Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models

By Russell D. Wolfinger, Greg Gibson, E. D. Wolfinger, L. Bennett, H. Hamadeh, P. Bushel, C. Afshari, R. Paules. J. Comput. Biol., 2001

1,213 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ccea618f9a2851e33398de395c4fc27d2ef982aa

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Citations in the corpus

Manipulation of thyroid status and/or GH injection alters hepatic gene expression in the juvenile chicken (2007, methodology): “The normalized values were then analyzed using SAS mixed model analysis of variance (Wolfinger et al., 2001).”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, neutral): “approaches have been proposed (Kerr et al., 2000; Newton et al., 2001; Ideker et al., 2000; Wolfinger et al., 2001; Rocke and Dubin, 2001; Lonnstedt and Speed, 2001).”
Evaluation of gene expression measurements from commercial microarray platforms. (2003, methodology): “Kerr et al. (8) and Wolfinger et al. ( 9 ), in this study we analyzed base 2 logarithms of the original fluorescent signals when modeling differential expression with an ANOVA model and computing correlations of signal across platforms (see below).”
Canopy photosynthesis, evapotranspiration, leaf nitrogen, and transcription profiles of maize in response to CO2 enrichment (2006, methodology): “The data were then transformed with a logarithm to the base 2 and the effects of elevated Ca treatment on gene expression were modeled and evaluated by determining the experimentwise error rate with the Bonferroni adjustment (Wolfinger et al., 2001).”
Use of within-array replicate spots for assessing differential expression in microarray experiments (2005, methodology): “A standard analysis method is to fit the same statistical model separately to the expression measurements for each gene (Wolfinger et al., 2001; Yang and Speed, 2003).”
Stochastic Models Inspired by Hybridization Theory for Short Oligonucleotide Arrays (2004, neutral): “By using a log-scale transformation before analyzing microarray data, a great number of investigators have, implicitly or explicitly, proposed a multiplicative measurement error model [7, 19, 15, 20 ] for microarray data.”
Design issues for cDNA microarray experiments (2002, methodology): “By contrast, suitably defined, standard statistical methods, such as t -tests, are applicable to analysing data from replicate slides, although some changes to these methods need to be mad”
Stress Response Versus Stress Tolerance: A Transcriptome Analysis of Two Rice Lines Contrasting in Tolerance to Phosphorus Deficiency (2009, methodology): “The model proposed by Wolfinger et al. (2001) was implemented for a loop design using R/ MAANOVA (Wu et al. 2002), where the effect of genotype and environment alone was estimated by the no-interaction model: Gene specific fixed effect ¼ Arrayþ Dyeþ Genotypeþ Env: Random effect ¼ Array; and…”
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, neutral): “Keywords: microarrays, empirical Bayes, linear models, hyperparameters, differential expression”
limma: Linear Models for Microarray Data (2005, background): “It is possible to do this using a mixed model representation which treats each spot as a randomized block [38, 28].”
Fundamentals of experimental design for cDNA microarrays (2002, background): “, and this framework has been extended to account for correlations and multiple sources of varianc”
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).”
An Expression-Driven Approach to the Prediction of Carbohydrate Transport and Utilization Regulons in theHyperthermophilic Bacterium Thermotoga maritima (2005, methodology): “malization analysis of variance model (83) was used to estimate global variation”
The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster (2001, methodology): “It has been pointed out that standard methods of quantitative genetic analysis can be applied to microarray dat”