# Run regression to fit genewise linear model

### Description

Fit genewise linear model using LIMMA package, ordinary linear regression, or permutation method.

### Usage

1 | ```
regress(object, design, contrast, method, adj="none", permute.time=1000)
``` |

### Arguments

`object` |
an "ExpressionSet" |

`design` |
design matrix from the make.design function |

`contrast` |
contrast matrix from the make.contrast function |

`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 |

`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. |

`permute.time` |
number of permutation times, only used for the permutation method. |

### Value

A dataframe contains rows for all the genes from object and the following columns: ID(probeid); Log2Ratio (estimate of the effect or the contrast, on the log2 scale); F (F statistics); P.Value (raw p-value); adj.P.Value (adjusted p-value or q-value)

### Author(s)

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

### References

Smyth, G.K. (2005) Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420

### Examples

1 2 3 4 5 6 7 8 9 10 11 | ```
data(testData)
normaldata<-pre.process("rma",testData)
## Create design matrix
design<-make.design(pData(normaldata), "group")
## Create contrast matrix - Compare group "A" vs. "C"
contrast<-make.contrast(design, "A", "C")
## Identify differentially expressed gene by using LIMMA method
result<-regress(normaldata, design, contrast, "L")
``` |