2,490
views
0
recommends
+1 Recommend
0 collections
    4
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

      Statistical Applications in Genetics and Molecular Biology
      Walter de Gruyter GmbH

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

          Related collections

          Author and article information

          Journal
          Statistical Applications in Genetics and Molecular Biology
          Walter de Gruyter GmbH
          1544-6115
          January 12 2004
          January 12 2004
          : 3
          : 1
          : 1-25
          Article
          10.2202/1544-6115.1027
          16646809
          dfa90f3c-3ec0-4c87-b6ca-134b6760c674
          © 2004
          History

          Comments

          Comment on this article

          scite_
          10,503
          24
          9,716
          0
          Smart Citations
          10,503
          24
          9,716
          0
          Citing PublicationsSupportingMentioningContrasting
          View Citations

          See how this article has been cited at scite.ai

          scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

          Similar content64

          Cited by3,200