Data-driven normalization strategies for high-throughput quantitative RT-PCR.

ABSTRACT:

Background

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. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.

Results

We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method

SUBMITTER: Mar JC 

PROVIDER: S-EPMC2680405 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature