RNA-Seq Cancer Classification Analysis

A comprehensive machine learning pipeline for cancer type classification using RNA-Seq gene expression data from the PANCAN dataset.

📊 Project Overview

This project implements a complete end-to-end pipeline for analyzing RNA-Seq gene expression data to classify different cancer types. Using the PANCAN dataset from UCI Machine Learning Repository, we developed and evaluated multiple machine learning models to distinguish between 5 cancer types based on gene expression profiles.

🎯 Objectives

• Perform comprehensive exploratory data analysis (EDA) on RNA-Seq data
• Implement proper preprocessing pipeline for gene expression data
• Develop and compare multiple machine learning classification models
• Identify key biomarker genes for cancer classification
• Create ensemble models for robust predictions

📁 Dataset Information

Source: UCI ML Repository - Gene Expression Cancer RNA-Seq

Dataset Details:

• Samples: 801 patients
• Features: 20,531 genes
• Cancer Types: 5 classes
  • BRCA: Breast Invasive Carcinoma
  • KIRC: Kidney Renal Clear Cell Carcinoma
  • COAD: Colon Adenocarcinoma
  • LUAD: Lung Adenocarcinoma
  • PRAD: Prostate Adenocarcinoma

🚀 Key Results

• Best Model Accuracy: >95%
• Cross-Validation Score: Consistently high across all folds
• Feature Reduction: From 20,531 to optimized gene subset
• Ensemble Performance: Enhanced robustness through model combination

📋 Requirements

pandas>=1.3.0
numpy>=1.21.0
scikit-learn>=1.0.0
matplotlib>=3.4.0
seaborn>=0.11.0
plotly>=5.0.0
scipy>=1.7.0
jupyter>=1.0.0

🛠️ Installation

1. Clone the repository:

git clone https://github.com/hmzi67/rna-seq-cancer-classification.git
cd rna-seq-cancer-classification

2. Install required packages:

pip install -r requirements.txt

3. Download the dataset:

• Download data.csv and labels.csv from the UCI repository
• Place them in the project root directory

📖 Usage

Running the Complete Pipeline

Execute the Jupyter notebook cells in order:

1. Data Loading and EDA (cell_1_imports.py - cell_4_gene_expression_analysis.py)
2. Preprocessing (cell_5_distribution_analysis.py - cell_8_normalization.py)
3. Dimensionality Reduction (cell_9_dimensionality_reduction.py)
4. Machine Learning (cell_1_ml_imports.py - cell_11_save_results.py)

Quick Start

# Load preprocessed data
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import joblib

# Load data
X = pd.read_csv('processed_gene_expression_data.csv', index_col=0)
y = pd.read_csv('processed_labels.csv', index_col=0)

# Load trained model
model = joblib.load('best_cancer_classifier.pkl')

# Make predictions
predictions = model.predict(X)

🔬 Methodology

1. Data Preprocessing

• Quality Control: Missing value analysis, outlier detection
• Gene Filtering: Removed low-expression and low-variance genes
• Normalization: Log2(x + 1) transformation for RNA-Seq data
• Feature Selection: Variance-based and PCA-based selection

2. Exploratory Data Analysis

• Expression distribution analysis
• Class balance evaluation
• Sample quality assessment
• Gene variance characterization

3. Machine Learning Pipeline

• Models Evaluated:

  • Logistic Regression
  • Random Forest
  • Support Vector Machine
  • Gradient Boosting
  • Extra Trees
  • K-Nearest Neighbors
  • Naive Bayes
  • Decision Tree
  • Neural Network (MLP)

• Validation Strategy:

  • 5-fold stratified cross-validation
  • 80-20 train-test split
  • Hyperparameter tuning with GridSearchCV

4. Model Evaluation

• Accuracy, Precision, Recall, F1-score
• ROC curves and AUC scores (One-vs-Rest)
• Confusion matrices
• Feature importance analysis

📈 Results Summary

Model Performance

Model	Accuracy	F1-Score	AUC
Random Forest	0.993789	0.993758	1.000
SVM	0.987578	0.987503	0.999
Logistic Regression	0.993789	0.993758	1.000
Decision Tree	0.987578	0.987539	0.9910

Key Findings

• ✅ High classification accuracy across all cancer types
• ✅ Identified biologically relevant biomarker genes
• ✅ Ensemble methods improved model robustness
• ✅ Clear separation between cancer types in dimensionality reduction

🧬 Biological Insights

Top Contributing Genes

The analysis identified several genes with high importance for cancer classification:

• Biological pathway analysis reveals cancer-relevant gene signatures
• Results align with known cancer biomarkers in literature

📊 Visualizations

The project generates comprehensive visualizations:

• Class Distribution: Cancer type prevalence
• Expression Patterns: Gene expression distributions
• PCA Analysis: Principal component visualization
• t-SNE Plot: Non-linear dimensionality reduction
• ROC Curves: Model performance evaluation
• Confusion Matrices: Classification accuracy breakdown

🔧 Configuration

Preprocessing Parameters

MIN_SAMPLES_EXPRESSED = 0.1  # 10% of samples
MIN_EXPRESSION_LEVEL = 1.0   # Minimum expression threshold
VARIANCE_THRESHOLD = 0.1     # Bottom 10% variance filter

Model Parameters

CV_FOLDS = 5                 # Cross-validation folds
TEST_SIZE = 0.2             # Train-test split ratio
RANDOM_STATE = 42           # Reproducibility seed

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📚 References

1. Dataset: Weinstein, John N., et al. "The cancer genome atlas pan-cancer analysis project." Nature genetics 45.10 (2013): 1113-1120.
2. RNA-Seq Analysis: Love, Michael I., Wolfgang Huber, and Simon Anders. "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology 15.12 (2014): 1-21.
3. Machine Learning: Pedregosa, F., et al. "Scikit-learn: Machine learning in Python." Journal of machine learning research 12.Oct (2011): 2825-2830.

👤 Author

hmzi67

• GitHub: @hmzi67
• LinkedIn: Connect with me

🙏 Acknowledgments

• UCI Machine Learning Repository for providing the dataset
• The Cancer Genome Atlas (TCGA) Research Network
• Open-source community for excellent machine learning tools

📊 Project Status

Status: ✅ Complete
Last Updated: 2025-06-13 07:12:05 UTC
Version: 1.0.0
Created By: hmzi67

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