| --- |
| 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: |
|
|
| 1. Use preprocessed data directly for ML models |
| 2. 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 |
