MOTIVATION: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA cannot be applied to such data. Moreover, the size of the data in genetic studies can be enormous, thereby making the results difficult to interpret. RESULTS: We developed a penalized non-linear CCA approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable through optimal scaling. Additionally, sparse results were obtained by adapting soft-thresholding to this non-linear version of the CCA. By means of simulation studies, we show that our method is capable of extracting relevant variables out of high-dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer. CONTACT: [email protected].

BACKGROUND: We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. RESULTS: We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. CONCLUSION: We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.