The specious art of single-cell genomics.

Autor: Chari T; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America., Pachter L; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America.; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Aug 17; Vol. 19 (8), pp. e1011288. Date of Electronic Publication: 2023 Aug 17 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011288
Abstrakt: Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Chari, Pachter. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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