A random variance model for detection of differential gene expression in small microarray experiments

By George W. Wright, Richard M. Simon. Bioinform., 2003

705 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/97cc7ab1b0d200d38f802bfc26c7315fb4a07ce3

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Citations in the corpus

Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans (2011, methodology): “Differentially expressed genes were identified through the random variance model.(16) A P value was calculated using the paired t-test.”
Testing significance relative to a fold-change threshold is a TREAT (2009, methodology): “Under the null hypothesis that βg =0, the moderated t follows a t-distribution on dg +d0 degrees of freedom (Smyth, 2004; Wright and Simon, 2003).”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “The use of linear models to describe multifactor microarray experiments is well established (18,19).”
BMC Bioinformatics (2005, positive): “As an alternative to the fudge factor, the random variance model (RVM) by [26] can be used and simple replacement of the pooled variance of t-statistic by the RVM variance results in close control of the FDR to the nominal level under dependence in moderate to large sample size conditions.”
Epithelial to Mesenchymal Transition Contributes to Drug Resistance in Pancreatic Cancer (2009, methodology): “Cancer Institute (18) was used to analyze the data.”
ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION (2016, methodology): “EB has been applied very successfully in gene expression analyses to moderate the genewise variance estimators (Baldi and Long, 2001; Wright and Simon, 2003; Smyth, 2004).”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “This includes methods for differential expression analysis [1, 2, 3], random effects [4, 5], gene set enrichment [6], gene set testing [7, 8] and so on.”
Statistical practice in high-throughput screening data analysis (2006, methodology): “An empirical Bayes approach using this model has been used successfully for analysis of microarray data with minimal replicatio”
Analysis of Gene Expression Data Using BRB-Array Tools (2007, background): “The individual gene test statistics are based on a hierarchical model that enables within-class variance information to be shared among genes in a manner that does not assume that all genes have the same variance (Wright and Simon, 2003).”