Joint variational autoencoders for multimodal imputation and embedding
Joint variational autoencoders for multimodal imputation and embedding
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DOI：
10.1038/s42256-023-00663-z
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发表时间：
2023-05-29
影响因子：
23.8
通讯作者：
Wang，Daifeng
中科院分区：
文献类型：
--
作者：
Kalafut，Noah Cohen;Huang，Xiang;Wang，Daifeng
单细胞多模态数据集测量了单个细胞的各种特征，从而能够深入了解细胞和分子机制。然而，多模式数据生成仍然成本高昂且具有挑战性，并且经常发生模式缺失。最近，机器学习方法已经开发用于数据输入，但通常需要完全匹配的多模态来学习可能缺乏模态特异性的常见潜在嵌入。为了解决这些问题，我们开发了一个开源的机器学习模型，联合变分自编码器用于多模态插值和嵌入（JAMIE）。JAMIE获取单细胞多模态数据，这些数据可能具有跨模态部分匹配的样本。变分自编码器学习每个模态的潜在嵌入。然后，在重建之前，将来自匹配样本的跨模态嵌入聚合以识别联合的跨模态潜在嵌入。为了进行跨模态插值，可以将一种模态的潜在嵌入与另一种模态的解码器一起使用。为了可解释性，Shapley值用于对跨模态输入和已知样本标签的输入特征进行优先级排序。我们将JAMIE应用于模拟数据和新兴的单细胞多模态数据，包括人类和小鼠大脑中的基因表达、染色质可及性和电生理。JAMIE在一般情况下明显优于现有的最先进的方法，并优先考虑多模态特征，为细胞分辨率提供潜在的新颖机制见解。
Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.