Analysis of single-cell RNA sequencing data based on autoencoders

Analysis of single-cell RNA sequencing data based on autoencoders

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 s...

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Bibliographic Details
Main Authors: Besozzi, D. (Author), Cvejic, A. (Author), Liò, P. (Author), Ricciuti, F. (Author), Tangherloni, A. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
article
autoencoder
Autoencoders
Batch correction
Cells
Clustering
Cytology
data integration
Data integration
Digital storage
dimensionality reduction
Dimensionality reduction
gene interaction
Gene interactions
genetics
Learning systems
Loss functions
loss of function mutation
Low-dimensional representation
machine learning
Machine Learning
Machine learning approaches
Molecular process
nonlinear system
pipeline
RNA
Rna sequencing
scRNA-Seq
sequence analysis
Sequence Analysis, RNA
single cell analysis
single cell RNA seq
Single-Cell Analysis
User-defined parameters
User-friendly tool
whole exome sequencing
Whole Exome Sequencing
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