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

TitleAn analytical framework for interpretable and generalizable single-cell data analysis.
Publication TypeJournal Article
Year of Publication2021
AuthorsZhou, J, Troyanskaya, OG
JournalNat Methods
Volume18
Issue11
Pagination1317-1321
Date Published2021/11/18
ISSN1548-7105
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.

DOI10.1038/s41592-021-01286-1
Alternate JournalNat Methods
PubMed ID34725480
Grant ListR01HG005998 / / U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) /
U54HL117798 / / U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) /
R01GM071966 / / U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) /
HHSN272201000054C / AI / NIAID NIH HHS / United States
395506 / / Simons Foundation /
RR190071 / / Cancer Prevention and Research Institute of Texas (Cancer Prevention Research Institute of Texas) /
HHSN272201000054C / AI / NIAID NIH HHS / United States