Single-cell multi-omics integration

What problem does it solves? Integrating multiple single-cell omics datasets (e.g., RNA-seq, ATAC-seq) to gain a holistic view of cellular states and trajectories is complex, requiring specialized algorithms for alignment, batch correction, and trajectory inference. This Skill provides a comprehensive guide to these advanced integration workflows.

Core Features & Use Cases

• Paired Multi-omics Integration: Combine scRNA-seq and scATAC-seq data using MOFA and GLUE for a unified view.
• Batch Integration: Harmonize datasets from multiple studies with SIMBA to remove technical variations.
• Reference Transfer & Trajectory Inference: Transfer cell type labels using TOSICA and infer developmental trajectories with StaVIA.
• Use Case: Combine scRNA-seq and scATAC-seq data from a developing tissue, use MOFA to identify shared latent factors, then infer developmental trajectories with StaVIA, and transfer cell type labels to new datasets using TOSICA.

Quick Start

Integrate my paired scRNA-seq and scATAC-seq data using MOFA, then visualize the factor correlations with cell types.