Single-Cell RNAseq Clustering.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) generates high-dimensional transcriptomic data from individual cells.
• Analyzing scRNA-seq data is crucial for understanding cellular heterogeneity and tissue organization.
• Unsupervised clustering is a fundamental step in scRNA-seq analysis for cell type identification.

Purpose of the Study:

• To provide a guide on approaching unsupervised clustering for scRNA-seq data.
• To discuss various clustering tools applicable to transcriptomic datasets.
• To highlight the limitations inherent in the scRNA-seq clustering process.

Main Methods:

• Description of methodologies for unsupervised clustering of scRNA-seq data.
• Overview of different available clustering algorithms and software.
• Guidance on applying these tools to transcriptomic datasets.

Main Results:

• Identification of distinct cell populations within complex biological samples.
• Characterization of cell subpopulation organization based on transcriptomic profiles.
• Understanding the performance and constraints of various clustering approaches.

Conclusions:

• Unsupervised clustering is vital for dissecting cellular heterogeneity in scRNA-seq studies.
• Selection of appropriate clustering tools and awareness of limitations are key for accurate cell type identification.
• This chapter serves as a resource for researchers analyzing scRNA-seq transcriptomics data.