A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis

Junfeng Chen¹ , Vikas Agrawal² , Magnus Rattray¹ , Marilyn AL West³ , Dina A St Clair³ , Richard W Michelmore⁴ , Sean J Coughlan⁵ and Blake C Meyers²

¹School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

²Department of Plant and Soil Sciences, and Delaware Biotechnology Institute, University of Delaware, Newark, Delaware 19714, USA

³Plant Sciences Department, University of California-Davis, One Shields Ave, Davis, CA 95616, USA

⁴The Genome Center & the Department of Plant Sciences, University of California-Davis, One Shields Ave, Davis, CA 95616, USA

⁵Agilent Technologies Inc., Little Falls Site, 2850 Centerville Road, Wilmington, DE 19808-1644, USA

author email corresponding author email

BMC Genomics 2007, 8:414doi:10.1186/1471-2164-8-414


Published:	12 November 2007

Abstract

Background

Several high-throughput technologies can measure in parallel the abundance of many mRNA transcripts within a sample. These include the widely-used microarray as well as the more recently developed methods based on sequence tag abundances such as the Massively Parallel Signature Sequencing (MPSS) technology. A comparison of microarray and MPSS technologies can help to establish the metrics for data comparisons across these technology platforms and determine some of the factors affecting the measurement of mRNA abundances using different platforms.

Results

We compared transcript abundance (gene expression) measurement data obtained using Affymetrix and Agilent microarrays with MPSS data. All three technologies were used to analyze the same set of mRNA samples; these samples were extracted from various wild type Arabidopsis thaliana tissues and floral mutants. We calculated correlations and used clustering methodology to compare the normalized expression data and expression ratios across samples and technologies. Abundance expression measurements were more similar between different samples measured by the same technology than between the same sample measured by different technologies. However, when expression ratios were employed, samples measured by different technologies were found to cluster together more frequently than with abundance expression levels.

Furthermore, the two microarray technologies were more consistent with each other than with MPSS. We also investigated probe-position effects on Affymetrix data and tag-position effects in MPSS. We found a similar impact on Affymetrix and MPSS measurements, which suggests that these effects were more likely a characteristic of the RNA sample rather than technology-specific biases.

Conclusion

Comparisons of transcript expression ratios showed greater consistency across platforms than measurements of transcript abundance. In addition, for measurements based on abundances, technology differences can mask the impact of biological differences between samples and tissues.