A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) offers unparalleled cellular resolution compared to bulk RNA sequencing.
• scRNA-seq data presents unique challenges, including increased technical noise and complexity, necessitating specialized analytical approaches.
• Existing bulk RNA sequencing bioinformatics pipelines are not suitable for scRNA-seq data analysis.

Purpose of the Study:

• To describe a computational workflow for the low-level analysis of scRNA-seq data.
• To provide a guide for researchers to construct their own scRNA-seq analysis pipelines.
• To demonstrate the application of open-source Bioconductor software packages for scRNA-seq data processing.

Main Methods:

• Utilized open-source Bioconductor software packages for scRNA-seq data analysis.
• Implemented quality control, data exploration, and normalization procedures.
• Applied advanced methods including cell cycle phase assignment, identification of highly variable and correlated genes, clustering, and marker gene detection.

Main Results:

• Demonstrated a comprehensive computational workflow for scRNA-seq data analysis.
• Successfully applied the workflow to diverse datasets, including haematopoietic stem cells, brain-derived cells, T-helper cells, and mouse embryonic stem cells.
• Provided practical examples for constructing custom scRNA-seq analysis pipelines.

Conclusions:

• The described workflow effectively addresses the complexities of scRNA-seq data.
• Open-source Bioconductor tools provide a robust framework for scRNA-seq data analysis.
• The workflow enables detailed exploration of cellular heterogeneity and gene expression profiles.