Support vector machine quantile regression for detecting differentially expressed genes in microarray analysis

One of the main objectives of microarray analysis is to identify genes differentially expressed under two distinct experimental conditions. This task is complicated by the noisiness of data and the large number of genes that are examined. Fold change (FC) based gene selection often misleads because error variability for each gene is heterogeneous in different intensity ranges. Several statistical methods have been suggested, but some of them result in high false positive rates because they make very strong parametric assumptions.

We present support vector quantile regression (SVMQR) using iterative reweighted least squares (IRWLS) procedure based on the Newton method instead of usual quadratic programming algorithms. This procedure makes it possible to derive the generalized approximate cross validation (GACV) method for choosing the parameters which affect the performance of SVMAR. We propose SVMQR based on a novel method for identifying differentially expressed genes with a small number of replicated microarrays.

We applied SVMQR to both three biological dataset and simulated dataset and showed that it performed more reliably and consistently than FC-based gene selection, Newton's method based on the posterior odds of change, or the nonparametric t-test variant implemented in significance analysis of microarrays (SAM).

The SVMQR method was an exploratory method for cDNA microarray experiments to identify genes with different expression levels between two types of samples (e.g., tumor versus normal tissue). The SVMQR method performed well in the situation where error variability for each gene was heterogeneous in intensity ranges.