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An analytical framework for interpretable and generalizable single-cell data analysis
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DOI：10.1038/s41592-021-01286-1.png)
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Abstract
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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. A linearly interpretable framework for analyzing single-cell omics data improves data representation, visualization and structure discovery.
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