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--- |
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license: mit |
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tags: |
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- biology |
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- single_cell |
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- deep_neural_networks |
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- benchmark |
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pretty_name: scREF, all cells |
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--- |
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# scREF |
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This dataset contains human single cell RNA-sequencing (scRNA-seq) data collected from 46 studies and standardized |
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by Diaz-Mejia JJ et al. (2025) for the paper [Benchmarking and optimizing organism wide single-cell RNA alignment methods](https://arxiv.org/abs/2503.20730) presented at the LMRL Workshop at the International Conference on Learning Representations (2025). |
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* Folder `Phenomic-AI/scref_ICLR_2025/zarr` contains standardized single-cell RNA data for each study in `zarr` format. |
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* Sub-folder names show: `{first author, last name}_{journal}_{year}_{Pubmed ID}`. |
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* `zarr` files can be loaded as AnnData objects in Python with [Dask + Zarr](https://anndata.readthedocs.io/en/latest/tutorials/notebooks/%7Bread%2Cwrite%7D_dispatched.html) |
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* Cell-metadata includes an `obs` slot with columns: |
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- `barcode`: unique cell identifier |
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- `authors_celltype`: original author cell type annotations |
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- `standard_true_celltype`: cell type annotations standardized across studies |
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- `sample_name`: unique sample identifier |
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- `tissue_collected`: tissue where the sample was collected from |
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- `included_scref_train`: boolean indicating if the cell was included in downsampled training and benchmark analyses. |
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* Code to compute Batch Adversarially trained single-cell Variational Inference (BA-scVI) is available at https://github.com/PhenomicAI/bascvi |
