Apr 29, 2020

Deep feature extraction of single-cell transcriptomes by generative adversarial network

BioRxiv : the Preprint Server for Biology
M. BahramiYue Li

Abstract

Single-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition 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 design. We present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.

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Mentioned in this Paper

Study
Nuclear Import
Interphase
Transcription, Genetic
Sample Fixation
SOX2
Cell Cycle
Drug Interactions
Mitotic Chromosome
DNA Binding

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