Dongshunyi Li1, Jun Ding2 and Ziv Bar-Joseph1,3 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA; 2Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada; 3Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA Corresponding author: zivbjcs.cmu.edu 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.

Received January 17, 2022. Accepted June 10, 2022.