# 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.

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*We accelerate cancer research through advanced single-cell analytics*