Open AccessAn analytical framework for interpretable and generalizable single-cell data analysis
Author(s) -
Jian Zhou,
Olga G. Troyanskaya
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-021-01286-1
Subject(s) - interpretability , computer science , inference , data mining , representation (politics) , visualization , exploratory data analysis , data set , set (abstract data type) , artificial intelligence , machine learning , external data representation , transferability , logit , politics , political science , law , programming language
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.