Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks.
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.


CONCLUSIONS
We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.
Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.
Sandra
S
Waaijenborg
Aeilko H
AH
Zwinderman
D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks / Q000235:genetics*; D005909:Glioblastoma / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015398:Signal Transduction