A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

By Pierre Baldi, Anthony D. Long. Bioinform., 2001

1,776 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/178e55a6ef96feeefe5c34b1822f4df9aed2a049

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In Molecular Biology

Citations in the corpus

Variance stabilization applied to microarray data calibration and to the quantification of differential expression (2002, background): “However, it has also become clear that for many data sets that are encountered in practice they are insufficient (e.g. Beißbarth et al. (2000); Hughes et al. (2000); Rocke and Durbin (2001); Newton et al. (2001); Baldi and Long (2001); Baggerly et al. (2001); Theilhaber et al. (2001)).”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “In microarray analysis, this problem has been solved by regularized t-tests (28) or more formally by empirical Bayes or related methods that share information between genes (18,19,29).”
Model-based variance-stabilizing transformation for Illumina microarray data (2008, methodology): “For example, the cyberT-test method (18) uses a moving window to reduce the dependency of variance on expression level.”
Microarray data analysis: from disarray to consolidation and consensus (2006, methodology): “Different procedures weight the gene-specific and combined elements differentl”
Nonparametric methods for identifying differentially expressed genes in microarray data (2002, methodology): “, 2001) and a hierarchical Bayesian modeling framework with Gaussian gene-independent models combined with a t-test (Baldi and Long, 2001).”
A multivariate empirical Bayes statistic for replicated microarray time course data (2006, neutral): “ar X iv :m at h/ 07 02 68 5v 1 [ m at h. ST ] 2 3 Fe b 20 07 The Annals of Statistics 2006, Vol. 34, No. 5, 2387–2412 DOI: 10.1214/009053606000000759 c© Institute of Mathematical Statistics, 2006”
High-throughput prediction of protein antigenicity using protein microarray data (2010, methodology): “Differential analysis is done using the CyberT software described in Baldi and Long (2001) and available online at http://cybert.ics.uci.edu.”
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, neutral): “The idea of using a t-statistic with a Bayesian adjusted denominator was also proposed by Baldi and Long (2001) who developed the useful cyberT program. Their work was limited though to two-sample control versus treatment designs and their model did not distinguish between differentially and non-dif”
Statistical tests for differential expression in cDNA microarray experiments (2003, methodology): “where v0 is a tunable parameter that determines the relative contributions of gene-specific and global variances and n is the number of replicate measurements for each condition [19].”
Statistical practice in high-throughput screening data analysis (2006, methodology): “For example, although we found no evidence of a relationship between signal intensity and replicate variability in the two data sets we examined, such a relationship has been used in the microarray context in combination with the inverse gamma variance distribution assumptio”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, background): “Choosing a statistically optimal experimental design and the appropriate statistical analysis of produced data is increasingly important aspect of such experiments (Kerr and Churchill, 2001; Baldi and Long, 2001).”
Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, background): “By contrast, information sharing can be beneficial, because it can counteract the effects of low sample size (e.g. regularizedt-statistics, Baldi and Long, 2001; Tusheret al., 2001).”
Modeling Oncogenic Signaling in Colon Tumors by Multidirectional Analyses of Microarray Data Directed for Maximization of Analytical Reliability (2010, methodology): “A multitude of strategies have been proposed for this task, ranging from simple methods using only the fold change criterion to more sophisticated approaches using permutation-based statistical testing [46] or a Bayesian probabilistic framework [47].”