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

Author

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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
    Icahn Genomics Institute
    Black Family Stem Cell Institute)

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.

Suggested Citation

  • Daniel Charytonowicz & Rachel Brody & Robert Sebra, 2023. "Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36961-8
    DOI: 10.1038/s41467-023-36961-8
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    References listed on IDEAS

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    1. Francisco Avila Cobos & José Alquicira-Hernandez & Joseph E. Powell & Pieter Mestdagh & Katleen De Preter, 2020. "Benchmarking of cell type deconvolution pipelines for transcriptomics data," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    2. Jiarui Ding & Aviv Regev, 2021. "Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    3. Francesco Vallania & Andrew Tam & Shane Lofgren & Steven Schaffert & Tej D. Azad & Erika Bongen & Winston Haynes & Meia Alsup & Michael Alonso & Mark Davis & Edgar Engleman & Purvesh Khatri, 2018. "Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    4. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    5. Francisco Avila Cobos & José Alquicira-Hernandez & Joseph E. Powell & Pieter Mestdagh & Katleen Preter, 2020. "Author Correction: Benchmarking of cell type deconvolution pipelines for transcriptomics data," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    6. Vijay Saxena & Hongyu Gao & Samuel Arregui & Amy Zollman & Malgorzata Maria Kamocka & Xiaoling Xuei & Patrick McGuire & Michael Hutchens & Takashi Hato & David S. Hains & Andrew L. Schwaderer, 2021. "Kidney intercalated cells are phagocytic and acidify internalized uropathogenic Escherichia coli," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
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