Applying Data-Driven Normalization Strategies for qPCR Data Using Bioconductor

High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. qPCR is widely accepted as the ”gold standard” for analysis of gene expression. Recent technological advances have greatly expanded the total number of genes that can be analyzed in a single assay; qPCR experiments now regularly analyze ”moderate” numbers of genes, in the range of fifty to a few thousand [1-3]. However, as the size of qPCR experiments has expanded, the need for effective data normalization techniques has become increasingly apparent. Normalization is the process of adjusting the relative expression measures between samples to compensate for various sources of variability in the assay and so to allow accurate comparisons of the results between different samples and conditions. This short vignette demonstrates how to use the functions available in the package qpcrNorm. As an example, we apply these functions to an artifically generated qPCR data set. This data has been closely simulated from original qPCR data, there are Ct measures for 2396 genes on samples taken at 13 times points. Each measurement was replicated three times and each sample was split over multiple 384-well plates. This data is stored as qpcrBatch.object.