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ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

An Integrated Method for Detecting Micro RNA Target Proteins through Reverse-phase Protein Arrays

Jiawen Zhu1, Song Wu1 and Jie Yang2*

1Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11790, USA

2Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11790, USA

*Corresponding Author:
Jie Yang
Department of Preventive Medicine
Stony Brook University
Stony Brook, NY 11790, USA
Tel: 631-444-2191
Fax: +1 631-444-7525
E-mail: [email protected]

Received date: November 03, 2014; Accepted date: November 18, 2014; Published date: January 01, 2015

Citation: Zhu J, Wu S, Yang J (2015) An Integrated Method for Detecting Micro RNA Target Proteins through Reverse-phase Protein Arrays. J Comput Sci Syst Biol 8:012-033. doi:10.4172/jcsb.1000166

Copyright: ©2015 Zhu J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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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