Analysis of single-cell RNA sequencing data based on autoencoders.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches.

Results

Autoencoders (AEs) have been effectively used to capture the non-linearities among

SUBMITTER: Tangherloni A 

PROVIDER: S-EPMC8186186 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature