Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates high-throughput data for biological discovery.
• Clustering is vital for cell type identification and understanding cellular heterogeneity in scRNA-seq data.
• Variability in clustering results from different methods can impact analysis accuracy.

Purpose of the Study:

• To review the applications of clustering ensemble methods in single-cell transcriptome data analysis.
• To identify challenges associated with using clustering ensembles for scRNA-seq data.
• To provide insights and references for researchers in this field.

Main Methods:

• Review of existing literature on clustering ensemble applications in scRNA-seq.
• Analysis of challenges and limitations of current clustering ensemble approaches.
• Synthesis of findings to offer constructive recommendations.

Main Results:

• Clustering ensemble methods generally yield more reliable results than individual clustering partitions.
• Ensemble approaches mitigate the instability and variability issues of single clustering methods.
• The review consolidates current knowledge on the utility and challenges of clustering ensembles.

Conclusions:

• Clustering ensembles are a valuable strategy for enhancing the accuracy of single-cell transcriptome data analysis.
• Addressing the identified challenges can further optimize the application of these methods.
• This review serves as a resource for researchers aiming to leverage clustering ensembles in scRNA-seq studies.