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Image Analysis and Data Normalization Procedures are Crucial for Microarray Analyses

Author(s): Ali Kpatcha Kadanga | Christine Leroux | Muriel Bonnet | Stéphanie Chauvet | Bruno Meunier | Isabelle Cassar-Malek | Jean-François Hocquette

Journal: Gene Regulation and Systems Biology
ISSN 1177-6250

Volume: 2;
Start page: 107;
Date: 2008;
Original page

Keywords: microarray | bovine | data analysis | experimental design | statistical analyses

This study was conducted with the aim of optimizing the experimental design of array experiments. We compared two image analysis and normalization procedures prior to data analysis using two experimental designs. For this, RNA samples from Charolais steers Longissimus thoracis muscle and subcutaneous adipose tissues were labeled and hybridized to a bovine 8,400 oligochip either in triplicate or in a dye-swap design. Image analysis and normalization were processed by either GenePix/MadScan or ImaGene/GeneSight. Statistical data analysis was then run using either the SAM method or a Student’s t-test using a multiple test correction run on R 2.1 software. Our results show that image analysis and normalization procedure had an impact whereas the statistical methods much less influenced the outcome of differentially expressed genes. Image analysis and data normalization are thus an important aspect of microarray experiments, having a potentially significant impact on downstream analyses such as the identification of differentially expressed genes. This study provides indications on the choice of raw data preprocessing in microarray technology.
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