--- a
+++ b/README.md
@@ -0,0 +1,102 @@
+# 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*