ArticleBiostatistics (Oxford, England)2012
Using control genes to correct for unwanted variation in microarray data.
Article in Biostatistics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 252 papers, 1 of them a synthesis that pooled it.
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Who cites it
252 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Comparative evaluation of HTG and TempO Seq targeted transcriptome profiling methods.Scientific reports · 2026Article
- Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer.iScience · 2026Article
- Cumulative environmental exposures adversely impact social behaviour and are associated with dysregulation of genes and proteins involved in epigenetic, ribosomal, and immune regulation in male mice.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
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- Conceptualizing Experimental Controls Using the Potential Outcomes Framework.The American statistician · 2025Article
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- ComBat-met: adjusting batch effects in DNA methylation data.NAR genomics and bioinformatics · 2025Article
- DNA-damage-associated protein co-expression network in cardiomyocytes informs on tolerance to genetic variation and disease.iScience · 2025Article
192 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Microarray expression studies suffer from the problem of batch effects and other unwanted variation. Many methods have been proposed to adjust microarray data to mitigate the problems of unwanted variation. Several of these methods rely on factor analysis to infer the unwanted variation from the data. A central problem with this approach is the difficulty in discerning the unwanted variation from the biological variation that is of interest to the researcher. We present a new method, intended for use in differential expression studies, that attempts to overcome this problem by restricting the factor analysis to negative control genes. Negative control genes are genes known a priori not to be differentially expressed with respect to the biological factor of interest. Variation in the expression levels of these genes can therefore be assumed to be unwanted variation. We name this method "Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques for assessing the performance of an adjustment method and compare the performance of RUV-2 with that of other commonly used adjustment methods such as Combat and Surrogate Variable Analysis (SVA). We present several example studies, each concerning genes differentially expressed with respect to gender in the brain and find that RUV-2 performs as well or better than other methods. Finally, we discuss the possibility of adapting RUV-2 for use in studies not concerned with differential expression and conclude that there may be promise but substantial challenges remain.
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