Using control genes to correct for unwanted variation in microarray data.

By Johann A. Gagnon-Bartsch, Terence P. Speed. Biostatistics, 2012

439 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5684109aa873a00f93168edbfffafd9a4ef4813a

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In Molecular Biology

Citations in the corpus

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution (2016, methodology): “For TWAS, we additionally used RUV [27, 32] and SVA [26].”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “The main axes of variation over these genes are designated as a “technical variability” component that is then either subtracted from the data or taken into account in the downstream analysis 48,49,68 .”
Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, methodology): “First we applied the Remove Unwanted Variation (RUV) method [22], an analysis that estimates and adjusts for unknown surrogate variables as done by Leek and Storey [23].”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, methodology): “Here R and Bioconductor are adopted to tie together the workflow and provide data structures, and their unique strengths in workflow components are leveraged, including statistical algorithms, visualization”
Functional normalization of 450k methylation array data improves replication in large cancer studies (2014, methodology): “To determine the number k of factors to remove (see Gagnon-Bartsch and Speed [28] for details of this parameter), we followed the approach described in [28].”
svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “The first extension, which I call supervised surrogate variable analysis (ssva) was introduced by Gagnon-Bartsch and colleagues [5].”
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “…analyses, and further allow ‘cor-rected’ residual gene expression data sets to be generated. scLVM is closely related to previous approaches that correct for hidden confounding factors in gene expression data 21,48 and the inference employed to fit hidden factors builds on the PANAMA model 30 .”
Bi-cross-validation for factor analysis (2015, background): “ing [59, 25], bioinformatics [52, 48, 37, 56, 21], finance”
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “It will be interesting to explore if further improvements are possible, for instance, using predicted cell-type specific DMCs [42], although we anticipate that such prior information will turn out to be more fruitful in the context of “semi-reference-free” approaches such as RUV [43].”
Harmonization of multi-site diffusion tensor imaging data (2017, methodology): “The FA values in the CSF can therefore act a surrogates for site effects in the WM. Similar to RUV [Gagnon-Bartsch and Speed, 2012], we use singular value decomposition (SVD) to obtain k latent factors of unwanted variation, denoted w1, w2,…, wk, estimated from the CSF control voxels.”
Batch effect removal methods for microarray gene expression data integration: a survey (2013, methodology): “The authors in [28] propose a quality metric to measure the effectiveness of a batch effect removal method.”
Spectral Deconfounding and Perturbed Sparse Linear Models (2018, background): “Adjusting for the confounding variables is very important in practice and several deconfounding methods have been suggested for various settings [8, 17, 7, 22, 27].”