Meng Dian, Feng Yu, Yuan Kaishen, Yu Zitong, Cao Qin, Cheng Lixin, Zheng Xubin
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
School of Computing and Information Technology, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf010.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type. Moreover, as the majority of single-cell cohorts only encompass one omics, it becomes critical to transfer the knowledge learnt from multimodal omics to enhance unimodal omics analysis. Therefore, we proposed a novel framework that leverages masked autoencoder with cross-attention mechanism, called scMMAE (single-cell multimodal masked autoencoder), to fuse multimodal omics and enhance unimodal omics analysis. scMMAE simultaneously captures both the shared features and the distinctive information of two single-cell omics modalities and transfers the knowledge to enhance single-cell transcriptome data. Comparative evaluations against benchmarking methods across various cohorts revealed a notable improvement, with an increase of up to 21% in the adjusted Rand index and up to 12% in normalized mutual information in the context of multimodal fusion. In the realm of unimodal omics, scMMAE demonstrated an overall enhancement of approximately 20% in the adjusted Rand index and nearly 10% in normalized mutual information. Other nine metrics, including the Fowlkes-Mallows index and silhouette coefficient, further underscored the high performance of scMMAE. Significantly, scMMAE exhibits an elevated level of proficiency in distinguishing between different cell types, particularly on CD4 and CD8 T cells. Availability and implementation: scMMAE source code at https://github.com/DM0815/scMMAE/.
多组学技术能够更深入地洞察生物过程和细胞功能，尤其是转录组学和蛋白质组学。人们已经提出了一些计算方法来整合单细胞转录组学和蛋白质组学的多组学数据。然而，现有方法主要集中在不同组学的对齐上，忽略了每种组学类型中固有的独特信息。此外，由于大多数单细胞队列仅包含一种组学数据，因此将从多组学中学到的知识转移以增强单组学分析变得至关重要。因此，我们提出了一种新颖的框架，即利用带有交叉注意力机制的掩码自动编码器，称为scMMAE（单细胞多模态掩码自动编码器），来融合多组学并增强单组学分析。scMMAE同时捕捉两种单细胞组学模态的共享特征和独特信息，并转移知识以增强单细胞转录组数据。在各个队列中与基准方法进行的比较评估显示出显著改进，在多模态融合的情况下，调整后的兰德指数提高了21%，标准化互信息提高了12%。在单组学领域，scMMAE在调整后的兰德指数中总体提高了约20%，在标准化互信息中提高了近10%。包括福克-马洛斯指数和轮廓系数在内的其他九个指标进一步凸显了scMMAE的高性能。值得注意的是，scMMAE在区分不同细胞类型方面表现出更高的熟练度，尤其是在CD4和CD8 T细胞上。可用性和实现方式：scMMAE源代码位于https://github.com/DM0815/scMMAE/ 。
