offline, user GMT)

• Accepts any custom GMT file
• Dotplot, barplot, cnetplot, emapplot
• Auto‑filtered term subset via gmt_terms

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📦 Installation

This package requires Bioconductor dependencies. Install everything cleanly with:

# Install devtools if not present
install.packages("devtools")

# Install rnaseqDegy from GitHub
devtools::install_github("ebareke/rnaseqDegy")

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🧠 Quick Start

library(rnaseqDegy)

run_rnaseqDegy(
  counts = "counts.tsv",
  design = "design.tsv",
  species = "human",
  de_method = "DESeq2",
  condition_reference = "Control",
  condition_test = "Mutant",
  batch_correction = TRUE,
  run_ora = TRUE,
  run_gsea = TRUE,
  gmt_dir = "gmt/",
  gmt_files = c("HALLMARK.gmt", "GO_BP.gmt"),
  output_dir = "results/Control_vs_Mutant",
  prefix = "C_vs_M"
)

This will generate:

• QC plots
• DE tables + volcano
• ORA (BP/MF/CC)
• GSEA (for each GMT)

Outputs are auto‑named and saved as PDF + PNG.

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📁 Input File Format

1. Count Matrix (TSV)

GeneID    S1   S2   S3   S4
ENSG0001  50   23   11   9
ENSG0002  140  180  90   76
...

• First column: GeneID (Ensembl or Symbol)
• Following columns: raw integer counts

2. Design File (TSV)

Sample   Batch   Condition
S1       B1      Control
S2       B1      Control
S3       B2      Mutant
S4       B2      Mutant

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🧬 Output Structure

results/
 ├── QC_PCA_*.pdf/png
 ├── QC_sampleCorrelation.pdf/png
 ├── QC_topVar500_heatmap.pdf/png
 ├── DE_results_all.tsv
 ├── normalized_counts.tsv
 ├── DE_volcano.pdf/png
 ├── ORA_GO_BP_*.tsv + plots
 ├── ORA_GO_MF_*.tsv + plots
 ├── ORA_GO_CC_*.tsv + plots
 ├── GSEA_<gmt>_*.tsv + plots

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🧪 Example Folder Layout

project/
 ├── counts.tsv
 ├── design.tsv
 ├── gmt/
 │    ├── HALLMARK.gmt
 │    └── GO_BP.gmt
 └── rnaseqDegy_results/

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⚙ Advanced Options

• filter_min_count, filter_min_prop
• batch_correction = TRUE/FALSE
• gmt_terms = c("GOBP_APOPTOSIS","MYC_TARGETS_V2")
• corr_method = "spearman"
• output_dir, prefix

All parameters mirror the original CLI pipeline.

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📘 Citation

If you use rnaseqDegy in your research, please cite:

Dr. Eric Bareke, Majewski Lab, McGill University.

(A full CITATION.cff file is included in this repository.)

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🤝 Contributing

See CONTRIBUTING.md for guidelines. Pull requests are welcome.

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📝 License

MIT License — see LICENSE file.