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• W2039521726 abstract "Remove unwanted variation (RUV) is a new statistical method for RNA-seq data normalization that uses control genes or samples to improve differential expression analysis. Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more complex unwanted technical effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple laboratories, technicians, and/or sequencing platforms." @default.
• W2039521726 created "2016-06-24" @default.
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• W2039521726 creator A5006317886 @default.
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• W2039521726 date "2014-08-24" @default.
• W2039521726 modified "2023-10-18" @default.
• W2039521726 title "Normalization of RNA-seq data using factor analysis of control genes or samples" @default.
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• W2039521726 doi "https://doi.org/10.1038/nbt.2931" @default.
• W2039521726 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4404308" @default.
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• W2039521726 hasPublicationYear "2014" @default.
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