Lim Hong Seo, Qiu Peng
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2024 Feb 28;20(2):e1011866. doi: 10.1371/journal.pcbi.1011866. eCollection 2024 Feb.
Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.
在现有的用于单细胞RNA测序分析的计算算法中，聚类和轨迹推断是两种常规应用的主要分析类型。对于给定的数据集，聚类和轨迹推断可以生成截然不同的可视化结果，从而导致对数据的解释大相径庭。为了解决这个问题，我们提出了多个分数来量化单细胞RNA测序数据的“聚类性”和“轨迹性”，换句话说，数据看起来是像不同簇的集合还是连续的进展轨迹。我们引入的分数基于成对距离分布、持久同调、向量大小、Ripley's K和连通度。使用模拟数据集，我们证明了所提出的分数能够有效区分类簇数据和类轨迹数据。使用真实的单细胞RNA测序数据集，我们证明这些分数可以作为聚类分析或轨迹推断是否是对数据进行生物学解释的更合适选择的指标。
