单细胞多组学测序正在广泛应用于生物医学研究中，并产生大量的多样性组学数据。然而原始的单细胞多组学数据包含多种类型的测序噪声和冗余信息，对后续生物医疗层面的分析造成困难。现有的降噪方法主要依赖于单一的数据分布假设，并针对性的处理单个组学数据，这对模型联合处理不同组学数据造成极大地限制。本研究提出一种使用单细胞多组学数据降噪的分析方法，称为scMAED(single-cell multi-omics data via a multi-head autoen-coder network to denoising)。模型在多头自动编码器网络中添加了分类解码器，以无监督的方式来最大程度的去除数据噪声。首先，使用两个编码器独立学习多组学数据的内部特征，并联合输出的低维特征进行共同解码。其次，分类解码器不做任何数据分布假设，通过使用预测的细胞簇标签来反馈数据信息，以最大限度的去除复杂噪声。最后，使用主成分分析和 t-SNE进行可视化。本文基于模拟数据集和真实的小鼠数据集对模型进行性能评估，结果显示sc-MAED在降噪效果上优于实验中的对比方法，并能够极大的改善单细胞多组学数据的质量。
Unsupervised Denoising of Single-Cell Multi-Omics Data Based on Multi-Head Autoencoder Network
Single-cell multi-omics sequencing is being widely used in biomedical research and generates large amounts of diverse omics data.However,raw single-cell multi-omics data contains multiple types of sequencing noise and redundant information,which makes subse-quent biomedical analysis difficult.Existing denoising methods mainly rely on a single data distribution assumption and process a single omics data in a targeted manner,which greatly limits the joint processing of different omics data by the model.Therefore,we design and propose an analytical method for denoising using single-cell multi-omics data,called sc-MAED(single-cell multi-omics data via a multi-head autoencoder network to denoising).The model adds a classification decoder to the multi-head autoencoder network to remove the maximum noise from the data in an unsupervised manner.First,two encoders are used to independently learn the internal features of the multi-omics data,and jointly decode the out-put low-dimensional features.Second,the classification decoder does not make any data dis-tribution assumptions,and uses the predicted cell cluster labels to feed back data information to minimize complex noise.Finally,we use principal component analysis and t-SNE for visu-alization.In this paper,we evaluate the performance of the model based on simulated data-sets and real mouse datasets.The results show that scMAED is superior to the experimental comparison method in denoising effect,and can greatly improve the quality of single-cell multi-omics data.
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