Datasets:

DeweyWang
/

ETH_ML4G_Project-1

license: mit
task_categories:
  - feature-extraction
tags:
  - genomics
  - gene-expression
  - epigenetics
  - bioinformatics
  - machine-learning
size_categories: 100K<n<1M
language:
  - en
pretty_name: Chromatin Landscape

Gene Expression Prediction Dataset

📌 Overview

This dataset is designed for predicting gene expression levels from chromatin landscape data, including histone modifications and chromatin accessibility.

It is part of a machine learning project in genomics, where the goal is to model the relationship between epigenetic signals and gene expression.

👉 Full project code (including preprocessing and prediction): https://github.com/Dewey-Wang/Gene-expression-prediction/tree/main

📂 Dataset Structure

The dataset consists of two main components:

1. Raw Data

Total size: 18.66 GB
Number of files: 72
Includes:
- Histone modification data (ChIP-seq)
- Chromatin accessibility (DNase-seq)
- Gene expression (CAGE)
- Gene annotation (TSS, gene body, RefSeq)

2. Preprocessed Data

Total size: 6.36 GB
Number of files: 53
Includes:
- Feature matrices for machine learning
- Aggregated signals around genomic regions (e.g. TSS windows)
- Normalized inputs ready for model training

👉 Full preprocessing code is available in the GitHub repository above.

🎯 Task

The main task is:

Predict gene expression levels from chromatin features

Input: epigenetic signals (ChIP-seq, DNase-seq)
Output: gene expression values

📊 Evaluation

Typical evaluation metrics:

Spearman correlation (primary)
Pearson correlation
R² score

🧬 Data Details

Genome version: hg38 / GRCh38
Multiple cell lines included
Data normalized for cross-cell-line comparison

🚀 Usage

You can either:

Use preprocessed data directly for ML models
Reproduce preprocessing using provided code

⚠️ Notes

Raw data is large (~18.66 GB)
Preprocessed data is recommended for quick experimentation
Suitable for machine learning and bioinformatics research