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Nat Commun. 2014 Sep 25;5:5125. doi: 10.1038/ncomms6125.

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

Author information

1
1] National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, USA [2] Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, California 94305, USA.
2
National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, USA.
3
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany.
4
Institute of Bioinformatics, Johannes Kepler University, Altenberger Str. 69, 4040 Linz, Austria.
5
Computational Genomics Program, Principe Felipe Research Center, Avd Eduardo Primo Yúfera 3, 46012 Valencia, Spain.
6
1] Computational Genomics Program, Principe Felipe Research Center, Avd Eduardo Primo Yúfera 3, 46012 Valencia, Spain [2] CIBER de Enfermedades Raras (CIBERER) and Functional Genomics Node, INB., Valencia, Spain.
7
ecSeq Bioinformatics, Brandvorwerkstrasse 43, 04275 Leipzig, Germany.
8
National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA.
9
Genomics Core Facility, Feinberg School of Medicine, Northwestern University, Tarry building 2-757, 300 E. Superior St. Chicago, Illinois 60611, USA.
10
1] Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, Vienna 1190, Austria [2] University of Warwick, Coventry CV4 7AL, UK.
11
Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, Vienna 1190, Austria.
12
Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, Room Y13-04, Box 140, New York, New York 10021, USA.
13
1] Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia [2] Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia.
14
Nationwide Children's Hospital, Columbus, Ohio 43205, USA.
15
1] CIBER de Enfermedades Raras (CIBERER) and Functional Genomics Node, INB., Valencia, Spain [2] Medical Genome Project, Genomics and Bioinformatics Platform of Andalusia, c/ Albert Einstein s/n, 41092 Sevilla, Spain.
16
Thermo Fisher Scientific, Research &Development, 2170 Woodward Street, Austin, Texas 78744, USA.
17
State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Schools of Life Sciences and Pharmacy, Fudan University, Shanghai 201203, China.
18
1] Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia [2] Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia.
19
1] Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia [2] Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia.
20
Division of Microbiology and Molecular Genetics, Center for Genomics, School of Medicine, Loma Linda University, Loma Linda, California 92350, USA.
21
Research Informatics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana 46285, USA.
22
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 N Oak Avenue, Marshfield, Wisconsin 54449, USA.

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.

PMID:
25254650
DOI:
10.1038/ncomms6125
[Indexed for MEDLINE]

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