Analysis of Variance for Gene Expression Microarray Data

By M. Kerr, Mitchell Martin, G. Churchill. J. Comput. Biol., 2000

1,435 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/65c4f601132902530ff0217b21e321e9c7e7812a

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Citations in the corpus

Cytosolic Ascorbate Peroxidase 1 Is a Central Component of the Reactive Oxygen Gene Network of Arabidopsisw⃞ (2005, methodology): “…construct was designed using the HSF21 open reading frame (Figure 5A), there are 21 different HSF genes in Arabidopsis, and oligomers (three subunits per DNA binding domain) of HSFs could contain multiple copies of a particular factor or monomers encoded by different HSF genes (Nover et al., 2001).”
A Practical Approach to Microarray Data Analysis (2003, background): “Most practitioners work with log-transformed data (Kerr et al., 2000; Quackenbush, 2001; Yang et al., 2002).”
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, neutral): “Kerr et al (2000) propose a single linear model for an entire microarray experiment whereas in this paper a separate linear model is fitted for each gene.”
The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster (2001, background): “, and in fact such methods suggest experimental designs that dispense with reference samples but increase statistical power as compared with ratio-based method”
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis (2007, background): “Major sources of expression variation are due to technical [3,4], environmental [5,6], demographic [7,8], or genetic [9–”
An Expression-Driven Approach to the Prediction of Carbohydrate Transport and Utilization Regulons in theHyperthermophilic Bacterium Thermotoga maritima (2005, methodology): “dyes, and cDNA spots allowed the use of analysis of variance (26, 83) models for”
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, background): “Genes that are differentially expressed among samples can then be identified by statistical algorithms (see for example [9, 13, 19, 31]).”
Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. (2001, background): “In a reverse labeling design ( 5 ,18) each of the two samples (say A and B) to be compared is divided into two aliquots and labeled with two different dyes (say Cy3 and Cy5) in separate steps.”
Reliability and reproducibility issues in DNA microarray measurements. (2006, background): “* Usually but not always, see, for example, the loop designs proposed by M.K. Kerr et al. [ 21 ], G.A.”
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, neutral): “However, the real data distributions sometimes do not fit closely to these models [9,16].”
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles (2003, neutral): “[23] and Lee et al.”
Fundamentals of experimental design for cDNA microarrays (2002, methodology): “Analysis-of-variance models for microarray data were introduced by Kerr et al”
Experimental design for gene expression microarrays. (2001, methodology): “3) (Kerr et al., 2000), so we have used linear models as a starting point for studying microarray experimental design.”
Variance stabilization applied to microarray data calibration and to the quantification of differential expression (2002, methodology): “Like in the ANOVA approach by Kerr et al. (2000), calibration is done not necessarily for pairs of samples but simultaneously for a whole set.”
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays (2004, neutral): “By using the log-scale transformation before analyzing microarray data, investigators have, implicitly or explicitly, assumed a multiplicative measurement error model (Dudoit et al., 2002; Newton et al., 2001; Kerr et al., 2000; Wolﬁnger et al., 2001).”