a nonparametric mean-variance smoothing method to assess arabidopsis cold stress transcriptional regulator cbf2 overexpression microarray data非参数均值-方差滤波方法来评估拟南芥转录监管机构cbf2冷应激过度微阵列数据.pdfVIP

A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data 1 2 Pingsha Hu *, Tapabrata Maiti 1 Department of Energy-Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America, 2 Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America Abstract Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is availab