The specious art of single-cell genomics

• Published in: PLoS computational biology Vol. 19; no. 8; p. e1011288
• Main Authors: Chari, Tara, Pachter, Lior
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.08.2023; Public Library of Science (PLoS)
• Subjects: Biological analysis; Biology and Life Sciences; Computer and Information Sciences; Data Analysis; Data mining; Data visualization; Datasets; Embedding; Engineering and Technology; Gene expression; Genetic research; Genomics; Humans; Information management; Methods; Physical Sciences; Qualitative analysis; Research and Analysis Methods; Social Sciences; Stem cells; Thermometers; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011288

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.