Jiawen Zhu, Song Wu and Jie Yang
Objective: Understanding functions of microRNAs (or miRNAs), particularly their effects on protein degradation, is biologically important. Emerging technologies, including the reverse-phase protein array (RPPA) for quantifying protein concentration and RNA-seq for quantifying miRNA expression, provide a unique opportunity to study miRNA-protein regulatory mechanisms. One naïve way to analyze such data is to directly examine the correlation between the raw miRNA measurements and protein concentrations estimated from RPPA. However, the uncertainty associated with protein concentration estimates is ignored, which may lead to less accurate results and significant power loss.
Methods: We propose an integrated nonlinear hierarchical model for detecting miRNA targets through original RPPA intensity data. This model is fitted within a maximum likelihood framework and the correlation test between miRNA and protein is assessed using Wald tests. We compare this model and the simple method through extensive simulation studies and a real dataset from the Cancer Genome Atlas (TCGA) project.
Results: This integrated method is shown to have consistently higher power than the simple method, especially when sample sizes are limited and when the RPPA intensity levels are close to the boundaries of imaging limits.
Conclusions: Our proposed method is powerful in detecting miRNA’s protein target through RPPA. We recommend this method in practice.
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Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report
