scSketch: Interactive Sketch-based Trajectory Exploration and Pathway-Aware Analysis of Single-Cell Data.

Area of Science:

• Computational Biology
• Single-cell Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis relies on exploring gene expression gradients within low-dimensional cell embeddings.
• Existing tools lack the capability for users to sketch trajectories and obtain interactive, pathway-level interpretations.

Purpose of the Study:

• To introduce scSketch, a novel tool for iterative exploration and testing of trajectory hypotheses in single-cell data.
• To enable users to sketch trajectories and probe continuous biological processes like cell differentiation.
• To maintain statistical validity and biological interpretability during interactive data exploration.

Main Methods:

• Users employ interactive directional sketching to draw trajectories on cell embeddings.
• scSketch automatically calculates gene-trajectory correlations.
• Online false discovery rate (FDR) control is applied for statistical validity during iterative analysis.
• Significant genes are annotated with Reactome pathways for biological context.

Main Results:

• scSketch facilitates hypothesis testing and exploration of dynamic cellular processes.
• Application to human cytomegalovirus-infected oral keratinocytes identified infection-associated gene expression gradients.
• Revealed gradients involved interferon responses, metabolic remodeling, and autophagy.

Conclusions:

• scSketch bridges exploratory visualization and mechanistic insights in single-cell biology.
• The tool supports hypothesis-driven exploration of complex biological systems.
• scSketch enhances the interpretability of scRNA-seq data by integrating trajectory inference with pathway analysis.