bio-single-cell-multimodal-integration

What problem does it solve?

Multimodal single-cell experiments measure several cellular layers (RNA, protein, chromatin), and integrating these modalities into a cohesive analysis is error-prone and time-consuming. This skill provides a unified framework to load, normalize, and jointly analyze multi-omics data, enabling consistent cell-type discovery and cross-modality interpretation.

Core Features & Use Cases

• Multimodal integration: combine RNA with protein (ADT) or RNA with ATAC to build a joint representation.
• Cross-platform workflows: supports Seurat-based (R) and MuData/Scanpy-based (Python) pipelines for end-to-end analysis.
• Visualization & interpretation: generate joint UMAPs, modality weights, and cross-modality marker discovery for robust cell-type annotation.
• Use Case: analyze CITE-seq or 10X Multiome datasets to identify common cell states across modalities and compare modality contributions.

Quick Start

Load your multimodal dataset (RNA + ADT or RNA + ATAC), run a WNN-based integration, and generate a joint clustering and visualization to start analyzing cell states.