On the gene ranking of replicated microarray time course data
نویسندگان
چکیده
Consider the gene ranking problem of replicated microarray time course experiments where there are multiple biological conditions, and genes of interest are those whose temporal profiles are different across conditions. We derive the multi-sample multivariate empirical Bayes statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multi-sample model assumes that time course replicates are i.i.d. multivariate normal vectors. On the other hand, we construct our cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes setting which guarantee closed form solutions 1 for the posterior odds. Our simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches work well. keywords: longitudinal; cross-sectional; microarray time course; gene ranking; empirical Bayes.
منابع مشابه
Materials for On the gene ranking of replicated microarray time course data
In contrast, when ν → 0, the posterior odds are just equation (5) with ν replaced by 0. This means that gene-specific Ws are so different such that moderation is not able to move these gene-specific W at all. Thus, the degree of moderation reflects how similar these gene-specific within group sums of squares are. The more similar they are, the greater degree of moderation our proposed statistic...
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