SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data.

Abstract

The ability to spatially measure multi-modal data provides an unprecedented opportunity to comprehensively explore molecular regulation at transcriptional, translational and metabolic levels to acquire insights on cellular activities underpinning health and disease. However, there is currently a lack of analytical tools to integrate complementary information across different spatial-omics data modalities, particularly with respect to spatial metabolomics data, which is becoming increasingly invaluable. We introduce SpaMTP, a versatile software that implements an end-to-end integrative analysis of spatial metabolomics and transcriptomics data. Based in R, SpaMTP bridges processing functionalities for metabolomics data from Cardinal with user-friendly cell-centric analyses implemented in Seurat. Furthermore, SpaMTPs comprehensive analysis pipeline covers (1) automated mass-to-charge ratio (m/z) metabolite annotation; (2) a wide range of metabolite-gene based downstream statistical analyses including differential expression, pathway analysis, and correlation analysis; (3) integrative spatial-omics analysis; and (4) a suite of visualisation functions. For flexibility and interoperability, SpaMTP includes various functions for data import/export and object conversion, enabling seamless integration with other R and Python packages. We demonstrated the utility of SpaMTP to draw new biological understandings through analysing two biological system. We believe this software and implemented methods will be broadly utilised in spatial multi-omics and spatial metabolomics analyses.