Poster
Attention-based Multi-instance Mixed Models
Jan Engelmann · Alessandro Palma · Jakub Tomczak · Fabian Theis · Francesco Paolo Casale
MR1 & MR2 - Number 129
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Student Paper Highlight |
Fri 3 May 6 a.m. PDT — 6:30 a.m. PDT
Predicting patient features from single-cell data can unveil cellular states implicated in health and disease. Linear models and average cell type expressions are typically favored for this task for their efficiency and robustness, but they overlook the rich cell heterogeneity inherent in single-cell data. To address this gap, we introduce GMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL), upholding the advantages of linear models while modeling cell-state heterogeneity. By leveraging predefined cell embeddings, GMIL enhances computational efficiency and aligns with recent advancements in single-cell representation learning. Our empirical results reveal that GMIL outperforms existing MIL models in single-cell datasets, uncovering new associations and elucidating biological mechanisms across different domains.