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The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense.

ABSTRACT: A recent paper in PLOS Computational Biology (Chari and Pachter, 2023) claimed that t-SNE and UMAP embeddings of single-cell datasets fail to capture true biological structure. The authors argued that such embeddings are as arbitrary and as misleading as forcing the data into an elephant shape. Here we show that this conclusion was based on inadequate and limited metrics of embedding quality. More appropriate metrics quantifying neighborhood and class preservation reveal the elephant in the room: while t-SNE and UMAP embeddings of single-cell data do not represent high-dimensional distances, they can nevertheless provide biologically relevant information.

SUBMITTER: Lause J

PROVIDER: S-EPMC10996625 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense.

Lause Jan J Kobak Dmitry D Berens Philipp P

bioRxiv : the preprint server for biology 20240731

A recent paper in <i>PLOS Computational Biology</i> (Chari and Pachter, 2023) claimed that <i>t</i> -SNE and UMAP embeddings of single-cell datasets fail to capture true biological structure. The authors argued that such embeddings are as arbitrary and as misleading as forcing the data into an elephant shape. Here we show that this conclusion was based on inadequate and limited metrics of embedding quality. More appropriate metrics quantifying neighborhood and class preservation reveal the eleph ...[more]

PMID: 38585748

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