Contrastive self|supervised clustering of scRNA|seq data

Contrastive self-supervised clustering of scRNA-seq data

Results. We propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of ...

Contrastive self-supervised clustering of scRNA-seq data - PubMed

On average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally ...

scAMAC: self-supervised clustering of scRNA-seq data based on ...

Therefore, deep learning-based clustering methods, broadly categorized into those based on autoencoders, graph neural networks and contrastive ...

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive ...

scDHA (Tran et al., 2021) exploits a stacked Bayesian self-learning network to learn compact and generalized representations of scRNA-seq data.

Self-supervised contrastive learning for integrative single cell RNA ...

CLEAR achieves superior performance on a broad range of fundamental tasks for scRNA-seq data analysis, including clustering, visualization, ...

ScCCL: Single-Cell Data Clustering Based on Self-Supervised ...

We propose ScCCL, a novel self-supervised contrastive learning method for clustering of scRNA-seq data.

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive ...

It also utilizes a dual self-supervised module to cluster cells and guide the training process. Furthermore, Some other works have tried to ...

Predicting cell types with supervised contrastive learning on ... - Nature

The pancreas dataset is a human pancreatic islet scRNA-seq data from 6 sequencing ... Contrastive self-supervised clustering of scRNA-seq data.

(PDF) Contrastive self-supervised clustering of scRNA-seq data

Results We propose contrastive-sc , a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of ...

Self-supervised contrastive learning for integrative single cell RNA ...

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis ... clustering, visualization, dropout correction ...

Contrastive self-supervised clustering of scRNA-seq data - OUCI

Abstract Background Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level.

Self-supervised contrastive learning for integrative single cell RNA ...

Consensus clustering of single-cell RNA-seq data by enhancing network affinity · Biology, Computer Science. Briefings Bioinform. · 2021.

Self-supervised contrastive learning for integrative single cell RNA ...

Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data. Nat Commun. 2021; 12: 1873. 6. Stegle O ...

Integrating large-scale single-cell RNA sequencing in central ...

In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data.

Peer Review reports - BMC Bioinformatics - BioMed Central

Clustering analysis is routinely performed on scRNA-seq data to ... Peer Review reports. From: Contrastive self-supervised clustering of scRNA-seq data ...

Deep single-cell RNA-seq data clustering with graph prototypical ...

and Defrance, M. Contrastive self-supervised clustering of scrna-seq data. BMC bioinformatics, 22(1):. 1–27, 2021.

Graph Contrastive Learning as a Versatile Foundation for Advanced ...

Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles.

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive ...

A novel method called Cluster-aware Iterative Contrastive Learning (CICL) for scRNA-seq data clustering is proposed, which utilizes an iterative ...

Self-supervised contrastive learning for integrative single cell RNA ...

PDF | We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation.

Deep enhanced constraint clustering based on contrastive learning ...

Abstract Single-cell RNA sequencing (scRNA-seq) measures transcriptome-wide gene expression at single-cell resolution. Clustering analysis of scRNA-seq data ...