Authors: Anastasia Litinetskaya, Maiia Shulman, Soroor Hediyeh-zadeh, Amir Ali Moinfar, Fabiola Curion, Artur Szałata, Alireza Omidi, Mohammad Lotfollahi, Fabian J. Theis
Preprint in bioRxiv, 2024
Abstract
Multimodal analysis of single-cell samples from healthy and diseased tissues at various stages provides a comprehensive view that identifies disease-specific cells, their molecular features and aids in patient stratification. Here, we present MultiMIL, a novel weakly-supervised multimodal model designed to construct multimodal single-cell references and prioritize phenotype-specific cells via patient classification. MultiMIL effectively integrates single-cell modalities, even when they only partially overlap, providing robust representations for downstream analyses such as phenotypic prediction and cell prioritization. Using a multiple-instance learning approach, MultiMIL aggregates cell-level measurements into sample-level representations and identifies disease-specific cell states through attention-based scoring. We demonstrate that MultiMIL accurately identifies disease-specific cell states in blood and lung samples, identifying novel disease-associated genes and achieving superior patient classification accuracy compared to existing methods. We anticipate MultiMIL will become an essential tool for querying single-cell multiomic atlases, enhancing our understanding of disease mechanisms and informing targeted treatments.
1/6 Introducing MultiMIL: a weekly supervised multimodal model to identify disease-specific changes in single-cell atlases. Using a multiple-instance learning framework and attention mechanism, it prioritizes cells linked to various phenotypes in a large single-cell reference Show more
