BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Cell-to-cell gene expression variability is crucial in biological systems like immunity and development.
• Single-cell RNA sequencing (scRNA-seq) quantifies this heterogeneity but suffers from technical noise.

Purpose of the Study:

• To present a computational workflow using the BASiCS Bioconductor package for robustly quantifying gene expression variability in scRNA-seq data.
• To enable identification of cell heterogeneity within and between cell populations while accounting for technical noise.

Main Methods:

• Utilized the BASiCS Bioconductor package for integrated data normalization, technical noise quantification, and downstream analysis.
• Employed a probabilistic decision rule to identify changes in expression variability between cell populations.
• Integrated quality control and data exploration using scater and scran Bioconductor packages.

Main Results:

• BASiCS effectively quantifies expression variability within and between cell groups, distinguishing highly and lowly variable genes.
• The workflow successfully identified changes in expression variability between cell populations, robust against technical noise and abundance differences.
• Demonstrated a complete pipeline using a public scRNA-seq dataset.

Conclusions:

• The BASiCS workflow provides a robust framework for analyzing gene expression variability in scRNA-seq data.
• This approach enhances the understanding of cellular heterogeneity in complex biological systems.
• Ensured reproducibility through a Docker image for the computational pipeline.