Liu Yufang, Chen Yongkai, Lu Haoran, Zhong Wenxuan, Yuan Guo-Cheng, Ma Ping
Department of Statistics, University of Georgia, Athens, GA, 30602, USA.
Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
BMC Bioinformatics. 2024 Apr 25;25(1):164. doi: 10.1186/s12859-024-05773-y.
Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.
多模态整合结合来自不同来源或模态的信息，以更全面地理解一种现象。多组学数据分析中的挑战在于数据的复杂性、高维度和异质性，这需要复杂的计算工具和可视化方法来对多组学数据进行恰当的解释和可视化。在本文中，我们提出了一种用于分析CITE-seq的新方法，称为正交多模态整合与聚类（OMIC）。我们的方法使研究人员能够整合多种信息来源，同时考虑它们之间的依赖性。我们使用CITE-seq数据集进行细胞聚类来证明我们方法的有效性。我们的结果表明，我们的方法在准确性、计算效率和可解释性方面优于现有方法。我们得出结论，我们提出的OMIC方法为多模态数据分析提供了一个强大的工具，极大地提高了整合数据的可行性和可靠性。
