Multi-sample snATAC-seq/snRNA-seq benchmarking datasets

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.