Hehuang Xie | Search PubMed

PubMed2006

Robust semiparametric microarray normalization and significance analysis.

Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.

Biometry↗

PubMed2005

A robust two-way semi-linear model for normalization of cDNA microarray data.

BACKGROUND: Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values. METHODS: We propose a robust semiparametric method in a two-way semi-linear model (TW-SLM) for normalization of cDNA microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that: (i) the percentage of differentially expressed genes is small; or (ii) the numbers of up- and down-regulated genes are about the same, as required in the LOWESS normalization method. We conduct simulation studies to evaluate the proposed method and use a real data set from a specially designed microarray experiment to compare the performance of the proposed method with that of the LOWESS normalization approach. RESULTS: The simulation results show that the proposed method performs better than the LOWESS normalization method in terms of mean square errors for estimated gene effects. The results of analysis of the real data set also show that the proposed method yields more consistent results between the direct and the indirect comparisons and also can detect more differentially expressed genes than the LOWESS method. CONCLUSIONS: Our simulation studies and the real data example indicate that the proposed robust TW-SLM method works at least as well as the LOWESS method and works better when the underlying assumptions for the LOWESS method are not satisfied. Therefore, it is a powerful alternative to the existing normalization methods.

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