@article{1426, author = {Jian Zhou and Olga Troyanskaya}, title = {An analytical framework for interpretable and generalizable single-cell data analysis.}, 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 {\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.

}, year = {2021}, journal = {Nature methods}, volume = {18}, pages = {1317-1321}, month = {11/2021}, issn = {1548-7105}, doi = {10.1038/s41592-021-01286-1}, language = {eng}, }