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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Healthy Vital Tissue Atlas data for CAR-Machine pipeline

Authors: Mumme, Hope;

Healthy Vital Tissue Atlas data for CAR-Machine pipeline

Abstract

Healthy Vital Tissue Atlas compiled for CAR-T identification pipeline project. 1. Files: File Name Description hema_genes.csv Feature names for the hematopoietic subset. hema_obs.csv Cell metadata for the hematopoietic subset. hema_sparse_matrix.p Sparse expression matrix (pickle format) for hematopoietic subset. Log-transformed counts (computed with scanpy normalize_total, log1p functions). hvta_celltype_weights.csv Example weights for HVTA cell-types. hvta_celltypes_category.csv Broad categories for cell-types in HVTA. hvta_tissue_weights.csv Example weights for HVTA tissues. nonhema_genes.csv Feature names for the non-hematopoietic subset. nonhema_obs.csv Cell metadata for the non-hematopoietic subset. nonhema_sparse_matrix.p Sparse expression matrix (pickle format) for non-hematopoietic subset. Log-transformed counts (computed with scanpy normalize_total, log1p functions). 2. Made up of 29 tissue types, encompassing over 1.5 million cells, and retrieved from the following 18 sources: Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nature Communications 11, 2285, doi:10.1038/s41467-020-16164-1 (2020). Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290, doi:doi:10.1126/science.abl4290 (2022). Han, X. et al. Construction of a human cell landscape at single-cell level. Nature 581, 303-309, doi:10.1038/s41586-020-2157-4 (2020). Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nature Methods 14, 955-958, doi:10.1038/nmeth.4407 (2017). Regev, A. et al. The Human Cell Atlas. eLife 6, e27041, doi:10.7554/eLife.27041 (2017). Madissoon, E. et al. scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biology 21, 1, doi:10.1186/s13059-019-1906-x (2019). Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512-518, doi:10.1038/s41586-019-1631-3 (2019). James, K. R. et al. Distinct microbial and immune niches of the human colon. Nature Immunology 21, 343-353, doi:10.1038/s41590-020-0602-z (2020). Ma, F. et al. Single cell and spatial sequencing define processes by which keratinocytes and fibroblasts amplify inflammatory responses in psoriasis. Nature Communications 14, 3455, doi:10.1038/s41467-023-39020-4 (2023). Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046, doi:doi:10.1126/science.add7046 (2023). Tosti, L. et al. Single-Nucleus and In Situ RNA–Sequencing Reveal Cell Topographies in the Human Pancreas. Gastroenterology 160, 1330-1344.e1311, doi:10.1053/j.gastro.2020.11.010 (2021). Gray, G. K. et al. A human breast atlas integrating single-cell proteomics and transcriptomics. Developmental Cell 57, 1400-1420.e1407, doi:10.1016/j.devcel.2022.05.003 (2022). Li, J. et al. Integrated multi-omics single cell atlas of the human retina. bioRxiv, 2023.2011.2007.566105, doi:10.1101/2023.11.07.566105 (2023). Guo, J. et al. The adult human testis transcriptional cell atlas. Cell Research 28, 1141-1157, doi:10.1038/s41422-018-0099-2 (2018). Elmentaite, R. et al. Cells of the human intestinal tract mapped across space and time. Nature 597, 250-255, doi:10.1038/s41586-021-03852-1 (2021). Jin, C. et al. Molecular and genetic insights into human ovarian aging from single-nuclei multi-omics analyses. Nature Aging, doi:10.1038/s43587-024-00762-5 (2024). Williams, D. W. et al. Human oral mucosa cell atlas reveals a stromal-neutrophil axis regulating tissue immunity. Cell 184, 4090-4104.e4015, doi:10.1016/j.cell.2021.05.013 (2021). Park, J.-E. et al. A cell atlas of human thymic development defines T cell repertoire formation. Science 367, eaay3224, doi:doi:10.1126/science.aay3224 (2020).

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average