Objective

During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets.

Approach and results

We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy.

Conclusion

We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

中文翻译：

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识别心血管疾病的时间分子特征：数据科学平台。

客观的

在心血管疾病进展过程中，心肌的分子系统（例如蛋白质组）经历了多种不同的变化。单个分子的动态、时间调控的改变是心脏对病理驱动因素和发病机制的最终发展的集体反应的基础。高通量组学技术的进步使得能够在人类疾病的动物模型中对目标系统进行经济高效的时间分析。然而，从组学数据中对时间模式进行计算分析仍然具有挑战性。特别是，缺乏涉及支持心血管调查的无监督统计方法的生物信息学管道，这阻碍了人们从这些复杂的数据集中提取生物医学见解的能力。

方法和结果

我们开发了一个非参数数据分析平台来解决时间组学数据集特有的计算挑战。我们的平台由三个模块组成。模块 I 使用三次样条或主成分分析 (PCA) 对时间数据进行预处理，同时完成缺失数据插补和去噪的任务。模块 II 通过 K 均值或层次聚类执行无监督分类。模块 III 评估和识别表现出与特定时间模式强关联的生物实体（例如，分子事件）。集群成员的 jackstraw 方法已被应用于估计p-values 和后验包含概率 (PIP)，两者都指导特征选择。为了证明分析平台的实用性，我们采用了时间蛋白质组学数据集，该数据集捕获了经历异丙肾上腺素 (ISO) 诱导肥大的小鼠心脏中氧化应激诱导的翻译后修饰 (O-PTM) 的蛋白质组范围动态。

结论

我们创建了一个平台 CV.Signature.TCP 来识别组学数据集中不同的时间簇。我们提出了一个心血管用例，以证明其在揭示心脏重塑中 O-PTM 法规背后的生物学见解方面的效用。该平台在开源 R 包中实现（https://github.com/UCLA-BD2K/CV.Signature.TCP）。