PIASO tutorial and processed single-cell omics datasets

This record contains a collection of single-cell omics datasets in AnnData HDF5 (.h5ad) format and other formats associated with PIASO (Precise Integrative Analysis of Single-cell Omics), a Python toolkit for single-cell data analysis (https://github.com/genecell/PIASO). The record bundles two categories of files: (i) input datasets used in the official PIASO tutorials, and (ii) PIASO-processed datasets containing derived results (embeddings, cluster labels, marker genes, integrated representations) produced by running PIASO modules on those inputs.

Contents. The archive covers the data modalities demonstrated in the PIASO tutorials, e.g.,:

single-cell RNA-seq (scRNA-seq) reference and query datasets used to illustrate normalization (INFOG), dimensionality reduction and integration (GDR), marker gene identification (COSG), cell-type annotation, and gene set scoring;

scATAC-seq datasets and snMultiome datasets used to demonstrate PIASO's ATAC preprocessing;

spatial transcriptomics datasets (e.g., MERFISH / Xenium) used to demonstrate scRNA-seq ↔ spatial integration and ligand-receptor interaction analysis, etc;

Files in this version.

• SEA-AD_RNA_MTG_subsample_excludeReference_20k_piaso.h5ad (1.79 GB) — 20,000-cell subsample of the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD), Middle Temporal Gyrus snRNA-seq, human. Raw UMI counts in layers['UMIs']. Source: Gabitto, M.I., Travaglini, K.J., Rachleff, V.M. et al. Integrated multimodal cell atlas of Alzheimer’s disease. Nat Neurosci 27, 2366–2383 (2024). https://doi.org/10.1038/s41593-024-01774-5

• AdultCortexMultiomeRNA_integrated_anno.h5ad (2.48 GB) — 17,412-cell integrated scRNA-seq from adult mouse cortex (P57), Multiome RNA modality across 5 batches; annotated cell types in obs['CellTypes']. Source: Bravo González-Blas, C., De Winter, S., Hulselmans, G. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods 20, 1355–1367 (2023). https://doi.org/10.1038/s41592-023-01938-4

• 10k_Mouse_Brain_CNIK_3p_gemx_10k_Mouse_Brain_CNIK_3p_gemx_count_sample_filtered_feature_bc_matrix.h5 (65.5 MB) — 10x Genomics 10K Mouse Brain CNIK, 3' GEM-X v4 chemistry, CellRanger-filtered matrix. Source: 10x Genomics public dataset• neuron_10k_v3_filtered_feature_bc_matrix.h5 (45.4 MB) — 10x Genomics E18 mouse brain neurons, 10K cells, Chromium v3, CellRanger-filtered matrix. Source: 10x Genomics public dataset• SC3_v3_NextGem_DI_Nuclei_5K_SC3_v3_NextGem_DI_Nuclei_5K_count_sample_feature_bc_matrix.h5 (19.3 MB) — 10x Genomics E18 mouse brain nuclei, 5K, Chromium Next GEM v3.1, CellRanger-filtered matrix. Source: 10x Genomics public dataset• 10k_Mouse_Neurons_3p_gemx_10k_Mouse_Neurons_3p_gemx_count_sample_filtered_feature_bc_matrix.h5 (64.5 MB) — 10x Genomics E18 mouse neurons, 10K, GEM-X v4, CellRanger-filtered matrix. Source: 10x Genomics public dataset

• PBMCMultiomeRop2023_SAN1.h5 (73.2 MB) — Human PBMC snMultiome RNA, SAN1 sample; CellRanger-filtered 10x Multiome RNA matrix. Source: De Rop, F.V., Hulselmans, G., Flerin, C. et al. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 42, 916–926 (2024). https://doi.org/10.1038/s41587-023-01881-x• PBMCMultiomeRop2023_SAN2.h5 (83.7 MB) — Human PBMC snMultiome RNA, SAN2 sample; CellRanger-filtered 10x Multiome RNA matrix. Source: De Rop, F.V., Hulselmans, G., Flerin, C. et al. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 42, 916–926 (2024). https://doi.org/10.1038/s41587-023-01881-x

• PIASOmarkerDB_AllenHumanImmuneHealthAtlas_L2_251219.csv (114.6 KB) — Pre-computed marker gene database entry for the Allen Human Immune Health Atlas at L2 annotation granularity; CSV with marker genes and specificity scores. Source: Gong, Q., Sharma, M., Glass, M.C. et al. Multi-omic profiling reveals age-related immune dynamics in healthy adults. Nature 648, 696–706 (2025). https://doi.org/10.1038/s41586-025-09686-5

Intended use. (i) Reproducing the results shown in the PIASO tutorials at https://piaso.org and https://genecell.github.io/PIASO, (ii) benchmarking PIASO against other single-cell analysis toolkits, and (iii) teaching and methods development. Primary data were obtained from publicly available sources; redistributed datasets retain their original licenses where stricter than CC BY 4.0, and per-file attribution is provided in the included README. PIASO-processed fields (embeddings, cluster labels, marker gene results) within these files are contributed under CC BY 4.0.

Software. PIASO is available at https://github.com/genecell/PIASO and can be installed via pip install piaso-tools or conda install -c conda-forge -c bioconda piaso.

Citation. If PIASO is useful for your research, please consider citing: Wu, S.J., Dai, M. et al. Pyramidal neurons proportionately alter cortical interneuron subtypes. Nature (2026). https://doi.org/10.1038/s41586-025-09996-8