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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Vydáno v:	Nature reviews. Genetics Ročník 11; číslo 10; s. 733 - 739
Hlavní autoři:	Leek, Jeffrey T., Scharpf, Robert B., Bravo, Héctor Corrada, Simcha, David, Langmead, Benjamin, Johnson, W. Evan, Geman, Donald, Baggerly, Keith, Irizarry, Rafael A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 01.10.2010 Nature Publishing Group
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Feature-3 content type line 23 ObjectType-Review-2 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1471-0056 1471-0064 1471-0064
DOI:	10.1038/nrg2825