CMOT: Cross-Modality Optimal Transport for multimodal inference.
CMOT: Cross-Modality Optimal Transport for multimodal inference.
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CMOT：多模式推理的跨模式最佳传输。
DOI：
10.1186/s13059-023-02989-8
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发表时间：
2023-07-11
期刊：
影响因子：
12.3
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中科院分区：
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单细胞测序技术的多模式测量有助于全面了解特定的细胞和分子机制。然而，单细胞的多种模式的同时分析是具有挑战性的，并且由于缺少模式和细胞-细胞对应性，数据集成仍然是难以捉摸的。为了解决这个问题，我们开发了一种计算方法，跨模态最优传输（CMOT），它将可用的多模态数据（源）中的细胞对齐到一个共同的潜在空间，并从映射的源细胞的另一种模态（目标）中推断细胞的缺失模态。CMOT在从脑发育、癌症到免疫学的各种应用中优于现有方法，并提供生物学解释，改善细胞类型或癌症分类。 在线版本包含补充材料，可通过10.1186/s13059-023-02989-8获得。
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. The online version contains supplementary material available at 10.1186/s13059-023-02989-8.