Scaling of Gene Expression Data Allowing the Comparison of Different Gene Expression Platforms

Abstract

Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the results from different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance using different overall correlation approaches, such as Up/Down classification, contingency tables, and correlation coefficients. Here, we demonstrate an approach to compare two platforms based on the calculation of the difference between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene, as opposed to the commonly used overall concordance measures between platforms. This between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to identify transcripts with similar regulation on both platforms.