single-cell
C2S-Pythia-410m-diverse-single-and-multi-cell-tasksvandijklab_-_C2S-Pythia-410m-diverse-single-and-multi-cell-tasks-ggufC2S-Pythia-410m-diverse-single-and-multi-cell-tasks-GGUFcellfinder_single_channel_defaultC2S-Pythia-410m-diverse-single-and-multi-cell-tasks-GGUFC2S-Pythia-410m-diverse-single-and-multi-cell-tasks-mlx-4Bitvandijklab_-_C2S-Pythia-410m-diverse-single-and-multi-cell-tasks-awqSingle_Cell_Classifier
single-cell-brain-zarr
Single-Cell Brain Zarr Collection
Production-ready brain single-cell RNA-seq data exported from the CellxGene Census into native Zarr stores for chunked, on-demand access on the Hugging Face Hub.
Why Zarr
Single-cell expression matrices get impractical fast if you treat them like ordinary dense files. Zarr is the point of this repo: it makes large atlas-scale data usable without forcing users to download or materialize the whole matrix before they can do anything useful.… See the full description on the dataset page: https://huggingface.co/datasets/KokosDev/single-cell-brain-zarr.aging-gene-expression-single-cell-mouse
A single-cell transcriptomic atlas characterizes ageing tissues in the mouse
https://www.nature.com/articles/s41586-020-2496-1#Sec2
Code to download and process this dataset is available in: https://github.com/seanome/2025-longevity-x-ai-hackathon
Dataset structure is originally from AnnData.
Descriptions of each data file is below.
Data Files
This dataset contains multiple parquet files, one for each sheet in the original Excel file:… See the full description on the dataset page: https://huggingface.co/datasets/longevity-db/aging-gene-expression-single-cell-mouse.gene-expression-single-cell-mouse
A single-cell transcriptomic atlas characterizes ageing tissues in the mouse
Code to download and process this dataset is available in: https://github.com/seanome/2025-longevity-x-ai-hackathon
Dataset structure is originally from AnnData.
Descriptions of each data file is below.
Data Files
This dataset contains multiple parquet files, one for each sheet in the original Excel file:
gene-expression-single-cell-mouse_*.parquet - Data files containing gene expression and… See the full description on the dataset page: https://huggingface.co/datasets/longevity-db/gene-expression-single-cell-mouse.SingleCell-Unseen-Benchmark
SingleCell-Unseen-Benchmark
Overview
SingleCell-Unseen-Benchmark is a large-scale unseen single-cell transcriptomic benchmark designed to systematically evaluate foundation models on cell identification and cell type tracing tasks.The benchmark covers tumor, stem, neural, and normal cell populations, with a particular emphasis on unseen data distributions, including rare cell types, cross-dataset generalization, and heterogeneous tumor states.
In addition to curated… See the full description on the dataset page: https://huggingface.co/datasets/SiatBioInf/SingleCell-Unseen-Benchmark.DeepSpot2Cell-HEST1k-Virtual-SingleCell
DeepSpot2Cell Virtual Single-Cell Spatial Transcriptomics
Virtual single-cell gene expression predictions for Visium spatial transcriptomics
samples, generated by DeepSpot2Cell.
Overview
This dataset provides predicted single-cell gene expression profiles for
Visium samples across 5,000 genes. The predictions were generated by running
a trained DeepSpot2Cell model on preprocessed Visium data from HEST-1k.
DeepSpot2Cell uses a permutation-invariant DeepSet… See the full description on the dataset page: https://huggingface.co/datasets/GravityBeng/DeepSpot2Cell-HEST1k-Virtual-SingleCell.PillarCorpus-single-cell
Dataset Card for PillarCorpus-single-cell
Dataset Details
Dataset Description
PillarCorpus includes over 90M single-cell RNA sequencing (scRNA-seq) profiles spanning human and mouse samples. This dataset covers multiple disease conditions and tissue types from various publicly available sources.
Curated by: (QiuLab)[https://www.devo-evo.com/]
License: MIT
Uses
The dataset has contains the following columns:
gene_ids:
Stored as… See the full description on the dataset page: https://huggingface.co/datasets/Aristotleo/PillarCorpus-single-cell.