scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data

Abstract Single-cell RNA sequencing (scRNA-seq) allows quantitative analysis of gene expression at the level of single cells, beneficial to study cell heterogeneity. The recognition of cell types facilitates the construction of cell atlas in complex tissues or organisms, which is the basis of almost...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Briefings in bioinformatics Jg. 23; H. 1
Hauptverfasser:	Yin, Qingyang, Wang, Yang, Guan, Jinting, Ji, Guoli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 17.01.2022 Oxford Publishing Limited (England)
Schlagworte:	Annotations Classification Data Analysis Feature extraction Gene expression Gene Expression Profiling Gene sequencing Heterogeneity Medical treatment Predictions Representations Ribonucleic acid RNA RNA-Seq Sequence Analysis, RNA Single-Cell Analysis - methods Whole Exome Sequencing ensemble learning scRNA-seq data feature extraction integrative autoencoder classification framework
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!