Loss-Function Learning for Digital Tissue Deconvolution

Goertler, Franziska and Schoen, Marian and Simeth, Jakob and Solbrig, Stefan and Wettig, Tilo and Oefner, Peter J. and Spang, Rainer and Altenbuchinger, Michael (2020) Loss-Function Learning for Digital Tissue Deconvolution. JOURNAL OF COMPUTATIONAL BIOLOGY, 27 (3). pp. 342-355. ISSN 1066-5277, 1557-8666

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Abstract

The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution addresses the following inverse problem: given the expression profile y of a tissue, what is the cellular composition c of that tissue? If X is a matrix whose columns are reference profiles of individual cell types, the composition c can be computed by minimizing Script capital L(y-Xc) for a given loss function Script capital L. Current methods use predefined all-purpose loss functions. They successfully quantify the dominating cells of a tissue, while often falling short in detecting small cell populations. In this study we use training data to learn the loss function Script capital L along with the composition c. This allows us to adapt to application-specific requirements such as focusing on small cell populations or distinguishing phenotypically similar cell populations. Our method quantifies large cell fractions as accurately as existing methods and significantly improves the detection of small cell populations and the distinction of similar cell types.

Item Type: Article
Uncontrolled Keywords: SINGLE-CELL; EXPRESSION; IMMUNE; ACTIVATION; RESPONSES; CYTOMETRY; VERSICAN; RECEPTOR; GROWTH; cellular composition; digital tissue deconvolution; loss-function learning; machine learning; model adaptation
Subjects: 500 Science > 530 Physics
600 Technology > 610 Medical sciences Medicine
Divisions: Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Physics > Institute of Theroretical Physics
Physics > Institute of Theroretical Physics > Chair Professor Braun > Group Tilo Wettig
Depositing User: Dr. Gernot Deinzer
Date Deposited: 01 Apr 2021 07:59
Last Modified: 01 Apr 2021 07:59
URI: https://pred.uni-regensburg.de/id/eprint/45304

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