A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

@article{McLachlan2006ASI,
  title={A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays},
  author={G. McLachlan and Richard Bean and L. B. Jones},
  journal={Bioinformatics},
  year={2006},
  volume={22 13},
  pages={
          1608-15
        }
}
  • G. McLachlan, Richard Bean, L. B. Jones
  • Published 2006
  • Computer Science, Medicine
  • Bioinformatics
    • MOTIVATION An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or… CONTINUE READING
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    References

    SHOWING 1-10 OF 42 REFERENCES
    A mixture model approach to detecting differentially expressed genes with microarray data
    • 182
    • PDF
    Detecting differential gene expression with a semiparametric hierarchical mixture method.
    • 556
    • PDF
    A Bayesian mixture model for differential gene expression
    • 171
    • Highly Influential
    • PDF
    Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
    • G. Smyth
    • Computer Science, Medicine
    • Statistical applications in genetics and molecular biology
    • 2004
    • 10,889
    • PDF
    A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments
    • 72
    • PDF
    Empirical Bayes Analysis of a Microarray Experiment
    • 1,663
    • Highly Influential
    • PDF
    A mixture model approach for the analysis of microarray gene expression data
    • 369
    • PDF
    Bayesian robust inference for differential gene expression in microarrays with multiple samples.
    • 93
    • PDF
    Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
    • 101
    Replicated microarray data
    • 747
    • PDF