Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

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,

SUBMITTER: Flores M 

PROVIDER: S-EPMC8769926 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature