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• W4387569894 abstract "Abstract Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use." @default.
• W4387569894 created "2023-10-13" @default.
• W4387569894 creator A5000849812 @default.
• W4387569894 creator A5016435979 @default.
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• W4387569894 date "2023-10-12" @default.
• W4387569894 modified "2023-10-14" @default.
• W4387569894 title "Precise identification of cell states altered in disease using healthy single-cell references" @default.
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• W4387569894 doi "https://doi.org/10.1038/s41588-023-01523-7" @default.
• W4387569894 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37828140" @default.
• W4387569894 hasPublicationYear "2023" @default.
• W4387569894 type Work @default.

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