Correspondence should be addressed to:
Genetics Department, Albert Einstein College of Medicine, New York, New York, 10461, USA
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.