Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

Daniel Charytonowicz, Rachel Brody and Robert Sebra ()
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Daniel Charytonowicz: Icahn School of Medicine at Mount Sinai
Rachel Brody: Icahn School of Medicine at Mount Sinai
Robert Sebra: Icahn School of Medicine at Mount Sinai

Nature Communications, 2023, vol. 14, issue 1, 1-20

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36961-8

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