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An analytical framework for interpretable and generalizable single-cell data analysis

Author(s): Zhou, Jian; Troyanskaya, Olga G

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Abstract: The scaling of single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. Here, we have developed a ‘linearly interpretable’ framework that combines the interpretability and transferability of linear methods with the representational power of non-linear methods. Within this framework we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory and surface estimation and enables their confidence set inference.
Publication Date: 1-May-2022
DOI: 10.1038/s41592-021-01286-1
ISSN: 1548-7105
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Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Nature Methods
Version: Author's manuscript

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