Multi-task learning from single-cell multimodal omics with Matilda

Abstract Single-cell multimodal omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of single-cell multimodal omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of single-cell multimodal omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular single-cell multimodal omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative single-cell multimodal omics data analysis.