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

Nat Methods. 2021 Nov;18(11):1317-1321. doi: 10.1038/s41592-021-01286-1. Epub 2021 Nov 1.

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.