MISF: Multimodal Data Integration Through Adaptive Similarity Learning and Matrix Factorization

Single-cell multimodal data can simultaneously provide cellular features at different levels, such as gene expression, chromatin accessibility, and spatial location. The integration of multimodal data can efficiently utilize the information from various views, thereby enhancing the reliability and accuracy of cellular research. The current integration methods mainly focus on obtaining the representation of cells but neglect the representation of genes, not beneficial to cell type-specific gene module analyses. Besides, some integration algorithms only can integrate multi-omics data and cannot be applied to spatial transcriptome data for integrating transcriptomic data and spatial location. To this end, we propose MISF, a Multimodal data Integration algorithm based on adaptive Similarity network learning and matrix Factorization. MISF integrates multimodal data and learns the lower-dimensional representations of cells and genes. We validate the feasibility of MISF on multiple single-cell multi-omics data and spatial transcriptome data and compare it with the existing multi-omics data integration methods as well as spatial transcriptome data analysis algorithms. The results demonstrate that MISF can effectively integrate multimodal data, localize different types of cell clusters, and outline cellular spatial distribution pattern. Furthermore, MISF facilitates cell clustering and cell type-specific gene module analyses, providing new insights for the study of cellular heterogeneity.