Integrative factor analysis - An unsupervised method for quantifying cross-study consistency of gene expression data

Integrative analyses of multiple gene expression studies are frequently performed. In the setting of two studies, integrative correlation (IGC) can be used to assess the consistency of co-expression of a given gene. For three or more studies, an extension of IGC gives a global score per gene. We propose to extend IGC and use factor analysis to assess the study-specific consistency of co-expression of genes when there are three or more studies, possibly on different platforms. Our method is able to identify studies whose expression patterns are different from others. Filtering genes based on our score is shown to improve the concordance of association with phenotype across studies.