Benchmarking single-cell multi-modal data integrations

Abstract

Single-cell multi-modal omics, remarked as "Method of the Year 2019" by Nature Methods, provides valuable insights into complex tissues. Recent advances have enabled the generation both unpaired (separate profiling) and paired (simultaneous measurement) multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes, and data quality. Here, we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy, and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics. The Stage 1 protocol for this Registered Report was accepted in principle on 3rd Jul 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Benchmarking_single-cell_multi-modal_data_integrations/26789572.