SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data. DOI Open Access
Andrew Causer, T.C. Lu, Christopher C. J. Fitzgerald

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 3, 2024

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

Language: Английский

SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data. DOI Open Access
Andrew Causer, T.C. Lu, Christopher C. J. Fitzgerald

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 3, 2024

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

Language: Английский

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