On non-detects in qPCR data.
BACKGROUND
Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects-those reactions failing to produce a minimum amount of signal.


RESULTS
We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression.


METHODS
The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project.
Department of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Matthew N
MN
McCall
Department of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Helene R
HR
McMurray
Department of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USADepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Hartmut
H
Land
Department of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Anthony
A
Almudevar
D000465:Algorithms; D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D051379:Mice; D008962:Models, Theoretical; D060888:Real-Time Polymerase Chain Reaction / Q000379:methods*; D012984:Software