LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods

, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs. calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.If RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors