README.md

metadata

license: mit
tags:
  - biology
  - single_cell
  - deep_neural_networks
  - benchmark
pretty_name: scREF, all cells

scREF

This dataset contains human single cell RNA-sequencing (scRNA-seq) data collected from 46 studies and standardized by Diaz-Mejia JJ et al. (2025) for the paper Benchmarking and optimizing organism wide single-cell RNA alignment methods presented at the LMRL Workshop at the International Conference on Learning Representations (2025).

• Folder Phenomic-AI/scref_ICLR_2025/zarr contains standardized single-cell RNA data for each study in zarr format.
• Sub-folder names show: {first author, last name}_{journal}_{year}_{Pubmed ID}.
• zarr files can be loaded as AnnData objects in Python with Dask + Zarr
• Cell-metadata includes an obs slot with columns:
  • barcode: unique cell identifier
  • authors_celltype: original author cell type annotations
  • standard_true_celltype: cell type annotations standardized across studies
  • sample_name: unique sample identifier
  • tissue_collected: tissue where the sample was collected from
  • included_scref_train: boolean indicating if the cell was included in downsampled training and benchmark analyses.
• Code to compute Batch Adversarially trained single-cell Variational Inference (BA-scVI) is available at https://github.com/PhenomicAI/bascvi