Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Area of Science:

• Biomedical Research
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) has revolutionized disease pathogenesis understanding and identified new diagnostic/therapeutic targets.
• Increasing high-throughput scRNA-seq data, especially from clinical samples, presents significant analysis challenges for researchers.

Purpose of the Study:

• To provide a comprehensive review of typical scRNA-seq data analysis workflows.
• To offer practical guidance and resources for researchers, particularly those new to the field and clinical applications.

Main Methods:

• Review of scRNA-seq data analysis pipeline: raw data processing, quality control, basic analysis, and advanced analysis.
• Summarization of current methodologies for each analysis stage.
• Provision of an online repository with software and scripts for implementation support.

Main Results:

• Detailed overview of scRNA-seq data analysis steps, from initial processing to complex analyses tailored for specific research questions.
• Identification of recommendations and potential challenges for various analysis tasks and approaches.
• A curated collection of computational tools and scripts to facilitate scRNA-seq data analysis.

Conclusions:

• This review serves as a valuable resource for researchers navigating scRNA-seq data analysis, especially in emerging clinical contexts.
• The provided tools and guidance aim to demystify complex data analysis, promoting wider adoption and application of scRNA-seq in biomedical research.