Clustering and classification methods for single-cell RNA-sequencing data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Measuring similarity in single-cell RNA sequencing (scRNA-seq) data is crucial but challenging with traditional methods.
• Existing clustering and classification techniques often struggle with the complexities of scRNA-seq data.
• The need for automated and integrated solutions has driven the development of specialized bioinformatics tools.

Purpose of the Study:

• To systematically review integrated methods and tools for scRNA-seq data analysis.
• To evaluate the advantages and disadvantages of various clustering and classification approaches for scRNA-seq.
• To provide a comprehensive overview of current scRNA-seq analysis strategies, including emerging techniques.

Main Methods:

• Systematic literature review of integrated bioinformatics tools for scRNA-seq.
• Comparative analysis of clustering and classification methods applied to scRNA-seq data.
• Discussion of linear, nonlinear, and dimensionality reduction techniques relevant to scRNA-seq.

Main Results:

• Identified and categorized various integrated methods and tools for scRNA-seq data processing.
• Detailed pros and cons of different clustering and classification strategies.
• Highlighted emerging powerful alternatives to traditional methods for scRNA-seq analysis.

Conclusions:

• Integrated methods offer significant advantages for addressing specific challenges in scRNA-seq data analysis.
• A thorough understanding of method-specific strengths and weaknesses is essential for optimal scRNA-seq study design.
• The review provides valuable resources, including descriptions and download URLs, for researchers utilizing scRNA-seq data.