Robust semiparametric microarray normalization and significance analysis.

Shuangge Ma; Michael R Kosorok; Jian Huang; Hehuang Xie; Liliana Manzella; Marcelo Bento Soares

doi:10.1111/j.1541-0420.2005.00452.x

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Robust semiparametric microarray normalization and significance analysis.

Shuangge Ma·Michael R Kosorok·Jian Huang·Hehuang Xie·Liliana Manzella·Marcelo Bento Soares

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Abstract

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.

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Biometry Female Gene Expression Profiling Humans Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Placenta Pregnancy RNA Journal Article Research Support, N.I.H., Extramural

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Shuangge Ma, Michael R Kosorok, Jian Huang, Hehuang Xie, Liliana Manzella, Marcelo Bento Soares. 2006. Robust semiparametric microarray normalization and significance analysis.. https://doi.org/10.1111/j.1541-0420.2005.00452.x

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