Continual integration of single-cell multimodal data with MIRACLE

Abstract

Single-cell sequencing technologies have revolutionized our understanding of cellular heterogeneity and facilitated the construction of multi-omics cell atlases via data integration. However, updating these atlases with new data conventionally requires reintegration of all data and is computationally intensive, hindering timely updates and dynamic adjustments in biological and medical research. To address this challenge, we present Multimodal Integration with Continual Learning (MIRACLE), a novel online learning framework for the adaptive and efficient integration of single-cell multimodal data. MIRACLE employs dynamic architectures and data rehearsal strategies to support continual learning, allowing diverse data to be integrated while minimizing information loss over time. Our evaluations demonstrate that MIRACLE achieves accurate online integration with reduced computational requirements, effectively updating and expanding atlases with new cross-tissue and cross-modal data, and precisely identifying novel cell types and transferring labels across datasets. MIRACLE provides an efficient and flexible tool for single-cell community to integrate, share and explore biological knowledge from single-cell multimodal data.