scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
• Existing downstream analysis tools for scRNA-seq data are often disparate, hindering integrated studies of cell differentiation and communication.

Purpose of the Study:

• To develop an automated pipeline for integrated downstream analysis of scRNA-seq data.
• To facilitate the study of cell differentiation and communication, particularly in rare diseases.

Main Methods:

• Development of the R package "scDown" for automated scRNA-seq downstream analysis.
• Integration of cell proportion difference, cell-cell communication, pseudotime, and RNA velocity analyses.
• Compatibility with scRNA-seq data annotated by both Seurat and Scanpy.

Main Results:

• Application of scDown to a published dataset identified a novel neuronal inflammatory signaling signature.
• This signature was associated with a rare genetic neurodevelopmental disorder.
• The identified signature was not detected using standard differential gene expression analysis alone.

Conclusions:

• The scDown pipeline provides a valuable tool for comprehensive scRNA-seq data analysis.
• scDown facilitates biological discovery, especially in the context of rare diseases.
• The pipeline enhances the ability to uncover complex biological mechanisms from scRNA-seq data.