A Semiparametric Integrated Model for Identifying miRNA Target Proteins

• Activity Number: 193
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jiawen Zhu* and Jie Yang and Song Wu
• Companies: SUNY Stony Brook and SUNY Stony Brook and SUNY Stony Brook
• Keywords: B-spline ; nonlinear mixed model ; RPPA ; miRNA

Abstract

It is biologically important to understand miRNAs function, particularly their effects on protein expression. By using emerging technologies, the reverse-phase protein array (RPPA) and RNA-seq, we have a unique opportunity to study miRNA-protein regulatory mechanisms. A naïve way to analyze such data is to directly examine the correlation between the raw miRNA measurements and protein concentrations estimated from RPPA through simple linear regression models. However, the uncertainty associated with protein concentration estimates is ignored. We have proposed a parametric integrated nonlinear hierarchical model to improve the simple method. But this model requires the RPPA response curve has a parametric function form such as a sigmoidal function. Therefore, we further proposed a semiparametric model by incorporating a nonparametric curve fitting technique, which relaxes the assumption of a specific parametric form for the RPPA response curve. Our simulation studies demonstrated that the semiparametric model better estimated the miRNA-protein correlation when the RPPA response curve was not sigmoidal. The proposed model was also illustrated through a real dataset from TCGA program.