A Comparative Study of Computational Methods for Multimodal Single-Cell Data Integration

摘要: Integrating multimodal single-cell data is formalized into predicting co-variation among DNA, RNA, and protein measurements within individual cells. Accurate prediction of single-cell table data across multiple modalities will provide a more in-depth understanding of the mechanisms underlying tissue function or dysfunction in health and disease. While previous studies highlight the potential of machine learning in multimodal single-cell integration, there is a lack of comparison studies that focus on technical aspects rather than biology-related details. Therefore, this study encompassed the entire pipeline and compared the four highest-performing methods in the Multimodal single-cell data integration NeurIPS 2022 Competition through performance and efficiency, which provides data scientists with appropriate entry points. Experiment results revealed the significance of preprocessing and feature engineering in …