Recursive Clustering of Cellular Diversity in scRNA-Seq Data.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Michael Squires, Peng Qiu
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引用次数: 0

Abstract

In scRNA-seq analysis, cell clusters are typically defined by a single round of feature extraction and clustering. This approach may miss phenotypic differences in cell types that are characterized by genes not sufficiently represented in the feature set derived using all cells, such as rare cell types. This work explores an alternative approach, where cell clusters are identified by recursively performing feature extraction and clustering on previously identified clusters, such that each subclustering step uses features that are more specific to distinguishing the higher-resolution subclusters. We benchmark this recursive approach against the conventional, nonrecursive clustering approach and demonstrate that the recursive method results in robust improvement in cell type detection on four scRNA-seq datasets across a wide range of clustering resolution parameters. We apply the recursive approach to cluster scRNA-seq data obtained from patients with Crohn's disease belonging to three clinical phenotypes and observe that recursive clustering captures phenotypic differences only visible at specific levels of granularity within an interpretable hierarchical framework while defining cell clusters within a gene expression feature space more specific to each cluster.

scRNA-Seq数据中细胞多样性的递归聚类。
在scRNA-seq分析中,细胞簇通常通过一轮特征提取和聚类来定义。这种方法可能会错过细胞类型的表型差异,这些差异是由基因表征的,在使用所有细胞衍生的特征集中没有充分代表,例如罕见的细胞类型。这项工作探索了一种替代方法,其中通过递归地对先前识别的集群进行特征提取和聚类来识别细胞集群,这样每个子聚类步骤使用更具体的特征来区分更高分辨率的子集群。我们将这种递归方法与传统的非递归聚类方法进行了基准测试,并证明递归方法在广泛的聚类分辨率参数范围内对四个scRNA-seq数据集的细胞类型检测产生了鲁棒性改进。我们将递归方法应用于从克罗恩病患者中获得的属于三种临床表型的scRNA-seq数据,并观察到递归聚类仅在可解释的层次框架内的特定粒度水平上可见的表型差异,同时在基因表达特征空间中定义每个簇更具体的细胞簇。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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