ScRNAPip: A systematic and dynamic pipeline for single-cell RNA sequencing analysis

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell sequencing technology offers enhanced accuracy for gene expression measurement compared to traditional multi-cellular methods.
• It enables the detection of low-abundance gene expression and rare noncoding RNA, driving research interest.
• Advancements in sequencing, cell separation, and amplification techniques facilitate single-cell genome sequencing.

Purpose of the Study:

• To develop a reproducible computational workflow for single-cell RNA sequencing (scRNA-seq) data analysis.
• To provide a comprehensive tutorial for researchers on constructing and refining scRNA-seq analysis pipelines.
• To illustrate the application of analysis steps using publicly available datasets.

Main Methods:

• Quality control
• Normalization
• Data correction
• Pseudotime analysis
• Copy number analysis

Main Results:

• A reproducible computational workflow for scRNA-seq data analysis was developed.
• The workflow includes essential steps from quality control to advanced analyses like pseudotime and copy number analysis.
• Practical recommendations for implementing these analysis steps were provided.

Conclusions:

• The developed workflow serves as a valuable tutorial for researchers in single-cell data analysis.
• It aids users in building and optimizing their own scRNA-seq analysis pipelines.
• This resource facilitates more accurate and comprehensive single-cell gene expression studies.