A mixture model approach for the analysis of microarray gene expression data

By David B. Allison, Gary L. Gadbury, Moonseong Heo, Jose R. Fernandez, Cheol-Koo Lee, Thomas A. Prolla, R. Weindruch. 2002

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0274982ca43e28442d403fb3b2387f6d52d3a8c7

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Citations in the corpus

Large-scale inference (2010, background): “(2003), Pounds and Morris (2003), Allison et al. (2002), Heller and Qing (2003), Broberg (2004), Aubert et al. (2004) and Liao et al. (2004). Recent methods, which also encompass Chapter 6’s problem of estimating the null density, include Jin and Cai (2007) and Muralidharan (2009a), the former using”
Covariate powered cross‐weighted multiple testing (2017, methodology): “, by Allison et al. [2002], Klaus and Strimmer [2011] and with covariates by Lei and Fithian [2018].”
False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. (2014, methodology): “Other refinements have been proposed, including other methods to estimate the fraction of true null hypotheses [30,31], and mixture models for the probability any specific null hypothesis is true [32].”
Estimating the number of true null hypotheses from a histogram of p values (2006, methodology): “The method proposed by Allison et al. (2002) is one excluded competitor.”
Multiple Comparisons: Bonferroni Corrections and False Discovery Rates (2004, methodology): “A third was suggested by Allison et al. (2002), who used ML to fit a mixture model to the p values, π0 come from the null distribution (and hence a uniform), while 1 − π0 come from the alternative where p values are assumed to follow some (otherwise unspecified) beta distribution.”
Size, power and false discovery rates (2007, background): “(2003), Pounds and Morris (2003), Allison et al. (2002), or Heller and Qin (2003); isotonic regression, Broberg (2005); local smoothing, Aubert et al. (2004); and hierarchical Bayes analyses, Liao et al. (2004), Do et al. (2004). All seem to perform reasonably well.”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, methodology): “(2002) are 92456hgu95a11, 92457hgu95a11, 92499hgu95a11, 92458hgu95a11, 92459hgu95a11, and 92500hgu95a11.”
Estimating the proportion of true null hypotheses, with application to DNA microarray data (2005, methodology): “Parametric estimation in this setting is studied for example by Allison et al. (2002), and Pounds and Morris (2003), who used finite mixtures of beta distributions.”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, methodology): “The distribution of P -values is modeled by a mixture of the b distribution and the uniform distribution (38) (see Supplementary Methods for details).”
CONSISTENCY OF A RECURSIVE ESTIMATE OF MIXING DISTRIBUTIONS (2009, background): “In recent years, these models have been widely used in genetics, bioinformatics, proteomics, computer vision, speech analysis and a host of other research areas; see, for example, [1, 5, 7, 21, 25, 27, 31].”
Microarray data analysis: from disarray to consolidation and consensus (2006, background): “For common designs, which we refer to as designs in which two groups of cases are evaluated for differential expression, evidence indicates that a minimum of 5 biological cases per group should be analyse”