Multi-sample snATAC-seq/snRNA-seq benchmarking datasets
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接：
https://zenodo.org/record/15073136
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资源简介：
Benchmarking Datasets used in:
Weinand et al., Defining effective strategies to integrate multi-sample single-nucleus ATAC-seq datasets via a multimodal-guided approach, In Prep.
Multiome (snATAC-seq + snRNA-seq) Dataset information:
Benchmarking Dataset
Original Study
Source
Cell Phenotype
Cell Count
Sample Count
1
Weinand et al., Nat Commun, 2024
RA & OA inflammatory tissue
Broad cell types
31,547
12
2
Weinand et al., Nat Commun, 2024
RA & OA inflammatory tissue
T cell states
8,069
11
3
Weinand et al., Nat Commun, 2024
RA PBMC
T cell substates
10,669
2 runs of 4 pooled samples each
4
Cheong et al., Cell, 2023
COVID-19 & healthy PBMC
Broad cell types
197,360
30
5
Cheong et al., Cell, 2023
COVID-19 & healthy PBMC
HSPC states
27,979
28
Data provided per Dataset:
inputs: All inputs for our command-line tool: https://github.com/immunogenomics/snATAC_benchmark/
outputs: Selected outputs from our tool, namely embeddings, NN metrics, LISI metrics, and UMAP coordinates for all dataset, feature, method, and correction combinations
UMAPs: UMAP plots for use in our website: https://immunogenomics.io/snATAC_benchmark/
Note: dataset4 inputs were too big to upload in one zip file, so they were uploaded in four zip files.
创建时间：
2025-03-25
