Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074 Aachen, Germany.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i282-i289. doi: 10.1093/bioinformatics/btac220.
The advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities.
Here we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering.
The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128.
Supplementary data are available at Bioinformatics online.
多模态单细胞测序技术的出现通过同时检测同一细胞的转录组、表观基因组和蛋白质组，为分子机制研究提供了新的视角。然而，迄今为止，用于整合多模态单细胞数据的现有计算方法要么计算成本高，要么需要划定参数范围，要么只能应用于特定模态。
我们在这里提出了一种单细胞多模态整合方法，名为 Multi-mOdal Joint IntegraTion of cOmpOnents（MOJITOO）。MOJITOO 使用典型相关分析来快速、无参数地检测来自多模态单细胞数据的细胞共享表示。此外，估计的典型组件可用于解释，即与潜在空间相关的特定于模态的分子特征的关联。我们使用双模态和三模态单细胞数据集来评估 MOJITOO，并表明 MOJITOO 在计算要求、原始潜在空间保留和聚类方面优于现有方法。
用于基准测试的软件、代码和数据可在 https://github.com/CostaLab/MOJITOO 和 https://doi.org/10.5281/zenodo.6348128 获得。
补充数据可在 Bioinformatics 在线获得。
