Description Usage Arguments Details Value Author(s) References Examples

Calculate the probability of differential expression of each feature in a microarray gene expression time-course data set.

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`eset` |
object of class matrix, ExpressionSet or exprSet containing log-ratios or log-values of expression for a series of microarrays |

`cond` |
character or factor vector giving the experimental group for each sample of eset. Not required for a single-condition time-course. |

`timepoint` |
numeric vector giving the time point for each sample of eset |

`replicate` |
character or factor vector giving the replicate ID of each sample of eset |

`twoColor` |
boolean indicating whether the data is from a two-color microarray platform |

`twoCondition` |
boolean indicating whether the data is from a two condition experiment (as opposed to a single condition experiment where the comparison is between baseline and subsequent time points) |

`alpha` |
the desired False Discovery Rate |

`verbose` |
whether to output more detailed information about the model fitting |

This function fits a model to estimate the probability of differential for each feature of time-course data set.

a numeric vector of the probability of differential expression for each feature in the data set.

Martin Aryee

The algorithm is described in detail in: Aryee et al., An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation), BMC Bioinformatics. 2009 Dec 10;10:409.

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