A research team led by Prof. LI Hong from Shanghai Institute of Nutrition and Health (SINH) of the Chinese Academy of Sciences developed a new toolkit named “SOAPy” for analyzing spatial omics data. This study was published in Genome Biology on March 29.

Spatial organization and microenvironment are important for understanding the tissues in normal and disease states. Spatial omics especially spatial transcriptomes have become popular in recent years, but extracting useful information from these complex spatial data is still challenging. A toolkit for the integrative analysis of microenvironmental spatial organization is in urgent need.

In this study, Prof. LI’s team developed a Python package named “SOAPy” which integrates a suite of algorithms to investigate gene expression variability and cell type distribution heterogeneity in spatial omics. 

SOAPy provides methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, and cell-cell communication. It can be applied to various spatial omics technologies and multiple areas in physiological and pathological contexts such as tumor biology and development biology. 

In addition to the diverse functions and extensive application scenarios of SOAPy, the newly proposed ligand-receptor communication analysis takes into account the difference between secreted and membrane-binding proteins, which is more biologically interpretable and remarkably reduce false positive rate. 

“We developed this toolkit to dissect the latent biological characteristics from multiple spatial perspectives,” said Prof. LI. “Even users who are not familiar with the spatial omics technology can rapidly conduct in-depth analyses using SOAPy.”

This study presents a valuable tool for spatial omics analysis, facilitating the dissection of microenvironment architecture.