.png){kind=icon}
Return
Benchmarking single-cell multi-modal data integrations
.png){kind=icon}
PRE
AI
DOI：10.1038/s41592-025-02737-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent advances have enabled the generation of both unpaired (separate profiling) and paired (simultaneous measurement) single-cell 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. This Registered Report presents a comprehensive benchmarking analysis of single-cell multi-modal data-integration methods.
Journal
IF:
32.1
Papers:
7.2K
Citations:
12.7W
Organization
Cited Papers
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.png)
NATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAIShareSaveStabilized mosaic single-cell data integration using unshared featuresNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveShareSaveMosaic integration and knowledge transfer of single-cell multimodal data with MIDASNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveA deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq dataGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSave
ShareSaveShareSave
View more