nizarassad / pan-cancer-analysis

The Cancer Genome Atlas Pan-Cancer analysis project

The Cancer Genome Atlas Pan-Cancer Analysis Project

Pan-cancer analysis involves assessing frequently mutated genes and other genomic abnormalities common to many different cancers, regardless of tumor origin. Using next-generation sequencing (NGS), pan-tumor projects such as The Cancer Genome Atlas2 have made significant contributions to our understanding of DNA and RNA variants across many cancer types.

Microscopic view of pan cancer cells: highlighting the diverse morphology of cancerous tissues

The objective of this project is to utilize regression techniques to predict a continuous value using data from The Cancer Genome Atlas (TCGA) Pan-Cancer analysis project.

Goal and Aims:

The goal of this project is to train regression models using this data to predict a continuous value representing a specific molecular characteristic of cancers. The aims are the following:

• Study the genomes of various cancers to better understand their molecular characteristics.
• Assist in developing new treatment strategies

Dataset

The data, collected from different types of tumors, can be downloaded from the two links below:

• Data CSV
• Labels CSV

Models

This project implements four different regression models using cancer genome data:

• Simple Linear Regression (SLR)
• Multiple Linear Regression (MLR)
• Ridge Regression (RR)
• Lasso Regression (LR)

Evaluation

The models are evaluated using two different metrics: mean squared error (MSE) and cross-validation.

Model	Score	Cross-validation	Mean Squared Error
Simple Linear Regression	0.98	0.98	0.05
Multiple Linear Regression	0.98	0.98	0.04
Ridge Regression	0.97	0.96	0.05
Lasso Regression	0.92	0.94	0.07

• Simple and multiple linear regression models exhibit comparable performance, while Ridge and Lasso regression models have slightly lower performance.