InSituPy: a framework for histology-guided, multi-sample analysis of single-cell spatial omics data.

Area of Science:

• Computational Biology
• Bioinformatics
• Pathology

Background:

• Single-cell spatial omics data offers deep insights into disease mechanisms.
• Analyzing diverse spatial omics data modalities and multiple datasets requires integrated frameworks.
• Current analytical approaches need enhancement for comprehensive multi-sample spatial omics data processing.

Purpose of the Study:

• Introduce InSituPy, a scalable framework for analyzing spatial omics data.
• Facilitate joint processing of multiple datasets and associated metadata.
• Enable integrated analysis from multi-sample to cellular and subcellular levels.

Main Methods:

• Developed a versatile and scalable Python framework, InSituPy.
• Implemented a modular data structure for organizing diverse data modalities and metadata per sample.
• Integrated interactive visualization tools and built-in analytical algorithms.
• Provided interfaces for external tools to establish standardized workflows.

Main Results:

• InSituPy enables scalable analysis of large patient cohorts using spatial omics technologies.
• The framework supports joint processing of multiple datasets and corresponding metadata.
• Interactive visualization tools facilitate the integration of histopathological expertise.
• Established a standardized workflow for multi-sample spatial omics data analysis.

Conclusions:

• InSituPy addresses the challenges of analyzing complex, multi-modal spatial omics data.
• The framework promotes collaborative hypothesis generation in translational research.
• InSituPy provides a standardized and scalable solution for spatial omics data analysis.