Use of within-array replicate spots for assessing differential expression in microarray experiments

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Use of within-array replicate spots for assessing differential expression in microarray experiments (2005)

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by Gordon K. Smyth , Joëlle Michaud , Hamish S. Scott

Venue:	Bioinformatics
Citations:	239 - 8 self

BibTeX

@ARTICLE{Smyth05useof,
    author = {Gordon K. Smyth and Joëlle Michaud and Hamish S. Scott},
    title = {Use of within-array replicate spots for assessing differential expression in microarray experiments},
    journal = {Bioinformatics},
    year = {2005},
    volume = {21},
    pages = {2067--2075}
}

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Abstract

Motivation. Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. Usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This loses valuable information about gene-wise variability. Results. A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. Availability. The methodology is implemented in the limma software package for R, available from the CRAN repository

Keyphrases

microarray experiment differential expression within-array replicate spot genewise variance control spot usual practice sample size full use spike-in experiment limma software package valuable information triplicate result common value common correlation method result separate linear model spiked-in rna empirical bayes method expression data gene-wise variability cran repository empirical bayes smoothing thereby improves inference method between-replicate correlation current method

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