A data denoising approach to optimize functional clustering of single cell RNA-sequencing data
A data denoising approach to optimize functional clustering of single cell RNA-sequencing data
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DOI：
10.1109/bibm49941.2020.9313483
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Changlin Wan;D. Jia;Yue Zhao;Wennan Chang;Sha Cao;Xiao Wang;Chi Zhang
中科院分区：
文献类型：
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作者：
Changlin Wan;D. Jia;Yue Zhao;Wennan Chang;Sha Cao;Xiao Wang;Chi Zhang
单细胞RNA测序（scRNA-seq）技术能够对复杂组织中具有不同表型和生理状态的数千个细胞进行全面的转录组学分析。已经进行了大量的努力来表征来自scRNA-seq数据的不同身份的单细胞，包括各种细胞聚类技术。虽然现有的方法可以以高分辨率处理不同细胞（亚）类型的单细胞，但同一细胞类型内的功能变异性的鉴定仍然没有解决。此外，缺乏稳健的方法来处理受试者间的变化，这通常会给单细胞的功能聚类带来严重的混杂效应。在这项研究中，我们开发了一种新的数据去噪和细胞聚类方法，即CIBS，为scRNA-seq数据提供生物学上可解释的功能分类。CIBS基于转录调控的系统生物学模型，该模型假设细胞激活状态的多模态分布，并且它利用离散化表达状态的布尔矩阵因子分解方法来稳健地导出功能模块。CIBS由一种新的快速布尔矩阵分解方法（即PFAST）授权，以增加大规模scRNA-seq数据的计算可行性。CIBS在从癌症肿瘤微环境收集的两个scRNA-seq数据集上的应用成功地鉴定了具有上皮-间充质转化和细胞外基质标志物基因的不同表达模式的癌细胞亚组，这是现有细胞聚类分析工具所不能揭示的。所鉴定的细胞群与不同患者的临床证实的淋巴结浸润和转移事件显著相关。
Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells’ activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients.