Normalization of cDNA microarray data.

By Gordon K. Smyth, Terence P. Speed. Methods, 2003

2,190 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5c0394a8130f4d686b4a578e5fcd895fabde498a

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In Molecular Biology

Citations in the corpus

Use of within-array replicate spots for assessing differential expression in microarray experiments (2005, methodology): “Foreground intensities were background corrected using the ‘morph’ background measure and the scorecard spot log-ratios were normalized using global loess normalization with the default smoothing span of 0.3 (Yang et al., 2001; Smyth and Speed, 2003).”
Long noncoding RNAs in mouse embryonic stem cell pluripotency and differentiation. (2008, methodology): “Data were background-corrected, normalized both within and between arrays (Smyth and Speed 2003), and differential expression analysis was performed by fitting a linear model of the data to the experimental design matrix and then calculating Bayesian statistics (B-statistics; posterior log odds) adj”
Controlling the number of false discoveries: application to high-dimensional genomic data (2004, methodology): “We note that recent statistical work has described more complex alternatives to median normalization to normalize gene expression data (Yang et al., 2001; Chen et al., 1997).”
Manipulation of thyroid status and/or GH injection alters hepatic gene expression in the juvenile chicken (2007, methodology): “Print-tip Loess adjustment was used within arrays to linearize signal across intensity (Smyth and Speed, 2003).”
Leukocyte Complexity Predicts Breast Cancer Survival and Functionally Regulates Response to Chemotherapy (2011, methodology): “For the dual-channel platforms, data were loess normalized (7) using the Bioconductor package limma.”
Evaluation of time profile reconstruction from complex two-color microarray designs (2008, methodology): “tion between arrays [11], and 3) print tip Loess with a quantile-based between array normalization [12,13] (the default normalization for lmbr, lmbr_dye, and limmaQual as used throughout this paper).”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, background): “Typically, in the process of normalizing microarray data, an attempt is made to remove systematic correlations that can occur due to the microarrayto-microarray variability that systematically affects all genes on the microarray (Yang et al., 2001).”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “Panel (C) was generated from a subset of 30 of the control arrays after print-tip loess normalization (12).”
Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. (2008, methodology): “(Fraley, 2003, 2002; Smyth, 2004; Smyth and Speed, 2003) and”
Analysis of array CGH data: from signal ratio to gain and loss of DNA regions (2004, background): “In practice, microarray experiments are subject to various sources of variation, including differences in incorporation efﬁciency between the two ﬂuorescent dyes, an intensity-dependent effect, and a print-tip effect, as reported by Yang et al. (2001) for expression data.”
ABA Is an Essential Signal for Plant Resistance to Pathogens Affecting JA Biosynthesis and the Activation of Defenses in Arabidopsis[W] (2007, methodology): “The resulting log ratios were print-tip loess normalized for each array (Smyth and Speed, 2003).”
Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets (2008, methodology): “Arrays from the Affymetrixbased studies were preprocessed using the robust multichip average [108], and arrays from two-color technology were LOESS (local regression) normalized [109, 110].”