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
2. We automated QC metrics
3. We integrated multiple datasets

4. We eliminated batch effects

5. Cell Population Analysis

6. We clustered cells without supervision
7. We identified cell types

8. We analyzed cell trajectories

9. Differential Expression

10. We employed multiple comparison methods
11. We ensured statistical rigor

12. We analyzed pathways

13. Machine Learning

14. We classified using Random Forests
15. We built predictive models
16. 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

# 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:

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