@article{4297,
  keywords = {Animals, Sequence Analysis, RNA, Humans, Computational Biology, Algorithms, Single-Cell Analysis, Software, Datasets as Topic, Computer Graphics, RNA-Seq},
  author = {Jian Zhou and Olga Troyanskaya},
  title = {An analytical framework for interpretable and generalizable single-cell data analysis.},
  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 {\textquoteright}linearly interpretable{\textquoteright} 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> },
  year = {2021},
  journal = {Nat Methods},
  volume = {18},
  pages = {1317-1321},
  month = {2021 Nov},
  issn = {1548-7105},
  doi = {10.1038/s41592-021-01286-1},
  language = {eng},
}