GeneXpressor 0.1.7

DESeq2 runner (Python + rpy2) for bulk RNA-seq with auto-discovery, E2E DE, plots, HTML report.

⚙️ GeneXpressor 💡

GeneXpressor is a streamlined, user-friendly tool designed for bulk RNA-seq differential expression analysis, built on the robust DESeq2 framework. It intelligently detects your count and metadata files, automates the entire DESeq2 workflow, and generates publication-quality plots along with a reproducible HTML report—all with a single command. Unlike traditional approaches, GeneXpressor allows you to process multiple datasets simultaneously, eliminating the need to select and analyze each dataset individually, making large-scale analyses faster and more efficient.​

🚀 Key Features

🔍 Auto-discovery of count + metadata files

🧠 End-to-end DESeq2 analysis with rpy2 integration

📊 Publication-ready visualizations (volcano, MA, heatmap, QC plots)

🧾 Optional HTML report summarizing results

⚙️ Cross-platform (Windows, macOS, Linux)

🧩 Supports both AUTO and manual dataset selection

Lightweight setup – no manual R scripting required

Run this in your R console:

if(!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager", repos="https://cloud.r-project.org")

BiocManager::install(c( "DESeq2", "apeglm", "ashr", "ggplot2", "pheatmap", "rmarkdown", "knitr" ), ask=FALSE)

💾 Installation pip install GeneXpressor

or

To confirm: GeneXpressor --help

🧩 Input Directory Structure Your working directory (--parent_dir) should contain:

MyProject/ ├─ counts.csv # genes × samples, first column = gene IDs ├─ metadata.csv # sample info; must include the condition column └─ (optional extra files)

Example metadata.csv:

sample condition S1 Disease S2 Control S3 Disease S4 Control

⚙️ CLI USE CASE

Example (Windows PowerShell)

genexpressor --parent_dir "C:\Users\<username>\Downloads\Deseq2-pkg" --pick AUTO --case_level Disease --control_level Control --alpha 0.05 --lfc_thr 2.0 --top_labels 20 --top_heatmap 50 --make_report true --debug true ` --threads 2

📊 Output Overview After a successful run, GeneXpressor will create a new results folder inside your working directory containing:

Example output structure:

C:\Users<username>\Downloads\Deseq2-pkg
├─ results/ │ ├─ deseq2_results.csv │ ├─ deseq2_significant.csv │ ├─ volcano_plot.png │ ├─ ma_plot.png │ ├─ heatmap.png │ └─ GeneXpressor_Report.html └─ logs/ └─ run.log

Command Options Flag Description --parent_dir Directory containing counts + metadata --pick Dataset selection (AUTO, ALL, or manual) --case_level / --control_level Condition names --alpha Adjusted p-value cutoff (default 0.05) --lfc_thr log₂ Fold-change threshold --top_labels Genes labeled in volcano plot --top_heatmap Top genes in heatmap --make_report Whether to generate HTML report --threads Parallel worker threads --debug Verbose console logging

🧪 Quick Test python -c "import genexpressor; print(genexpressor.version)" genexpressor --help 0.1.4 genexpressor CLI OK

Troubleshooting

Issue Cause / Fix

ModuleNotFoundError: genexpressor.cli Reinstall the package

(pip install --upgrade GeneXpressor)

rpy2 or R_HOME errors Ensure R is installed and on PATH (R --version)

DESeq2 not found

Install via BiocManager::install("DESeq2")

Permission denied

Run from a directory you own

Missing plots / report Check --make_report true and logs 📘Citation

If you use GeneXpressor in your work, please cite: Malik, S. (2025). GeneXpressor: Automated DESeq2 runner for bulk RNA-seq via rpy2. PyPI. https://pypi.org/project/GeneXpressor

👤 Author Sheryar Malik Bioinformatics Scientist 📧 [e-mail] 🔗 GitHub: shari01 : https://github.com/shari01/GeneXpressor

📄 License MIT License © 2025 Sheryar Malik