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

Author

Listed:
  • Sarah A. Munro

    (National Institute of Standards and Technology
    Stanford University)

  • Steven P. Lund

    (National Institute of Standards and Technology)

  • P. Scott Pine

    (National Institute of Standards and Technology
    Stanford University)

  • 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
    CIBER de Enfermedades Raras (CIBERER) and Functional Genomics Node, INB.)

  • 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
    University of Warwick)

  • 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
    The University of Melbourne)

  • 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.
    Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia)

  • 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
    The University of Melbourne)

  • Gordon K. Smyth

    (The Walter and Eliza Hall Institute of Medical Research
    The University of Melbourne)

  • Nancy Stralis-Pavese

    (Chair of Bioinformatics, Boku University Vienna)

  • Zhenqiang Su

    (National Center for Toxicological Research, Food and Drug Administration
    Present address: Discovery Science, Thomson Reuters IP & Science, 22 Thomson Place, Boston, Massachusetts 02210, USA)

  • 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
    Stanford University)

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.

Suggested Citation

  • 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 & Paw, 2014. "Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures," Nature Communications, Nature, vol. 5(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6125
    DOI: 10.1038/ncomms6125
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    Cited by:

    1. Duo Wang & Yaqing Liu & Yuanfeng Zhang & Qingwang Chen & Yanxi Han & Wanwan Hou & Cong Liu & Ying Yu & Ziyang Li & Ziqiang Li & Jiaxin Zhao & Leming Shi & Yuanting Zheng & Jinming Li & Rui Zhang, 2024. "A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    2. Mattia Zaghi & Federica Banfi & Luca Massimino & Monica Volpin & Edoardo Bellini & Simone Brusco & Ivan Merelli & Cristiana Barone & Michela Bruni & Linda Bossini & Luigi Antonio Lamparelli & Laura Pi, 2023. "Balanced SET levels favor the correct enhancer repertoire during cell fate acquisition," Nature Communications, Nature, vol. 14(1), pages 1-21, December.

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