SCOREM: statistical consolidation of redundant expression measures

Abstract

Many platforms for genome-wide analysis of gene expression contain ‘redundant’ measures for the same gene. For example, the most highly utilized platforms for gene expression microarrays, Affymetrix GeneChip® arrays, have as many as ten or more probe sets for some genes. Occasionally, individual probe sets for the same gene report different trends in expression across experimental conditions, a situation that must be resolved in order to accurately interpret the data. We developed an algorithm, SCOREM, for determining the level of agreement between such probe sets, utilizing a statistical test of concordance, Kendall's W coefficient of concordance, and a graph-searching algorithm for the identification of concordant probe sets. We also present methods for consolidating concordant groups into a single value for its corresponding gene and for post hoc analysis of discordant groups. By combining statistical consolidation with sequence analysis, SCOREM possesses the unique ability to identify biologically meaningful discordant behaviors, including differing behaviors in alternate RNA isoforms and tissue-specific patterns of expression. When consolidating concordant behaviors, SCOREM outperforms other methods in detecting both differential expression and overrepresented functional categories.