https://github.com/torchstack-ai/cancer-biomarker-discovery
scRNASeq drug discovery and biomarker project
https://github.com/torchstack-ai/cancer-biomarker-discovery
bioinformatics cancer-research data-analysis data-visualization r scrna-seq-analysis startup
Last synced: 6 months ago
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scRNASeq drug discovery and biomarker project
- Host: GitHub
- URL: https://github.com/torchstack-ai/cancer-biomarker-discovery
- Owner: torchstack-ai
- License: mit
- Created: 2025-03-08T17:37:32.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T17:48:31.000Z (7 months ago)
- Last Synced: 2025-03-08T18:28:36.092Z (7 months ago)
- Topics: bioinformatics, cancer-research, data-analysis, data-visualization, r, scrna-seq-analysis, startup
- Language: R
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Cancer Biomarker Discovery Platform
## Overview
This repository contains an example analysis of multiple scRNA-Seq datasets to identify cancer biomarkers, infer mechanistic relationships, and develop a platform that could lead to prognostic evaluation. The client was a startup company we worked with that ended up raising a seed round.
This bioinformatics pipeline analyzes single-cell RNA sequencing (scRNA-seq) data to identify therapeutic targets and biomarkers in cancer treatment. We specialize in characterizing tumor heterogeneity and treatment response patterns at single-cell resolution.
## Research Objectives and Pipeline Description
### 🔬 Advanced Analytics
- **Single-cell Resolution**: Map gene expression patterns in individual cells
- **Treatment Response Profiling**: Discover molecular signatures that distinguish treatment responders from non-responders
- **Tumor Microenvironment Mapping**: Map complex cellular interactions in the tumor ecosystem
- **Immune Cell Profiling**: Analyze immune cell populations and their states in depth
### 📊 Robust Data Integration
- We integrated multiple scRNA-seq datasets seamlessly
- We corrected batch effects using the Harmony algorithm
- We implemented rigorous quality control and normalization
- We standardized all data processing steps
### 🎯 Therapeutic Target Discovery
- We analyzed differential expression across multiple cell populations
- We identified cell-type specific markers
- We performed pathway enrichment analysis
- We classified cell types using machine learning
## Business Value
### For Biotech Companies
- **Accelerate Drug Development**: Find and validate new therapeutic targets faster
- **Patient Stratification**: Create biomarker signatures to select optimal patients
- **Mechanism Insights**: Reveal drug response mechanisms at cellular resolution
- **Resource Optimization**: Focus your development on the most promising targets
### For Clinical Research
- **Treatment Response**: Track and predict treatment effectiveness
- **Resistance Mechanisms**: Uncover pathways driving drug resistance
- **Personalized Medicine**: Tailor treatment strategies to individual patients
- **Biomarker Development**: Find and validate clinical biomarkers
## Technical Capabilities
### Analysis Pipeline
1. Data Quality Control & Integration
- We automated QC metrics
- We integrated multiple datasets
- We eliminated batch effects
2. Cell Population Analysis
- We clustered cells without supervision
- We identified cell types
- We analyzed cell trajectories
3. Differential Expression
- We employed multiple comparison methods
- We ensured statistical rigor
- We analyzed pathways
4. Machine Learning
- We classified using Random Forests
- We built predictive models
- We ranked feature importance
### Data Visualization
- We created interactive UMAP plots
- We generated customizable heatmaps
- We produced publication-ready figures (not attached)
- We delivered comprehensive reports (not attached)
## Getting Started
### Prerequisites
- R (>= 4.0.0)
- Our installation script lists all required R packages
### Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/cancer-biomarker-discovery.git
# Install dependencies
Rscript setup/install_dependencies.R
```
### Usage
1. Set your parameters in `config.R`
2. Run the analysis:
```R
source("notebooks/scRNAseq_analysis.Rmd")
```
## Support
Contact us for technical support or collaboration:
- 📧 Email: scampit@torchstack.ai
- 💬 Issues: GitHub Issues
## License
We license this project under the MIT License - see the LICENSE file for details.
---
*We accelerate cancer research through advanced single-cell analytics*