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

Feuer R, Vlaic S, Arlt J, Sawodny O, Dahmen U, Zanger UM, Thomas M (2015) LEMming: A Linear Error Model to Normalize Parallel Quantitative Real-Time PCR (qPCR) Data as an Alternative to Reference Gene Based Methods. PLOS One 10(9), e0135852. PubMed

Abstract

Gene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔCt, 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.

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doi: 10.1371/journal.pone.0135852 PMID: 26325269