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license: mit
task_categories:
- tabular-regression
tags:
- biology
- genomics
pretty_name: "gReLU tutorial 3 dataset (Microglia scATAC-seq)"
size_categories:
- 10K<n<100K
---
# microglia-scatac-tutorial-data
## Dataset Summary
This dataset contains pseudobulk scATAC-seq data for human microglia, derived from the study by Corces et al. (2020) (https://www.nature.com/articles/s41588-020-00721-x). Genome coordinates correspond to the hg38 reference genome. This data is used in tutorial 3 of gReLU (https://github.com/Genentech/gReLU/blob/main/docs/tutorials/3_train.ipynb).
## Dataset Structure
The dataset is divided into two files: peaks and fragments.
### Statistics
| File | Rows | Description |
|------|------|-------------|
| `peak_file.narrowPeak` | 83,320 | Called ATAC-seq peaks |
| `fragment_file.bed` | 57,919,016 | Individual ATAC-seq fragments |
### 1. Peaks file (`peak_file.narrowPeak`)
Standard ENCODE narrowPeak format (tab-separated, no header).
- `chrom`: Chromosome / Contig name
- `start`: 0-based start position
- `end`: End position
- `name`: Peak identifier
- `score`: Integer score for display
- `strand`: Orientation
- `signalValue`: Measurement of overall enrichment
- `pValue`: Statistical significance (-log10)
- `qValue`: False discovery rate (-log10)
- `peak`: Point-source (summit) relative to start
### 2. Fragments file (`fragment_file.bed`)
Standard BED6 format representing individual ATAC-seq fragments.
- `chrom`: Chromosome
- `start`: Start position
- `end`: End position
- `source`: Sequencing run identifier (e.g., `SRR11442505`)
- `score`: Placeholder (0)
- `strand`: Orientation
## Usage
```python
from huggingface_hub import hf_hub_download
import pandas as pd
peak_path = hf_hub_download(
repo_id="Genentech/microglia-scatac-tutorial-data",
repo_type="dataset",
filename="peak_file.narrowPeak"
)
peaks = pd.read_csv(peak_path, sep='\t', header=None)
frag_path = hf_hub_download(
repo_id="Genentech/microglia-scatac-tutorial-data",
repo_type="dataset",
filename="fragment_file.bed"
)
fragments = pd.read_csv(frag_path, sep='\t', header=None,
names=['chrom', 'start', 'end', 'source', 'score', 'strand'])
```
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