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 Kreil, Pawel P. Labaj, Shengh Li, Yang Liao, Simon M. Lin, Joseph Meehan, Christopher E. Mason, Javier Santoyo-Lopez, Robert A. SetterquistLeming 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, Marc Salit

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number5125
Number of pages10
Journalnature communications
Volume5
DOIs
Publication statusPublished - Sept 2014

Fields of science

  • 303 Health Sciences
  • 304 Medical Biotechnology
  • 304003 Genetic engineering
  • 305 Other Human Medicine, Health Sciences
  • 101004 Biomathematics
  • 101018 Statistics
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102004 Bioinformatics
  • 102010 Database systems
  • 102015 Information systems
  • 102019 Machine learning
  • 106023 Molecular biology
  • 106002 Biochemistry
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 106041 Structural biology
  • 301 Medical-Theoretical Sciences, Pharmacy
  • 302 Clinical Medicine

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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