Orthogonal Multimodality Integration and Clustering in Single-cell Data

2579 

Contributed Abstract 

Poster 

Yufang Liu (1), Yongkai Chen (1), Haoran Lu (2), Wenxuan Zhong (2), Guo-cheng Yuan (3), Ping Ma (2)

(1) N/A, N/A, (2) University of Georgia, N/A, (3) Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY

Yongkai Chen  
N/A

Haoran Lu  
University of Georgia

Wenxuan Zhong  
University of Georgia

Guo-cheng Yuan  
Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai

Ping Ma  
University of Georgia

Yufang Liu  
N/A

Yufang Liu  
N/A

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

Miscellaneous