39: Orthogonal Multimodality Integration and Clustering in Single-cell Data

Music City Center 

Multimodal integration combines data from diverse sources or modalities to provide a more holistic understanding of a phenomenon. The challenges in multi-omics data analysis stem from the complexity, high dimensionality, and heterogeneity of the data, which require advanced computational tools and visualization methods for effective interpretation. This paper introduces a novel method called Orthogonal Multimodality Integration and Clustering (OMIC) to analyze CITE-seq data.

Our approach allows researchers to integrate various data sources while accounting for interdependencies. We demonstrate its effectiveness in cell clustering using CITE-seq datasets. The results show that our method outperforms existing techniques in terms of accuracy, computational efficiency, and interpretability. We conclude that OMIC is a powerful tool for multimodal data analysis, enhancing the feasibility and reliability of integrated data analysis.

Multimodality Integration

CITE-seq

Cell Clustering 

Section on Statistics in Genomics and Genetics