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CMOT: Cross-Modality Optimal Transport for multimodal inference

Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities...

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Detalles Bibliográficos
Autores principales: Alatkar, Sayali Anil, Wang, Daifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10334579/
https://www.ncbi.nlm.nih.gov/pubmed/37434182
http://dx.doi.org/10.1186/s13059-023-02989-8
Descripción
Sumario:Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell–cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02989-8.