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.

[1]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Terry M. Therneau,et al.  Normalization of two-channel microarray experiments: a semiparametric approach , 2005, Bioinform..

[3]  M. Kosorok,et al.  Marginal asymptotics for the “large $p$, small $n$” paradigm: With applications to microarray data , 2005, math/0508219.

[4]  Jian Huang,et al.  A robust two-way semi-linear model for normalization of cDNA microarray data , 2005, BMC Bioinformatics.

[5]  P. Tam,et al.  Normalization and analysis of cDNA microarrays using within-array replications applied to neuroblastoma cell response to a cytokine. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Taesung Park,et al.  Evaluation of normalization methods for microarray data , 2003 .

[7]  Jian Huang,et al.  A Two-Way Semilinear Model for Normalization and Analysis of cDNA Microarray Data , 2005 .

[8]  M J van der Laan,et al.  Gene expression analysis with the parametric bootstrap. , 2001, Biostatistics.

[9]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[10]  Pierre R. Bushel,et al.  Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models , 2001, J. Comput. Biol..

[11]  Terence P. Speed,et al.  Normalization for cDNA microarry data , 2001, SPIE BiOS.

[12]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[13]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .