Run Batch Effects Potentially Compromise the Usefulness of Genomic Signatures for Ovarian Cancer

Baggerly, Keith A.; Coombes, Kevin R.; Neeley, E. Shannon

doi:10.1200/jco.2007.15.1951

Cited by 66 publications

(54 citation statements)

References 4 publications

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“…(Bonnefoi et al, 2011;Potti et al, 2011) Furthermore, this research group and critical review by another research group have confirmed that the dataset that was used in the present meta-analysis was indeed correctly annotated. (Baggerly, Coombes, & Neeley, 2008;Dressman et al, 2007) With the availability of more datasets, we noticed variation among pathway's association with survival outcome.…”

Section: Discussionmentioning

confidence: 99%

Oncogenic Pathway Signatures and Survival Outcome

Trinh¹,

Dam²,

Dirix³

et al. 2012

Ovarian Cancer - Basic Science Perspective

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Section: Discussionmentioning

confidence: 99%

Oncogenic Pathway Signatures and Survival Outcome

Trinh¹,

Dam²,

Dirix³

et al. 2012

Ovarian Cancer - Basic Science Perspective

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“…However, normalization techniques do not control for batch effects caused by technical artifacts. These batch effects require additional correction techniques (Scherer 2009) and failure to do so has led to spurious associations (Spielman and Cheung 2007;Baggerly et al 2008).Many different correction and normalization techniques are currently used in gene expression studies (for review see Chen et al 2011;Qin et al 2012). Principal components analysis (PCA) is one method that has been used for the correction of widespread batch effects (Leek and Storey Clearly PCA is a powerful tool to analyze and understand high-dimension gene expression data.…”

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confidence: 99%

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confidence: 99%

Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression

et al. 2013

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Principal components analysis has been employed in gene expression studies to correct for population substructure and batch and environmental effects. This method typically involves the removal of variation contained in as many as 50 principal components (PCs), which can constitute a large proportion of total variation present in the data. Each PC, however, can detect many sources of variation, including gene expression networks and genetic variation influencing transcript levels. We demonstrate that PCs generated from gene expression data can simultaneously contain both genetic and nongenetic factors. From heritability estimates we show that all PCs contain a considerable portion of genetic variation while nongenetic artifacts such as batch effects were associated to varying degrees with the first 60 PCs. These PCs demonstrate an enrichment of biological pathways, including core immune function and metabolic pathways. The use of PC correction in two independent data sets resulted in a reduction in the number of cis-and transexpression QTL detected. Comparisons of PC and linear model correction revealed that PC correction was not as efficient at removing known batch effects and had a higher penalty on genetic variation. Therefore, this study highlights the danger of eliminating biologically relevant data when employing PC correction in gene expression data. GENE expression profiling has become a very popular technique used to quantify regulatory changes in messenger (m)RNA expression associated with disease and environmental factors. Gene expression acts as an intermediate phenotype between genotypes and complex traits and is known to act as a modifier to disease susceptibility (Nica and Dermitzakis 2008;Li et al. 2012). Genetic variation underlying gene expression levels has been well established and reported within the literature, with the transcript levels for the majority of genes being heritable to some degree (Price et al. 2011;Grundberg et al. 2012;Powell et al. 2012b).Microarray technology can simultaneously capture the expression of thousands of transcripts within an individual.However, these arrays are sensitive to environmental or experimental perturbations, for example due to different laboratory technicians and reagents (Churchill 2002;Irizarry et al. 2005), microarray chip and chip position (Luo et al. 2010), temperature (Scherer 2009), and even ozone levels (Thomas et al. 2003). These effects can constitute a substantial proportion of variance within a data set (Leek et al. 2010).Normalization strategies have become standard in gene expression studies to correct for nonnormal distributions and inconsistencies between arrays ( Allison et al. 2006). However, normalization techniques do not control for batch effects caused by technical artifacts. These batch effects require additional correction techniques (Scherer 2009) and failure to do so has led to spurious associations (Spielman and Cheung 2007;Baggerly et al. 2008).Many different correction and normalization techniques are currently us...

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“…These variables can then be included in statistical models to remove any batch effects [154,[158][159][160]. Failure to correct for batch effects has led to spurious results in downstream analyses [161,162].…”

Section: Batch Effectsmentioning

confidence: 99%

“…However, normalization techniques do not control for batch effects caused by technical artifacts. These batch effects require additional correction techniques [154] and failure to do so has led to spurious associations [161,162].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the genetic and environmental factors driving gene expression variability

Goldinger¹

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Gene expression variation is a quantitative trait that drives phenotypic diversity across populations.On a cellular level, gene expression is an intermediate phenotype between stored genetic information and the functional utilization of this information within the cell. Through Genome Wide Association Studies (GWAS), thousands of genetic polymorphisms associated with numerous diseases have been identified. These have provided many novel insights into the disrupted biological processes that drive the etiology of various health conditions.expression Quantitative Trait Loci (eQTLs) provide an additional layer of biological information about the physiological impact of common genetic variants.Therefore, the study of the genetic regulation of gene expression (eQTL studies) has been useful both in the validation and functional characterisation of GWAS polymorphisms. This has contributed to a better understanding of the precise molecular processes that contribute to the development of disease.Global transcriptomic analyses have provided as greater insight into the level of complexity that drives biological systems. Transcriptomic data are often comprised of gene regulatory and co-expression networks, an emergent property of transcriptomic and other omic data. These networks within each omics fields interact with each other to further add layers of complexity that drive biological systems.Variation contained with gene expression datasets can, therefore, provide detail into the flow of information through these biological systems and how these can be influenced by genetic polymorphisms.Transcriptome variation is highly influenced by genetic and environmental factors. Genetic regulation of gene expression represents, with some exceptions, fixed regulatory points that strictly control the expression of genes. Variance attributed to environmental effects, on the other hand, are often biological responses to specific stimuli. The dissection of the genetic and environmental influences on expression levels will help to form a baseline upon which network models can be built to disseminate the biological flow of information in healthy, latent or disease groups. Booth, "The low EOMES/TBX21 molecular phenotype in multiple sclerosis reflects CD56+ cell dysregulation and is affected by immunomodulatory therapies

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Run Batch Effects Potentially Compromise the Usefulness of Genomic Signatures for Ovarian Cancer

Cited by 66 publications

References 4 publications

Oncogenic Pathway Signatures and Survival Outcome

Oncogenic Pathway Signatures and Survival Outcome

Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression

Characterization of the genetic and environmental factors driving gene expression variability

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