This is a wrapper function complete the following tasks: 1. Create a design matrix 2. Create a contrast 3. Run regression 4. Select differentaly expressed gene 5. Output the differentally expressed gene to a html file

1 2 3 | ```
AffyRegress(normal.data, cov, compare1, compare2, method,
int=NULL, level=NULL, adj="none", p.value=0.05, m.value=0,
filename="result")
``` |

`normal.data` |
an 'ExpressionSet' |

`cov` |
a list of 1-n covariates |

`compare1` |
the first value of the main covariate. For example, suppose that the main covariate is drug, and there are three unique values: "drug1", "drug2", and "placebo". You would like to compare "drug1" to "drug2". Then you would use "drug1" as compare1 |

`compare2` |
the second value of the main covariate. Based on the previoius example, if you would like to compare "drug1" vs "drug2", then you would use "drug2" as compare2 |

`method` |
Three methods are supported by this function: "L" for using LIMMA method - compute moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value; "F" for using ordinary linear regression; "P" for permuation test by resampling the phenotype |

`int` |
if int=NULL, the interaction effect is not considered; otherwise, use two integers to indicate which covariates are considered for interaction effect. For example, if cov<-c("estrogen","drug","time") and you are considering the interaction between "estrogen" and "time", then you would write int=c(1,3) |

`level` |
you only specify this term when the design matrix contains an interaction term. Suppose that you would like to compare "drug1" to "drug2" only when estrogen is "present", where "present" is one of the values of the estrogen variable. You will use "present" as level. |

`adj` |
adjustment method for multiple comparison test, including "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". The default value is "none". Type help(p.adjust) for more detail. |

`p.value` |
p value, the default value is 0.05 |

`m.value` |
fold change cut-off value, default value is 0 |

`filename` |
name of the output file |

A dataframe which has the same format as the one created by select.sig.gene function.

Xiwei Wu xwu@coh.org, Xuejun Arthur Li xueli@coh.org

1 2 3 | ```
data(testData)
normaldata<-pre.process("rma",testData)
result<-AffyRegress(normaldata, "group", "A", "C", "L")
``` |

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