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. 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

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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 (US)
Article number5125
JournalNature communications
Volume5
DOIs
StatePublished - 2014
Externally publishedYes

Bibliographical note

Funding Information:
We thank David L. Duewer and Jerod Parsons for review of the manuscript, Cecelie Boysen for discussion of results and all other members of the SEQC consortium who supported this work. P.P.Ł., N.S.-P. and D.P.K. acknowledge the support from the Vienna Scientific Cluster (VSC), the Vienna Science and Technology Fund (WWTF), Baxter AG, Austrian Research Centres (ARC) Seibersdorf and the Austrian Centre of Biopharmaceutical Technology (ACBT). J.D. was supported by grant BIO2011–27069 from the Spanish Ministry of Economy and Competitiveness (MINECO). L.S. and Y.Yu acknowledge support from China’s National Supercomputing Center in Guangzhou.

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