The first principal component (PC1) was used to assess the major pattern of gene expression variability in the dataset. Genes that were highly correlated with PC1 were used to build a surrogate biomarker. Throughout this work we used Pearson correlation coefficients, ρ, and assessed their significance, p, assuming normal distribution for Fisher z-transformed values, atanh ρ [28]. Significant differential expression for each gene was evaluated using t-test p-values [28]. Multiple-testing correction of p-values was done according to Benjamini-Hochberg procedure to obtain false-discovery rates (FDR) [29]. These analyses were performed using Statistical Toolbox of MATLAB R2007a (Mathworks Inc. Natick, MA).
