Gene expression Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed.Results:Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In themultiple scanapproachonly one-third of the differentially expressedgeneswere shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to <70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scansmay be best for detecting expression differences in high-signal genes, while highintensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. Availability:Thedatapresentedcanbeviewedat http://www.ncbi. nlm. nih.gov/geo/ under GEO accession no. GSE3017. Contact: schnable@iastate.edu Supplementary information: Data from these experiments can be viewed at http://www.plantgenomics. iastate.edu/microarray/data/ INTRODUCTION DNA microarrays simultaneously examine the relative abundances of thousands of transcripts in two RNA samples (Schena et al., 1995). Microarray experiments can be divided into seven steps. Much has been published regarding experimental design (Churchill, 2002; Kerr, 2003; Kerr and Churchill, 2001; Simon and Dobbin, 2003; Yang and Speed, 2002), array production (Diehl et al., 2001; Rickman et al., 2003; Taylor et al., 2003), RNA isolation and amplification (Baugh et al., 2001; Luo et al., 1999; Naderi et al., 2004; Pabon et al., 2001; Van Gelder et al., 1990; Wilson et al., 2004), labeling and hybridization considerations (Heller et al., 1997; Yue et al., 2001), and downstream data analyses (Leung and Cavalieri, 2003; Quackenbush, 2002; Slonim, 2002; Wolfinger et al., 2001). However, an often-overlooked aspect of microarray experiments is post-hybridization data acquisition. Although the seminal microarray publication (Schena et al., 1995) described a data acquisition strategy using two different laser settings, this approach is not used in typical microarray protocols. Instead, most protocols recommend scanning one time per channel at settings that minimize the number of saturated spots (Hegde et al., 2000; Leung and Cavalieri, 2003). While this approach captures a subset of the statistically significant differences, it potentially excludes genes on the basis of signal intensity. Duggan et al. (1999) reported that one of the limiting factors in microarray experiments is signal detection for low-signal spots. Indeed, scan intensities necessary for preventing saturation of high-signal genes may prove inadequate for the detection of differential expression in low-signal genes. Procedures that correct saturated spots have been explored by Dudley et al. (2002) and Dodd et al. (2004). Although both procedures extend the dynamic range of the acquired data, differences in gene expression may be undetectable when the data are analyzed as a single set. An alternative approach to obtain more gene expression information is to independently analyze the datasets collected at different scan settings, and subsequently combine all the analyses. This approach was applied to an experiment aimed at identifying differences in transcript abundance in developing To whom correspondence should be addressed: Patrick S. Schnable, 2035B Roy J. Carver Co-Lab, Iowa State University, Ames, IA 50011-3650, USA. Tel: +1 515 294 0975; Fax: +1 515 294 5256; Email: schnable@iastate.edu Present address: Stanford University, 385 Serra Mall, Stanford, CA 943055020, USA 2006 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. D ow naded rom http/academ ic.p.com /binform atics/article-t/22/15/1863/242997 by gest on 04 M ay 2019 maize anthers. The tassel, which is the male reproductive structure of maize, bears spikelets that contain anthers that proceed through a series of morphologically defined developmental stages and ultimately produce pollen. Here we describe the analysis of multiple datasets produced by scanning each of several microarrays at multiple intensities. Our work was motivated by the hypothesis that the scan intensity required to achieve resolution necessary for detecting differential expression is inversely related to a gene’s signal intensity. Thus, images containing low, intermediate and high numbers of saturated spots would be expected to detect differences in gene expression among genes that exhibit high-, mediumand low-signal strengths, respectively. To test this hypothesis empirically, three datasets, spanning a 20-fold difference in average signal strength, were analyzed to identify the number of statistically significant differences detected and the degree of overlap among the three datasets. Additionally, the multiple-scan images were analyzed using the linear regression (LR) procedure described by Dudley et al. (2002). Analysis of the multiple-scan result set supports our hypothesis and demonstrates that scanning at multiple intensities can play an important role in acquiring data for microarray analyses. Furthermore, the comparison between the datasets of the multiple-scan and the linear regression approaches demonstrates that the multiple-scan method identifies statistically significant differences in gene expression that the linear regression is unable to identify.