Update README.md

README.md

@@ -1,9 +1,20 @@
+---
+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 public respositories and standardized 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).
+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](https://arxiv.org/abs/2503.20730) 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 analyzed, in `zarr` format. Sub-folder names show: `{first author, last name}_{journal}_{year}_{Pubmed ID}`.
+* 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](https://anndata.readthedocs.io/en/latest/tutorials/notebooks/%7Bread%2Cwrite%7D_dispatched.html)
 * Cell-metadata includes an `obs` slot with columns:
     - `barcode`: unique cell identifier
@@ -12,4 +23,4 @@ This dataset contains human single cell RNA-sequencing (scRNA-seq) data collecte
     - `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
+* Code to compute Batch Adversarially trained single-cell Variational Inference (BA-scVI) is available at https://github.com/PhenomicAI/bascvi