The specious art of single-cell genomics

By T. Chari, Lior S. Pachter. bioRxiv, 2021

339 citations recorded by the source corpus.

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 two or three 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 two, 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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6f58a16a357d8fea18a987176e98e6d72e01741f

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Citations in the corpus

Best practices for single-cell analysis across modalities (2023, methodology): “Independent benchmarks 5,50,51 showed that community detection based on graph modularity optimization via the Louvain algorithm works best for cluster identification.”
Dtour: a Steerable Tour De Vis through High-dimensional Data (2026, unknown): “Despite many debates about the usefulness and faithfulness of non-linear DR methods (e.g. [10, 19]), such methods are widely used and undoubtedly useful if interpreted with care [44, 17, 3].”