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Poster
in
Workshop: Machine Learning for Genomics Explorations (MLGenX)

Evaluating Spatial Encoding Strategies for Cell Type Annotation with Spatial Omics Data

Merel Kuijs · Alma Andersson · Ehsan Hajiramezanali · Tommaso Biancalani · Aicha BenTaieb


Abstract:

Recent spatial omics research leverages the assumption that spatial information enhances model performance on the cell type annotation task. This study investigates and challenges that assumption by conducting benchmark experiments comparing the performance of spatial and non-spatial models. We show that graph-based spatial models do not consistently outperform non-spatial models, provide theories to explain our findings, and make recommendations for future work on spatial encoding strategies.

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