Methods for evaluating gene expression from Affymetrix microarray datasets

doi:10.1186/1471-2105-9-284

BMC Bioinformatics

Volume 9

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Luo ZW

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Research article

Methods for evaluating gene expression from Affymetrix microarray datasets

Ning Jiang¹^* , Lindsey J Leach¹^* , Xiaohua Hu³ , Elena Potokina¹ , Tianye Jia¹ , Arnis Druka² , Robbie Waugh² , Michael J Kearsey¹ and Zewei W Luo¹^,3

¹School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK

²Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK

³Institute of Biostatistics, Fudan University, Shanghai 200433, PR China

author email corresponding author email* Contributed equally

BMC Bioinformatics 2008, 9:284doi:10.1186/1471-2105-9-284


Published:	17 June 2008

Additional files

Additional file 1:

Pearson's Product Moment Correlation Coefficients among yeast gene expression indices calculated from seven different methods.

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Additional file 2:

Statistical properties of estimated yeast gene expression indices from seven data extraction methods. (a) Intraclass correlation coefficients between biological replicates of the estimated expression indices for 5,814 genes; (b) Sensitivity for detecting differentially expressed genes; and (c) Calibration p-values across FDR levels.

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Additional file 3:

Pair-wise Pearson correlation coefficients between all pairs of 24 sets (8 barley cultivars × 3 replicates) of 22,840 gene expression indices calculated from the MAS5.0 and RMA methods with different background correction steps and for the AD and MBEI methods with different normalization steps.

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Additional file 4:

Mutual predictability of the number of yeast genes declared differentially expressed from seven data extraction methods.

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