In whole genome microarray studies major gene expression changes are easily identified, but it is a challenge to capture small, but biologically important, changes. Pathway-based programs can capture small effects but may have the disadvantage of being restricted to functionally annotated genes. A structured approach toward the identification of major and small changes for interpretation of biological effects is needed. We present a structured approach, a framework, that addresses different considerations in 1) the identification of informative genes in microarray data sets and 2) the interpretation of their biological relevance. The steps of this framework include gene ranking, gene selection, gene grouping, and biological interpretation. Random forests (RF), which takes gene-gene interactions into account, is examined to rank and select genes. For human, mouse, and rat whole genome arrays, less than half of the probes on the array are annotated. Consequently, pathway analysis tools ignore half of the information present in the microarray data set. The framework described takes all genes into account. RF is a useful tool to rank genes by taking interactions into account. Applying a permutation approach, we were able to define an objective threshold for gene selection. RF combined with self-organizing maps identified genes with coordinated but small gene expression responses that were not fully annotated but corresponded to the same biological process. The presented approach provides a flexible framework for biological interpretation of microarray data sets. It includes all genes in the data set, takes gene-gene interactions into account, and provides an objective threshold for gene selection.
|Publication status||Published - 1 Jan 2008|