Pantazis Lucio José, Frechtel Gustavo Daniel, Cerrone Gloria Edith, García Rafael, Iglesias Molli Andrea Elena
Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires (ITBA), Lavardén 315 1437, Ciudad Autónoma de Buenos Aires, Argentina.
CONICET-Universidad de Buenos Aires. Instituto de Inmunología, Genética y Metabolismo (INIGEM). Laboratorio de Diabetes y Metabolismo. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina.
EJIFCC. 2023 Oct 16;34(3):228-244. eCollection 2023 Oct.
Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.
To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.
A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.
In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.
The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.
由于先验信息有限、高维度和异质性，分析纵向基因表达数据极具挑战性。在2型糖尿病等多因素疾病的研究中也会出现类似的困难。聚类方法可用于自动将相似的观察结果分组。所得组内的常见临床值表明可能存在关联。然而，将传统聚类方法应用于随时间变化的基因表达无法捕捉反应中的变化。因此，基于形状的聚类可用于在大型时间代谢补偿干预中通过基因表达变化识别患者群体。
寻找在白细胞介素-1β基因表达随时间变化中表现出相似结构的受试者之间的临床分组模式。
一种基于白细胞介素-1β表达行为的形状聚类新方法被应用于一个真实的2型糖尿病患者纵向数据库。为了正确捕捉反应中的变化，我们将传统聚类方法应用于测量值之间的斜率。
在这种情况下，使用曼哈顿距离的K-中心点算法在相应数据库中产生了最佳结果。在所得的组中，其中一个聚类在与代谢综合征相关的许多关键临床值方面与其他数据相比存在显著差异。
所提出的方法可用于根据基因表达的变化模式（或其他应用）对患者进行分组，因此，即使在对个体的临床特征没有先验知识且每个个体的时间点较少的情况下，也能提供临床见解。
