Artikel ; Online: Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve.
2023 Band 14, Heft 1, Seite(n) 1350
Abstract: We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD ...
Abstract | We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context. |
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Mesh-Begriff(e) | Transcriptome ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Gene Expression Profiling/methods ; RNA-Seq/methods |
Sprache | Englisch |
Erscheinungsdatum | 2023-03-11 |
Erscheinungsland | England |
Dokumenttyp | Journal Article |
ZDB-ID | 2553671-0 |
ISSN | 2041-1723 ; 2041-1723 |
ISSN (online) | 2041-1723 |
ISSN | 2041-1723 |
DOI | 10.1038/s41467-023-36961-8 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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