Unsupervised cell functional annotation for single-cell RNA-Seq

Li, D.; Ding, J.; Bar-Joseph, Z.

doi:10.1101/2021.11.20.469410

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bioRxiv · 10.1101/2021.11.20.469410

Unsupervised cell functional annotation for single-cell RNA-Seq

Li, D.·Ding, J.·Bar-Joseph, Z.

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Abstract

One of the first steps in the analysis of single cell RNA-Sequencing data (scRNA-Seq) is the assignment of cell types. While 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 datasets from several different organs. As we show, by using knowledge on gene sets, 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. Softwarehttps://github.com/doraadong/UNIFAN

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Li, D., Ding, J., Bar-Joseph, Z.. 2021-11-21. Unsupervised cell functional annotation for single-cell RNA-Seq. https://doi.org/10.1101/2021.11.20.469410

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