Spatial integration of multi-omics single-cell data with SIMO DOI Creative Commons

Penghui Yang,

Kaiyu Jin,

Yue Yao

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 1, 2025

Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing describing multimodal information at the scale. To address this, we develop SIMO, a computational method designed Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates transcriptomics with RNA-seq but expands beyond, enabling integration across multiple modalities, such as chromatin accessibility DNA methylation, which have been co-profiled spatially before. We benchmark on simulated datasets, demonstrating its high accuracy robustness. Further application biological reveals SIMO's ability to detect topological patterns cells their regulatory modes layers. Through comprehensive analysis real-world data, uncovers heterogeneity, offering deeper insights into organization regulation molecules. These findings position powerful tool advancing biology by revealing previously inaccessible insights.

Language: Английский

Spatial integration of multi-omics single-cell data with SIMO DOI Creative Commons

Penghui Yang,

Kaiyu Jin,

Yue Yao

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 1, 2025

Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing describing multimodal information at the scale. To address this, we develop SIMO, a computational method designed Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates transcriptomics with RNA-seq but expands beyond, enabling integration across multiple modalities, such as chromatin accessibility DNA methylation, which have been co-profiled spatially before. We benchmark on simulated datasets, demonstrating its high accuracy robustness. Further application biological reveals SIMO's ability to detect topological patterns cells their regulatory modes layers. Through comprehensive analysis real-world data, uncovers heterogeneity, offering deeper insights into organization regulation molecules. These findings position powerful tool advancing biology by revealing previously inaccessible insights.

Language: Английский

Citations

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