Abstract
In this work we present a pipeline (DeepTME) that integrates single cells transcription, spatial location of cells, cell type composition, and tissue morphology, using deep learning. DeepTME classifies the tissue microenvironments (TME) at single cell resolution by learning low-dimensional features from all transcription profiles, images similarity, neighboring information, and cell type composition, The proposed approach is able to capture important TME features than can used to quantify disease progression and severity and ultimately identify the molecular components of disease progression and obtain insight into their mechanisms. National Institutes of Health. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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