Speaker: Sofia Mongardi
27 Gennaio 2026 | 14:15
DEIB, Sala Seminari "A. Alario" (Ed. 21)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, January 27th, 2026 at 2:15 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB "Alessandra Alario" Seminar Room (Building 21) organized by the Data Science for Bioinformatics group.The seminar will be held by Sofia Mongardi, PhD student in Information Technologies, on the following subject: "Spatial transcriptomics analysis and denoising with SpaTIM".
Recent advances in spatial transcriptomics (ST) have made it possible to measure gene expression while preserving the spatial organization of cells within tissue samples. Alongside technological improvements, a growing number of computational approaches have been proposed to analyze ST data. Many of these approaches rely on graph-based models, mainly graph neural networks (GNNs), to capture the relationships between neighboring spots and to learn meaningful representations for downstream tasks like spatial domain identification. While these models achieve state-of-the-art performance, they all rely on a predefined and uniformly-constructed graph, usually built using spatial proximity between spots. This approach assumes that spatially adjacent spots are functionally similar, an assumption that does not always hold. To address these limitations, we propose SpaTIM, a novel computational approach for spatial transcriptomics analysis based on GNNs that incorporates additional morphological context to improve graph construction and representation learning. Using morphological information to refine the graph, we ensure that connected spots have similar morphological features, allowing the model to dynamically adjust graph connectivity beyond simple spatial proximity. This allows the model to filter out noisy connections and enhance biologically meaningful relationships, potentially improving the accuracy of spatial domain identification and other downstream tasks.