Vitessce: integrative visualization of multimodal and spatially resolved single-cell data

Area of Science:

• Single-cell biology
• Bioinformatics
• Computational biology

Background:

• Multi-omics technologies provide high-resolution data from millions of cells.
• Analyzing high-dimensional single-cell data (transcriptomic, proteomic, genomic, imaging) simultaneously is challenging.
• Current visualization methods struggle with the complexity and scale of multimodal single-cell data.

Purpose of the Study:

• To introduce Vitessce, an interactive web-based visualization framework.
• To enable simultaneous exploration of multimodal and spatially resolved single-cell data.
• To facilitate the visual analysis of millions of data points across diverse data types.

Main Methods:

• Development of an open-source, interactive web-based visualization framework.
• Integration of multiple coordinated views for data exploration.
• Demonstration of integrative visualization for cell-type annotations, gene expression, spatial transcripts, and cell segmentations.

Main Results:

• Vitessce allows for the integrative visualization of millions of data points from multimodal single-cell experiments.
• The framework supports simultaneous exploration of transcriptomic, proteomic, genomic, and imaging data.
• Users can explore cell-type annotations, gene expression, spatially resolved transcripts, and cell segmentations within coordinated views.

Conclusions:

• Vitessce addresses the challenge of visualizing high-dimensional, multimodal single-cell data.
• The framework enhances the exploration of complex biological datasets with single-cell and spatial resolution.
• Vitessce is an open-source tool available for broader scientific adoption and use.