@article{Li01092022,
author = {Li, Dongshunyi and Ding, Jun and Bar-Joseph, Ziv}, 
title = {Unsupervised cell functional annotation for single-cell RNA-seq},
volume = {32}, 
number = {9}, 
pages = {1765-1775}, 
year = {2022}, 
doi = {10.1101/gr.276609.122}, 
abstract ={One of the first steps in the analysis of single-cell RNA sequencing (scRNA-seq) data is the assignment of cell types. Although a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments, we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both low-dimensional representation for all genes and cell-specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-seq data sets from several different organs. We show, by using knowledge about gene sets, that UNIFAN greatly outperforms prior methods developed for clustering scRNA-seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster, making annotations easier.}, 
URL = {http://genome.cshlp.org/content/32/9/1765.abstract}, 
eprint = {http://genome.cshlp.org/content/32/9/1765.full.pdf+html}, 
journal = {Genome Research} 
}