Neuro-Genomics Project

This project focuses on analyzing sequencing data to uncover molecular mechanisms in neurological diseases and evaluate immunotherapy potential in breast cancer. It includes Python and R scripts for data processing, analysis, and visualization.

Table of Contents

• Overview
• Technologies Used
• File Structure
• How to Run the Project
• Results
• License
• Contributors

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Overview

Part 1: Bulk Sequencing Analysis

• Objective: Analyze RNA-seq data to identify molecular deficiencies in a mouse knockout model.
• Key Steps:
  1. Preprocessing RNA-seq data using Python and kallisto.
  2. Importing and analyzing transcript-level data in R using tximport.
  3. Conducting differential gene expression analysis with DESeq2.

Part 2: Single-Cell Sequencing Analysis

• Objective: Predict the effectiveness of immunotherapy for a breast cancer patient.
• Key Steps:
  1. Preprocessing single-cell RNA-seq data with Seurat.
  2. Clustering cells and identifying cell types based on marker genes.
  3. Visualizing spatial distributions and answering clinical questions.

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Technologies Used

• Languages: Python, R
• Tools: kallisto, tximport, DESeq2, Seurat
• Visualization: UMAP, PCA, ggplot2
• Platforms: GitHub for version control

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File Structure

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├── finel_projet_part1.ipynb   # Python script for preprocessing RNA-seq data
├── genomics_project_Q1.R      # R script for Part 1 - analysis of bulk sequencing
├── genomics_project_Q2.R      # R script for Part 2 - analysis of single cell sequencing
├── Neuro Genomics Project.pdf # Detailed project report
├── neuro-genomics project instructions.pdf # Project instructions
├── README.md                  # Project documentation

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How to Run the Project

Prerequisites

• Python 3.x
• R with the following packages installed: kallisto, tximport, DESeq2, Seurat

Instructions

1. Clone the repository:

   git clone https://github.com/12danielLL/Neurogenomics_Project.git
   cd neuro-genomics-project

2. Install dependencies:
   • Python: pip install -r requirements.txt
   • R: Install the required packages manually or use the script install_packages.R.
3. Run the Python preprocessing script:

   python scripts/preprocess.py

4. Execute R scripts for analysis:
   • Bulk analysis: Rscript scripts/bulk_analysis.R
   • Single-cell analysis: Rscript scripts/single_cell_analysis.R

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Results

Bulk Sequencing Analysis

• Key pathways identified: lipid metabolism, neurological function, and cell structure.
• Potential treatment strategies proposed: supporting myelin production.

Single-Cell Analysis

• Immune cells comprise over 50% of the biopsy.
• Immune cells are spatially mixed with tumor cells, increasing the likelihood of immunotherapy success.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contributors

• Daniel Broker
• Or Shachar