CoCo-ST detects global and local biological structures in spatial transcriptomics datasets v1
Year: 2025
Type: preprint
Abstract: A protocol describing the application of the CoCo-ST algorithm on spatial transcriptomics datasets to detect biologically relevant spatial structures. We demonstrate CoCo-ST's ability to detect low variance spatial structures driving tumor progression. Two main challenges with precancer analysis are: The high variance in global spatial patterns which might lead to the overlook of local, low-variance domains that hold significant biological relevance. The complexity of spatial domain detection across diverse tissue samples with varying spatial resolutions, necessitating a robust and scalable approach. Here, we show CoCo-ST’s contrastive learning approach for identifying both high and low-variance spatial structures, leveraging multi-sample data. This protocol is associated with our proposed CoCo-ST algorithm for spatial domain detection in Nature Cell Biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Awards: Dissecting mechanisms of resistance to immune checkpoint blockade in NSCLC through the interplay of molecular and spatial architecture, QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCER, Identify radiopathomics markers to guide immunotherapy for non-small cell lung cancer, G9000090962, Radioimmunogenomic Habitat Phenotypes to Predict Efficacy of Neoadjuvant Immunotherapies in Non-Small Cell Lung Cancer
OpenAlex ID: W4412527951