This repository contains R and Python scripts used in the Rashid Lab for bulk and single-cell RNA-seq analysis. Our work focuses on comparing gene expression across tissues and developmental states, identifying differentially expressed genes, and translating those changes into pathway- and mechanism-level interpretation.
| Folder | Description |
|---|---|
DESeq2_updated |
Current DESeq2 differential expression pipeline with the latest parameter tuning and output structure |
DESeq2_refactored_3_level_3_10_26 |
Refactored DESeq2 pipeline supporting multi-level (3-group) contrasts |
DESeq2_Clemson_output |
DESeq2 analysis outputs for the Clemson dataset |
Gene_enrichment |
Gene ontology (GO) and pathway enrichment analysis scripts |
RNAseq_pathway_pipeline |
End-to-end pipeline from count matrices to pathway-level results |
ssRNAseq_analysis |
Single-cell RNA-seq analysis scripts and outputs |
HOX_focus |
Scripts focused on HOX gene expression analysis |
cGAST:STING_codex |
cGAS-STING pathway-focused analysis and visualization |
3D_network map |
Scripts for generating 3D gene network visualizations |
cnet_plot_tool |
Concept network (cnet) plot generation tool |
sPLSDA |
Sparse Partial Least Squares Discriminant Analysis scripts |
gene_validation_tool |
Tools for validating gene lists and cross-referencing annotations |
ggallus_conversion_tool |
Gene ID conversion utilities for Gallus gallus (chicken) genome |
Project_specific_analysis |
One-off or project-specific analysis scripts |
Claude_RNAseq |
Experimental scripts developed with AI assistance |
Most scripts require R (>= 4.0) with the following commonly used packages:
- DESeq2
- clusterProfiler
- ggplot2
- enrichplot
- mixOmics (for sPLSDA)
- dplyr, tidyr, readr (tidyverse)
Python scripts require Python 3 with pandas, numpy, matplotlib.
To install Bioconductor packages in R:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("DESeq2", "clusterProfiler", "enrichplot"))
- Prepare your count matrix - Scripts expect a gene x sample count matrix (CSV or TSV) and a corresponding sample metadata table.
- Run DESeq2 - Start with the DESeq2_updated folder for the most current differential expression workflow.
- Pathway enrichment - Feed DESeq2 output into Gene_enrichment or RNAseq_pathway_pipeline for GO/KEGG enrichment analysis.
- Visualization - Use cnet_plot_tool and 3D_network map for network-level visualization of results.
Each folder contains its own scripts and, where applicable, example input/output files.
For questions about this repository, please contact Galen O'Shea-Stone @ galenoshea@gmail.com