Batch effects correction in scRNA-seq based on biological-noise decoupling autoencoder and central-cross loss

IF 2.6 4区 生物学 Q2 BIOLOGY
Zhangjie Di , Bo Yang , Meng Li , Yue Wu , Hong Ji
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引用次数: 0

Abstract

Technical or biologically irrelevant differences caused by different experiments, times, or sequencing platforms can generate batch effects that mask the true biological information. Therefore, batch effects are typically removed when analyzing single-cell RNA sequencing (scRNA-seq) datasets for downstream tasks. Existing batch correction methods usually mitigate batch effects by reducing the data from different batches to a lower dimensional space before clustering, potentially leading to the loss of rare cell types. To address this problem, we introduce a novel single-cell data batch effect correction model using Biological-noise Decoupling Autoencoder (BDA) and Central-cross Loss termed BDACL. The model initially reconstructs raw data using an auto-encoder and conducts preliminary clustering. We then construct a similarity matrix and a hierarchical clustering tree to delineate relationships within and between different batches. Finally, we introduce a Central-cross Loss (CL). This loss leverages cross-entropy loss to prompt the model to better distinguish between different cluster labels. Additionally, it employs the Central Loss to encourage samples to form more compact clusters in the embedding space, thereby enhancing the consistency and interpretability of clustering results to mitigate differences between different batches. The primary innovation of this model lies in reconstructing data with an auto-encoder and gradually merging smaller clusters into larger ones using a hierarchical clustering tree. By using reallocated cluster labels as training labels and employing the Central-cross Loss, the model effectively eliminates batch effects in an unsupervised manner. Compared to current methods, BDACL can mitigate batch effects without losing rare cell types.
基于生物噪声解耦自动编码器和中心交叉损失的 scRNA-seq 批次效应校正。
不同实验、时间或测序平台造成的技术或生物相关性差异会产生批次效应,掩盖真实的生物信息。因此,在为下游任务分析单细胞 RNA 测序(scRNA-seq)数据集时,通常要去除批次效应。现有的批次校正方法通常通过在聚类前将不同批次的数据缩小到较低维度空间来减轻批次效应,这可能会导致稀有细胞类型的丢失。为了解决这个问题,我们采用生物噪声解耦自动编码器(BDA)和中心交叉损失(Central-cross Loss)引入了一种新的单细胞数据批次效应校正模型,称为 BDACL。该模型最初使用自动编码器重建原始数据,并进行初步聚类。然后,我们构建一个相似性矩阵和一个分层聚类树,以划分不同批次内部和之间的关系。最后,我们引入了中心交叉损失(Central-cross Loss,CL)。这种损失利用交叉熵损失来促使模型更好地区分不同的聚类标签。此外,它还利用中心损失来鼓励样本在嵌入空间中形成更紧凑的聚类,从而提高聚类结果的一致性和可解释性,减少不同批次之间的差异。该模型的主要创新在于使用自动编码器重建数据,并使用分层聚类树将较小的聚类逐渐合并为较大的聚类。通过使用重新分配的聚类标签作为训练标签,并采用中心交叉损失(Central-cross Loss),该模型以无监督的方式有效消除了批次效应。与目前的方法相比,BDACL 可以在不丢失稀有细胞类型的情况下减轻批次效应。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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