Machine learning approaches for biomarker discovery using single-cell RNA sequencing.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity for biomarker discovery.
• Traditional methods like differential gene expression analysis are commonly used but have limitations.
• The rise of artificial intelligence and machine learning is transforming biomarker discovery in transcriptomics.

Purpose of the Study:

• To provide a comprehensive overview of machine learning (ML) methods applied to scRNA-seq biomarker discovery.
• To detail the methodological diversity in current ML approaches for scRNA-seq data.
• To offer researchers a thorough understanding of the ML landscape for identifying novel biomarkers.

Main Methods:

• Review of existing literature on ML applications in scRNA-seq biomarker discovery.
• Categorization of ML methods based on discovery level, algorithms, feature selection, and metrics.
• Analysis of downstream biological interpretation of ML-identified biomarkers.

Main Results:

• ML-based approaches for scRNA-seq biomarker discovery are diverse and rapidly advancing.
• Key differentiating factors include supervised learning algorithms, feature selection strategies, and classification metrics.
• Methodological variations impact the identification and validation of potential biomarkers.

Conclusions:

• Machine learning presents a powerful and increasingly prominent toolkit for biomarker discovery using scRNA-seq data.
• Understanding the diversity of ML methods is crucial for effective application and interpretation.
• This review serves as a guide to the current state of ML in scRNA-seq biomarker discovery.