Arbor Research Collaborative for Health, Ann Arbor, Michigan, United States of America.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS One. 2022 Jun 10;17(6):e0268547. doi: 10.1371/journal.pone.0268547. eCollection 2022.
We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.
我们提出了一种通过整合异构连续和分类数据对具有共同临床症状的个体进行亚型划分的方法。我们通过对具有重叠症状和多因素病因的下尿路症状（LUTS）女性进行聚类来说明这种方法。该研究的数据来自多中心观察性研究——下尿路功能障碍症状研究网络（LURN），包括 545 名女性的自我报告的尿和非尿症状、膀胱日记和体格检查数据。这些多维数据的异质性需要进行彻底和非平凡的预处理，包括通过对照进行缩放和加权以减轻数据冗余，而各种数据类型（连续和分类）需要使用加权 Tanimoto 指数方法的新方法。使用半监督聚类方法整合仅在队列的一部分中可用的数据域。引入了用于确定共识聚类中最佳聚类数的新对比标准，并与现有标准进行了比较。通过使用多个聚类质量标准和对聚类进行两两比较以测试显著不同的变量来确认聚类的独特性。通过分析 3 个月和 12 个月随访的纵向数据来探索聚类动态。使用开发的方法确定了 5 个具有 LUTS 的女性聚类。没有一个聚类可以用单一症状来描述，而是由各种严重程度的不同症状组合来描述。对血清样本的靶向蛋白质组学分析表明，不同聚类之间差异表达的蛋白质和受影响的途径不同。讨论了所确定的聚类的临床相关性，并将其与当前评估 LUTS 患者的常规方法进行了比较。描述了用于数据预处理、聚类和聚类评估的程序选择的基本原理和思维过程。为利用具有多个异构数据域的聚类方法的出版物提供了最小报告要求的建议。
