Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures

Sarah A. Munro (), Steven P. Lund, P. Scott Pine, Hans Binder, Djork-Arné Clevert, Ana Conesa, Joaquin Dopazo, Mario Fasold, Sepp Hochreiter, Huixiao Hong, Nadereh Jafari, David P. Kreil, Paweł P. Łabaj, Sheng Li, Yang Liao, Simon M. Lin, Joseph Meehan, Christopher E. Mason, Javier Santoyo-Lopez, Robert A. Setterquist, Leming Shi, Wei Shi, Gordon K. Smyth, Nancy Stralis-Pavese, Zhenqiang Su, Weida Tong, Charles Wang, Jian Wang, Joshua Xu, Zhan Ye, Yong Yang, Ying Yu and Marc Salit ()
Additional contact information
Sarah A. Munro: National Institute of Standards and Technology
Steven P. Lund: National Institute of Standards and Technology
P. Scott Pine: National Institute of Standards and Technology
Hans Binder: Interdisciplinary Centre for Bioinformatics, University of Leipzig
Djork-Arné Clevert: Institute of Bioinformatics, Johannes Kepler University
Ana Conesa: Computational Genomics Program, Principe Felipe Research Center
Joaquin Dopazo: Computational Genomics Program, Principe Felipe Research Center
Mario Fasold: ecSeq Bioinformatics
Sepp Hochreiter: Institute of Bioinformatics, Johannes Kepler University
Huixiao Hong: National Center for Toxicological Research, Food and Drug Administration
Nadereh Jafari: Genomics Core Facility, Feinberg School of Medicine, Northwestern University
David P. Kreil: Chair of Bioinformatics, Boku University Vienna
Paweł P. Łabaj: Chair of Bioinformatics, Boku University Vienna
Sheng Li: Institute for Computational Biomedicine, Weill Cornell Medical College
Yang Liao: The Walter and Eliza Hall Institute of Medical Research
Simon M. Lin: Nationwide Children's Hospital
Joseph Meehan: National Center for Toxicological Research, Food and Drug Administration
Christopher E. Mason: Institute for Computational Biomedicine, Weill Cornell Medical College
Javier Santoyo-Lopez: CIBER de Enfermedades Raras (CIBERER) and Functional Genomics Node, INB.
Robert A. Setterquist: Thermo Fisher Scientific, Research & Development
Leming Shi: State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University
Wei Shi: The Walter and Eliza Hall Institute of Medical Research
Gordon K. Smyth: The Walter and Eliza Hall Institute of Medical Research
Nancy Stralis-Pavese: Chair of Bioinformatics, Boku University Vienna
Zhenqiang Su: National Center for Toxicological Research, Food and Drug Administration
Weida Tong: National Center for Toxicological Research, Food and Drug Administration
Charles Wang: Center for Genomics, School of Medicine, Loma Linda University
Jian Wang: Research Informatics, Eli Lilly and Company, Lilly Corporate Center
Joshua Xu: National Center for Toxicological Research, Food and Drug Administration
Zhan Ye: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation
Yong Yang: Research Informatics, Eli Lilly and Company, Lilly Corporate Center
Ying Yu: State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University
Marc Salit: National Institute of Standards and Technology

Nature Communications, 2014, vol. 5, issue 1, 1-10

Abstract: Abstract There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ‘dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Date: 2014
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DOI: 10.1038/ncomms6125

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