Title | An analytical framework for interpretable and generalizable single-cell data analysis. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Zhou, J, Troyanskaya, OG |
Journal | Nat Methods |
Volume | 18 |
Issue | 11 |
Pagination | 1317-1321 |
Date Published | 2021 Nov |
ISSN | 1548-7105 |
Keywords | Algorithms, 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> |
DOI | 10.1038/s41592-021-01286-1 |
Alternate Journal | Nat Methods |
PubMed ID | 34725480 |
PubMed Central ID | PMC8959118 |
Grant List | HHSN272201000054C / 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 |