Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data

Abstract

Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem. As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective. Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

Figure 1. The boxplots of all 17 arrays from S-PPAR datasets

X1–X5 refer to data from the expression arrays and they show different distributions from X6–X17 plots of the exon arrays. This plot is generated by the boxplot() function of R.

Figure 2. Demonstration of Coex-Rank approach

Gene_a and Gene_a′ are two assumed genes. Gene_a is a top-ranked gene on all input lists for Coex-Rank processing, but Gene_a′ is only present at the bottom of some of the input lists or even absent from some fo the input lists. Coex-Rank approach prioritizes Gene_a′ because it is highly correlated with already-top-ranked Gene_a.