Tackling the widespread and critical impact of batch effects in high-throughput data

Batch effects can lead to incorrect biological conclusions but are not widely considered. The authors show that batch effects are relevant to a range of high-throughput 'omics' data sets and are crucial to address. They also explain how batch effects can be mitigated. High-throughput techn...

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Bibliographic Details
Published in:Nature reviews. Genetics Vol. 11; no. 10; pp. 733 - 739
Main Authors: Leek, Jeffrey T., Scharpf, Robert B., Bravo, Héctor Corrada, Simcha, David, Langmead, Benjamin, Johnson, W. Evan, Geman, Donald, Baggerly, Keith, Irizarry, Rafael A.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.10.2010
Nature Publishing Group
Subjects:
631/1647/1513
631/1647/48
Agriculture
Animal Genetics and Genomics
Biological and medical sciences
Biology
Biomedical and Life Sciences
Biomedicine
Biotechnology - methods
Biotechnology - standards
Biotechnology - statistics & numerical data
Cancer Research
Computational Biology - methods
Diabetes
DNA methylation
DNA microarrays
DNA sequencing
Fundamental and applied biological sciences. Psychology
Gene Function
Genes
Genetics of eukaryotes. Biological and molecular evolution
Genomes
Genomics
Genomics - methods
Genomics - standards
Genomics - statistics & numerical data
Human Genetics
Nucleotide sequencing
Oligonucleotide Array Sequence Analysis - methods
Oligonucleotide Array Sequence Analysis - standards
Oligonucleotide Array Sequence Analysis - statistics & numerical data
opinion-2
Periodicals as Topic - standards
Polymerase chain reaction
Quantitative genetics
Research Design - standards
Research Design - statistics & numerical data
Sequence Analysis, DNA - methods
Sequence Analysis, DNA - standards
Sequence Analysis, DNA - statistics & numerical data
Stem cells
United States
Review
ISSN:1471-0056, 1471-0064, 1471-0064
Online Access:Get full text
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Description
Summary:Batch effects can lead to incorrect biological conclusions but are not widely considered. The authors show that batch effects are relevant to a range of high-throughput 'omics' data sets and are crucial to address. They also explain how batch effects can be mitigated. High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so.
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ISSN:1471-0056
1471-0064
1471-0064
DOI:10.1038/nrg2825