Multi-task benchmarking of single-cell multimodal omics integration methods

Liu, C.; Ding, S.; Kim, J. H.; Long, S.; Xiao, D.; Ghazanfar, S.; Yang, P.

doi:10.1101/2024.09.15.613149

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bioRxiv · 10.1101/2024.09.15.613149

Multi-task benchmarking of single-cell multimodal omics integration methods

Liu, C.·Ding, S.·Kim, J. H.·Long, S.·Xiao, D.·Ghazanfar, S.·Yang, P.

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Abstract

Single-cell multimodal omics technologies have empowered the profiling of complex biological systems at a resolution and scale that were previously unattainable. These biotechnologies have propelled the fast-paced innovation and development of data integration methods, leading to a critical need for their systematic categorisation, evaluation, and benchmark. Navigating and selecting the most pertinent integration approach poses a significant challenge, contingent upon the tasks relevant to the study goals and the combination of modalities and batches present in the data at hand. Understanding how well each method performs multiple tasks, including dimension reduction, batch correction, cell type classification and clustering, imputation, feature selection, and spatial registration, and at which combinations will help guide this decision. This study develops a much-needed guideline on choosing the most appropriate method for single-cell multimodal omics data analysis through a systematic categorisation and comprehensive benchmarking of current methods. The Stage 1 protocol for this Registered Report was accepted in principle on 30th July 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Multi-task_benchmarking_of_single-cell_multimodal_omics_integration_methods/26789902.

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Liu, C., Ding, S., Kim, J. H., Long, S., Xiao, D., Ghazanfar, S., Yang, P.. 2024-09-19. Multi-task benchmarking of single-cell multimodal omics integration methods. https://doi.org/10.1101/2024.09.15.613149

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