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 Nov
ISSN1548-7105
KeywordsAlgorithms, Animals, Computational Biology, Computer Graphics, Datasets as Topic, Humans, RNA-Seq, Sequence Analysis, RNA, Single-Cell Analysis, Software
Abstract

<p>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.</p>

DOI10.1038/s41592-021-01286-1
Alternate JournalNat Methods
PubMed ID34725480
PubMed Central IDPMC8959118
Grant ListHHSN272201000054C / AI / NIAID NIH HHS / United States
R01 GM071966 / GM / NIGMS NIH HHS / United States
R01 HG005998 / HG / NHGRI NIH HHS / United States
U54 HL117798 / HL / NHLBI NIH HHS / United States