Key result
The CV.Signature.TCP platform successfully identified distinct temporal clusters in omics datasets, demonstrating its utility in unveiling biological insights in cardiac remodeling.
Why the study?
Computational analysis of temporal patterns from omics data remains challenging, and bioinformatic pipelines using unsupervised statistical approaches to support cardiovascular investigations are lacking.
The development of the CV.Signature.TCP platform provides a valuable open-source tool for extracting biological insights from complex temporal omics datasets in cardiovascular research.
No immediate clinical implications; extends open-source tools for temporal omics analysis in preclinical cardiac remodeling.
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).
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Chung et al. (2020) studied Cardiovascular disease. CV.Signature.TCP platform was evaluated on Identification of distinct temporal clusters in omics datasets. The CV.Signature.TCP platform successfully identified distinct temporal clusters in omics datasets, demonstrating its utility in unveiling biological insights in cardiac remodeling.
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