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Deep feature extraction of single-cell transcriptomes by generative adversarial network
, Article Bioinformatics ; Volume 37, Issue 10 , 2021 , Pages 1345-1351 ; 13674803 (ISSN) ; Maitra, M ; Nagy, C ; Turecki, G ; Rabiee, H. R ; Li, Y ; Sharif University of Technology
Oxford University Press
2021
Abstract
Motivation: Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs. Results: Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding....