scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network.

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Abstract

Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and more well-annotated scRNA-seq reference data is publicly available, automatical label transference algorithms are gaining popularity over manual marker gene-based annotation methods. However, most existing methods fail to unify cell-type annotation with dimensionality reduction, and are unable to generate deep latent representation from the perspective of data generation.In this article, we propose scSemiGAN, a semi-supervised cell-type annotation and dimensionality reduction framework based on generative adversarial network, to overcome these challenges, modeling scRNA-seq data from the aspect of data generation. Our proposed scSemiGAN is capable of performing deep latent representation learning and cell-type label prediction simultaneously. Through extensive comparison with four state-of-the-art annotation methods on diverse simulated and real scRNA-seq datasets, scSemiGAN achieves competitive or superior performance in multiple downstream tasks including cell-type annotation, latent representation visualization, confounding factor removal and enrichment analysis.The code of scSemiGAN is available on GitHub: https://github.com/rafa-nadal/scSemiGAN.Supplementary data are available at Bioinformatics online.© The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].