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

Mojtaba Bahrami | Zendy; Malosree Maitra | Zendy; Corigy | Zendy; Gustavo Turecki | Zendy; Hamid R. Rabiee | Zendy; Yue Li | Zendy

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

Author(s) -

Mojtaba Bahrami,

Malosree Maitra,

Corigy,

Gustavo Turecki,

Hamid R. Rabiee,

Yue Li

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa976

Subject(s) - computer science , generative model , generative grammar , feature (linguistics) , source code , embedding , generative adversarial network , raw data , code (set theory) , artificial intelligence , data mining , machine learning , deep learning , philosophy , linguistics , set (abstract data type) , programming language , operating system

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.

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