Article

Recovering Single-cell Heterogeneity Through Information-based Dimensionality Reduction

2021-01-20

Abstract excerpt

Dimensionality reduction is crucial to summarizing the complex transcriptomic landscape of single cell datasets for downstream analyses. However, current dimensionality reduction approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly-defined populations. Here, we present surprisal component analysis (SCA), a technique that leverages the information-theoretic no...

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Literature Corpus work: f3775674-e356-5cee-83a7-51f97c13a857
DOI: 10.1101/2021.01.19.427303

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