📊 Lecture 3: Linear Regression of Cancer Data

Learn to analyze massive biological datasets using Python packages like Pandas. Explore real cancer dependency data and discover gene relationships.

🎯 Getting Started

Start with Built-in Packages - Learn the tools that come free with Python.

Master Pandas DataFrames - Handle millions of data points effortlessly.

Real cancer research data - Work with the same datasets used in actual research.

Available

📦 Built-in Python Packages for Biology

Discover powerful tools that come with every Python installation

  • ▸random - Simulations and sampling
  • ▸collections.Counter - Count nucleotides and amino acids
  • ▸statistics - Built-in statistical functions
  • ▸datetime - Experiment timing and tracking
  • ▸pathlib - Cross-platform file management
  • ▸csv - Simple data file I/O
Open in Colab
Available

🐼 Introduction to Pandas

Master DataFrames for analyzing large biological datasets

  • ▸Creating and loading DataFrames
  • ▸Selecting rows and columns
  • ▸Filtering biological data
  • ▸Basic statistics and summaries
  • ▸Working with the DepMap dataset
Open in Colab
Available

🧬 DepMap Cancer Dependencies

Analyze real cancer cell line dependency data

  • ▸Loading the DepMap dataset
  • ▸Exploring cancer cell lines
  • ▸Finding gene dependencies
  • ▸Calculating correlations
  • ▸Identifying similar genes to ATR
Open in Colab
Available

📊 Data Filtering & Selection

Advanced techniques for working with large datasets

  • ▸Boolean indexing with biological data
  • ▸Filtering by cell line type
  • ▸Selecting specific genes
  • ▸Handling missing values
  • ▸Creating data subsets
Open in Colab
Available

🔬 Statistical Analysis

Perform statistical analysis on biological data

  • ▸Basic statistics with Python
  • ▸Mean, median, and standard deviation
  • ▸Understanding biological variation
  • ▸Statistical significance
  • ▸Applying statistics to datasets
Open in Colab
Available

🔄 Sorting & Ranking Data

Sort and rank biological datasets to find insights

  • ▸Sorting DataFrames by values
  • ▸Finding top and bottom genes
  • ▸Multi-column sorting
  • ▸Ranking gene dependencies
  • ▸Identifying outliers and extremes
Open in Colab
Apply It!

🧪 Biological Data Analysis Toolkit

Real Python tools for comprehensive data analysis!

  • ▸Complete DepMap analysis pipeline
  • ▸Gene dependency explorer
  • ▸Statistical analysis tools
  • ▸Data visualization helpers
  • ▸Integrated analysis workflows
Open in Colab

🗺️ Learning Path

Week 1: Built-in packages and basic data handling

Week 2: Pandas fundamentals and DataFrame operations

Week 3: Statistical analysis and correlations

Week 4: Linear regression modeling

🚀 Final Project: Cancer Gene Dependency Analysis

By the end of this lecture series, you'll analyze real cancer research data and:

  • ✓Load and explore the DepMap cancer dataset (1,200+ cell lines)
  • ✓Identify genes with similar dependencies to ATR kinase
  • ✓Perform correlation analysis on 30,000+ genes
  • ✓Build linear regression models for gene relationships
  • ✓Filter and analyze specific cancer types
  • ✓Generate publication-ready results and insights

📚 Part of the Python for Biologists course by Helfrid Hochegger

University of Sussex | Year 3 Biology, Biochemistry & Neuroscience