Contrastive self-supervised clustering of scRNA-seq data

Contrastive self-supervised clustering of scRNA-seq data

Background: Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq...

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Bibliographic Details
Main Authors: Ciortan, M. (Author), Defrance, M. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Cells
cluster analysis
Cluster analysis
Cluster Analysis
Clustering
Clustering algorithms
Clustering techniques
Computationally efficient
Contrastive learning
Cytology
Deep learning
Embeddings
Experimental analysis
Image processing
Learning systems
Network architecture
Neural networks
Optimization
RNA, Small Cytoplasmic
sc-RNA seq
Self-supervised representation learning
sequence analysis
Sequence Analysis, RNA
Single cell
single cell analysis
Single-Cell Analysis
small cytoplasmic RNA
State-of-the-art methods
State-of-the-art techniques
Supervised clustering
Transcriptional activity
Unsupervised learning
Unsupervised learning method
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