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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 configurations: `peaks` and `fragments`.
### 1. Peaks Configuration (`peak_file.narrowPeak`)
Standard ENCODE narrowPeak format (tab-separated).
- `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 Configuration (`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 grelu.io.bed
import pandas as pd
peak_path = hf_hub_download(
repo_id="Genentech/microglia-scatac-tutorial-data",
repo_type="dataset",
filename="peak_file.narrowPeak"
)
peaks = grelu.io.bed.read_narrowpeak(peak_file)
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'])
``` |