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Mentions: Microarray has become a powerful tool for biological and medical science to monitor transcriptome changes under different treatments. However, because of high price of microarray experiments, replicates for each experiment are restricted in most cases. The feature of small replicates and large gene numbers, e.g., about 6,000 in yeast and 23,000 in Arabidopsis, in microarray data usually results in poor estimation of gene-specific variances. Several methods have been suggested for modification of gene specific variances or covariances to improve the estimation. For example, Efron et al.  suggested modifying the denominator of the -statistic to allow estimation less sensitive to gene-specific variances. Smyth  proposed smoothing gene-specific variances to a common value. Cui et al.  and Tong and Wang  developed shrinkage estimators for gene specific variances using Stein-type estimation under squared error loss function which were used to construct traditional - type and - type statistics. In all the above estimators, gene specific means were assumed to be independent of variances. It has been observed that means are related to variances in microarray experiments; usually genes with high expression level show high variances, while genes with low expression level display small variances (Figure 1).
A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data
Bottom Line: 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.The source code written in R is available from the authors on request.
Affiliation: Department of Energy-Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America. firstname.lastname@example.org
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 available from the authors on request.
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