This paper systematically benchmarks forty computational methods for integrating single-cell multimodal omics data. The authors categorize the integration methods into four types—vertical, diagonal, mosaic, and cross—and evaluate their performance across seven crucial tasks, including dimension reduction, clustering, batch correction, and spatial registration. Using a variety of real and simulated datasets, the study analyzes the strengths and weaknesses of each method based on different evaluation metrics, noting that performance is often dependent on the specific task, dataset, and data modality combination. Ultimately, the report provides a data-driven guideline and interactive tool for researchers to select the most appropriate integration method for their single-cell omics analysis.
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