Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 05 2020
11 05 2020
Historique:
received:
17
11
2019
accepted:
20
03
2020
entrez:
13
5
2020
pubmed:
13
5
2020
medline:
6
8
2020
Statut:
epublish
Résumé
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity.
Identifiants
pubmed: 32393754
doi: 10.1038/s41467-020-15851-3
pii: 10.1038/s41467-020-15851-3
pmc: PMC7214470
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2338Subventions
Organisme : NEI NIH HHS
ID : R01 EY030192
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY031209
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM108600
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM125301
Pays : United States
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