A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data.
A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data.
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DOI：
10.1016/j.crmeth.2021.100071
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发表时间：
2021-09-27
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单细胞多组学分析的最新发展使得能够在单细胞水平上同时检测多个性状，从而更深入地了解不同组织中的细胞表型和功能。然而，目前，从复杂的多模态单细胞数据中推断联合表示和学习多模态之间的关系是具有挑战性的。在这里，我们提出了scMM，一种新的基于深度生成模型的框架，用于提取可解释的联合表示和交叉模态生成。scMM通过利用专家混合的多模态变分自动编码器来解决数据的复杂性。scMM的伪细胞生成策略弥补了深度学习模型的有限可解释性，并且所提出的方法通过实验发现了与潜在维度相关的多模态调节程序。对最近生成的数据集的分析验证了scMM有助于高分辨率聚类，具有丰富的可解释性。此外，我们表明，与最先进的和传统的方法相比，scMM的交叉模态生成导致更精确的预测和数据集成。scMM从单细胞多组学数据中学习低维联合表示scMM在单细胞多模态数据中检测先前被忽视的细胞群体假细胞生成使scMM能够学习可解释的潜在维度scMM通过跨模态生成准确预测缺失的模态革命性的单细胞多组学技术已经能够跨多种模态获取单个细胞的特征，例如转录组，表观基因组，和表面蛋白质。然而，用于复杂和高维多模态单细胞数据的综合分析的计算方法目前是有限的。在这里，我们提出了scMM，一种用于单细胞多组学数据综合分析的专家混合深度生成模型。scMM有效地从多模态单细胞数据推断可解释的联合表示。此外，scMM学习跨模态的底层关系，从而实现单细胞数据的跨模态生成。Minoura等人报告scMM的发展，这是一种基于多模态深度生成模型的框架，用于分析单细胞多组学数据。scMM从可用于下游分析的高维多模态数据中提取生物学上可解释的联合表示。此外，它还可以学习单细胞模态之间的关系，从而实现对缺失模态的多对多预测。
The recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models, and the proposed approach experimentally discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that crossmodal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches. scMM learns low-dimensional joint representations from single-cell multiomics data scMM detects previously overlooked cell populations in single-cell multimodal data Pseudocell generation enables scMM to learn interpretable latent dimensions scMM accurately predicts missing modalities by crossmodal generation Revolutionary single-cell multiomics technologies have enabled acquiring characteristics of individual cells across multiple modalities, such as transcriptome, epigenome, and surface proteins. However, computational methods for integrated analysis of complex and high-dimensional multimodal single-cell data are currently limited. Here, we present scMM, a mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data. scMM effectively infers interpretable joint representations from multimodal single-cell data. In addition, scMM learns underlying relationships across modalities, enabling crossmodal generation of single-cell data. Minoura et al. report the development of scMM, a multimodal deep generative model-based framework for analyzing single-cell multiomics data. scMM extracts biologically interpretable joint representations from high-dimensional multimodal data that can be used for downstream analyses. In addition, it learns relationships among single-cell modalities, enabling many-to-many prediction of missing modalities.