RT Journal
A1 Li, Dongshunyi
A1 Ding, Jun
A1 Bar-Joseph, Ziv
T1 Unsupervised cell functional annotation for single-cell RNA-seq
JF Genome Research 
JO Genome Research 
YR 2022 
FD September 01 
VO 32 
IS 9 
SP 1765 
OP 1775 
DO 10.1101/gr.276609.122 
UL http://genome.cshlp.org/content/32/9/1765.abstract 
AB 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.