Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
IF: 13.994
Cited by: 10


Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.


Gene Expression
batch correction
cell-type identificationfunctional prediction
deep learning
dimensionality reduction
single-cell RNA-seq

MeSH terms

Cluster Analysis
Deep Learning
Gene Expression Profiling
Machine Learning
Sequence Analysis, RNA
Single-Cell Analysis


Flores, Mario
Liu, Zhentao
Zhang, Tinghe
Hasib, Md Musaddaqui
Chiu, Yu-Chiao
Ye, Zhenqing
Paniagua, Karla
Jo, Sumin
Zhang, Jianqiu
Gao, Shou-Jiang
Jin, Yu-Fang
Chen, Yidong
Huang, Yufei

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