High-Dimensional Multimodal Data Integration for Immune-Driven Diseases: A Practical Guide

Recent advances in multiomics technologies have revolutionized the study of immune-driven diseases by enabling high-dimensional, single-cell resolution analyses. This chapter provides a practical guide for integrating CyTOF (mass cytometry) and single-cell RNA sequencing (scRNA-seq) data to address key challenges in this field. The integration of these modalities allows for consistent and reproducible cell type annotation, the transfer of annotations to assist in characterizing difficult-to-identify populations, and the transcriptional characterization of rare and heterogeneous subpopulations. Using tools such as OMIQ and R, the chapter outlines workflows for preprocessing, normalization, and scaling of CyTOF data, as well as dimensionality reduction and clustering techniques. The integration process involves creating Seurat objects, identifying common features, and using anchor-based methods to link CyTOF and scRNA-seq datasets. The chapter also discusses the use of multimodal deep learning techniques for rare subpopulation detection and emphasizes the importance of reproducibility and standardization in multiomics integration. By leveraging these methodologies, researchers can gain deeper insights into cellular heterogeneity and function, ultimately enhancing the understanding of immune-driven diseases. The chapter concludes by addressing integration challenges and proposing future directions for improving model interpretability and capturing nonlinear molecular interactions.