BulkTrajBlend trajectory interpolation

What problem does it solves? Single-cell RNA-seq developmental trajectories often have gaps or missing intermediate cell states, limiting a complete understanding of cellular transitions. This Skill uses BulkTrajBlend to leverage bulk RNA-seq data to interpolate these missing states, providing a more continuous view of development.

Core Features & Use Cases

• Synthetic Cell Generation: Deconvolute bulk RNA-seq samples to generate synthetic intermediate single cells.
• GNN-Based Community Detection: Train Graph Neural Network (GNN) models to identify overlapping communities within the integrated data.
• Trajectory Interpolation: Synthesize continuity and interpolate missing cell states to create smooth developmental trajectories.
• Trajectory Analysis: Analyze and validate interpolated trajectories using VIA and PAGA for topological insights.
• Use Case: Enhance a sparse single-cell trajectory of brain development by integrating matched bulk RNA-seq, generating intermediate cell types, and then re-analyzing the trajectory with VIA and PAGA to reveal finer transitions.

Quick Start

Use BulkTrajBlend to interpolate missing OPC states in my scRNA-seq trajectory using bulk RNA-seq data, then visualize the new trajectory with PAGA.