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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1317-1321
Number of pages5
JournalNature Methods
Volume18
Issue number11
DOIs
StatePublished - Nov 2021

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Fingerprint

Dive into the research topics of 'An analytical framework for interpretable and generalizable single-cell data analysis'. Together they form a unique fingerprint.

Cite this