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- W4213270761 abstract "Abstract 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." @default.
- W4213270761 created "2022-02-24" @default.
- W4213270761 creator A5002768368 @default.
- W4213270761 creator A5008638025 @default.
- W4213270761 creator A5024265492 @default.
- W4213270761 creator A5028190064 @default.
- W4213270761 creator A5041942814 @default.
- W4213270761 creator A5042784238 @default.
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- W4213270761 creator A5043640443 @default.
- W4213270761 creator A5046855484 @default.
- W4213270761 creator A5057799604 @default.
- W4213270761 creator A5068887242 @default.
- W4213270761 creator A5073343116 @default.
- W4213270761 creator A5087249630 @default.
- W4213270761 date "2021-12-21" @default.
- W4213270761 modified "2023-10-17" @default.
- W4213270761 title "Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis" @default.
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