README.md

metadata

license: mit
task_categories:
  - tabular-regression
tags:
  - biology
  - genomics
pretty_name: gReLU tutorial 3 dataset (Microglia scATAC-seq)
size_categories:
  - 10K<n<100K
configs:
  - config_name: peaks
    data_files:
      - split: train
        path: peak_file.narrowPeak
  - config_name: fragments
    data_files:
      - split: train
        path: fragment_file.bed

tutorial-3-data (Microglia scATAC pseudobulk)

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

Loading Peaks

from datasets import load_dataset

dataset = load_dataset("Genentech/tutorial-3-data", "peaks", split="train", delimiter="\t")
dataset = load_dataset("Genentech/tutorial-3-data", "fragments", split="train", delimiter="\t")