Inferring functionally relevant molecular tissue substructures by agglomerative clustering of digitized spatial transcriptomes

Abstract excerpt

<h4>Summary</h4> Developments on spatial transcriptomics (ST) are providing means to interrogate organ/tissue architecture from the angle of the gene programs defining their molecular complexity. However, computational methods to analyze ST data under-exploits the spatial signature retrieved within the maps. Inspired by contextual pixel classification strategies applied to image analysis, we have developed MULTIL...

Topics

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• Algorithms
• Cancer Cells and Metastasis
• Cancer Genomics and Diagnostics
• Cluster Analysis
• Neural Networks, Computer
• Phenotype
• Radiomics and Machine Learning in Medical Imaging
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

107e693b-32cf-53ad-84c2-e505f7474664

DOI

10.1101/2020.11.09.374660