Using control genes to correct for unwanted variation in microarray data

Gagnon-Bartsch, Johann A.; Speed, Terence P.

doi:10.1093/biostatistics/kxr034

Cited by 425 publications

(530 citation statements)

References 21 publications

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“…These were normalised out using a cyclic loess [19] strategy as described in Additional File 1. This normalisation appeared to be more suitable than RUV [20] and SVA [21] improving concordance with microarray results as seen in Additional Figures 1 and 2 in Additional File 1 .…”

Section: Methodssupporting

confidence: 69%

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information

et al. 2013

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BackgroundThe cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation.ResultsWe have proposed a novel approach called Tshrink+ for inferring differential gene expression through improved modelling of the gene-wise variances. Existing methods share information between genes of similar average expression by shrinking, or moderating, the gene-wise variances to a fitted common variance. We have been able to achieve improved estimation of the common variance by using gene-wise sample variances from external experiments, as well as gene length.ConclusionsUsing biological data we show that utilising additional external information can improve the modelling of the common variance and hence the calling of differentially expressed genes. These sources of additional information include gene length and gene-wise sample variances from other RNA-Seq and microarray datasets, of both related and seemingly unrelated tissue types. The results of this are promising, with our differential expression test, Tshrink+, performing favourably when compared to existing methods such as DESeq and edgeR when considering both gene ranking and sensitivity. These improved variance models could easily be implemented in both DESeq and edgeR and highlight the need for a database that offers a profile of gene variances over a range of tissue types and organisms.

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Section: Methodssupporting

confidence: 69%

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information

et al. 2013

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“…This has led to the development of a number of methods to control for underlying confounding factors (Leek and Storey 2007;Kang et al 2008;Listgarten et al 2010;Stegle et al 2010;Fusi et al 2012;Gagnon-Bartsch and Speed 2012). However, these methods generally cannot distinguish trans-eQTL hotspots from batch effects.…”

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confidence: 99%

The Dissection of Expression Quantitative Trait Locus Hotspots

et al. 2016

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Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues. KEYWORDS eQTL; pleiotropy; multivariate analysis; data visualization; gene expression T HERE is a long history of efforts to map the genetic loci [called quantitative trait loci (QTL)] that contribute to variation in quantitative traits in experimental organisms, particularly to learn about the etiology of disease (Broman 2001;Jansen 2007). But it remains difficult to identify the genes underlying QTL (Nadeau and Frankel 2000). There has been much interest recently in measuring gene expression in disease-relevant tissues in QTL experiments as a way to speed the process from QTL to gene (Jansen and Nap 2001;Albert and Kruglyak 2015). The genetic control of gene expression is itself of great interest.Expression quantitative trait loci (eQTL) analysis attempts to find the genomic locations that influence variation in gene expression levels [messenger RNA (mRNA) abundances]. eQTL near the genomic location of the influenced gene are called local eQTL, and eQTL far away from the influenced gene are called trans-eQTL. When a genomic region influences the expression of many genes, the region is called a trans-eQTL hotspot.eQTL hotspots have been observed in many genetic studies (e.g., Brem et al. 2002;Schadt et al. 2003;Yvert et al. 2003;Chesler et al. 2005), and they are of particular interest because gene expressions mapping to the same location may indicate the existence of a genetic regulator.Batch effects (artifacts arising from technical or environmental factors) are common in microarray experiments. This has led to the development of a number of methods to control for underlying confounding factors (Leek and Storey 2007;Kang et al. 2008;Listgarten et al. 2010;Stegle et al. 2010;Fusi et al. 2012;Gagnon-Bartsch and Speed 2012). However, these methods generally cannot distinguish trans-eQTL hotspots ...

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“…Even where an attempt has been made to summarise gene expression data, it was only applied to microarrays [121,122] and not transferred to other types of expression data. What is also needed is a method which, once devised, does not require adjustment of sample data for it to work, as has been the case previously [123][124][125]. Moreover, even unique "tissue specific genes" might be of little practical use if they are expressed at low levels and would therefore be absent in many smaller libraries or not detected in smaller size samples, after all, the general tendency is to miniaturise the assays and samples with the ultimate goal of single cell analysis [126][127][128].…”

Section: Existing Expression Data Quality Control Methods and Their Amentioning

confidence: 99%