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

PloS One
Ronny FeuerMaria Thomas

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. We developed a RG independent data normalization approach based on a tailored linear error model for parallel qPCR data, called LEMming. It uses the assumption that the mean Ct values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the re...Continue Reading

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Citations

Jul 12, 2016·PLoS Pathogens·Karthickeyan Chella KrishnanMalak Kotb
Jan 23, 2016·Oncotarget·Anne DropmannNadja M Meindl-Beinker
Mar 25, 2017·Gut·Katja Breitkopf-HeinleinPeter Ten Dijke
Nov 11, 2017·Biotechnology Letters·Muhammad ShakeelFengliang Jin
Jul 28, 2016·PLoS Computational Biology·Julia RexJohannes G Bode

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Datasets Mentioned

BETA
GSE55434

Methods Mentioned

BETA
PCR
FCS
Assay
chip

Software Mentioned

limma
script
Time PCR Analysis
Real
R
DAG Expression
- Plot
package impute
LEMming
geNorm

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