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Sample Datasets

Real biological datasets for practicing data analysis and building your Python skills. All datasets are research-grade and used in published studies.

📊 About the Cancer Dependency Map (DepMap)

The Cancer Dependency Map is a genome-wide CRISPR screening dataset that measures how dependent cancer cell lines are on each gene for survival. High dependency scores indicate genes that are essential for cancer cell survival.

🔬
CRISPR Screening
Systematic gene knockouts
🧪
Cancer Cell Lines
Diverse cancer types
📈
Dependency Scores
Gene essentiality data
Learn more about DepMap↗

📁 Available Datasets

🧬

Complete DepMap Dataset

24Q2Advanced

Complete gene dependency dataset from the Cancer Dependency Map project

Dataset Features:

  • •~18,000 genes across cancer cell lines
  • •CRISPR knockout dependency scores
  • •Cell line metadata including cancer type
  • •Comprehensive dataset for advanced analysis
Used In:
Lecture 3
File Size:
~45 MB
Download CSV (~45 MB)
🎯

Filtered DepMap Dataset

24Q2Beginner-Friendly

Curated subset focusing on breast and myeloid cancers

Dataset Features:

  • •Breast and myeloid cancer cell lines only
  • •Same gene dependency data structure
  • •Smaller dataset perfect for learning
  • •Ideal for classroom exercises
Used In:
Lecture 3
File Size:
~12 MB
Download CSV (~12 MB)

📋 Dataset Structure

Columns Include:

  • •Cell Line ID: Unique identifier for each cancer cell line
  • •Cancer Type: Primary cancer classification
  • •Gene Symbols: Standard gene nomenclature
  • •Dependency Scores: CRISPR knockout effects

Analysis Ideas:

  • •Find genes essential across all cancer types
  • •Identify cancer-specific dependencies
  • •Compare dependency patterns between cancers
  • •Discover potential drug targets

💡 How to Use These Datasets

1️⃣

Download

Click download to save the CSV file to your computer

2️⃣

Load in Python

Use pandas.read_csv() to import the data into your notebook

3️⃣

Explore & Analyze

Follow along with lecture notebooks to discover biological insights

📝 Quick Start Code:

import pandas as pd
df = pd.read_csv('combined_model_crispr_data_filtered.csv')
df.head()