Description Usage Arguments Value Author(s) Examples

Based on the result from the interaction test by looking at the result from the regressResult object, this function partitions tne orignal data, an ExpressionSetinto groups, one contains the genes without the interaction and others contains the genes with the interaction across different level of covariates.

1 | ```
postInteraction(eSet, regressObject, mainVar, compare1, compare2, method = regressionMethod(regressObject), adj = adjustment(regressObject))
``` |

`eSet` |
an ExpressionSet |

`regressObject` |
a regressResult |

`mainVar` |
variable of main interest |

`compare1` |
the first value of the |

`compare2` |
Based on the example for |

`method` |
It is used to run regression within each level of the effect modifier. choose the follwoing three options: "limma" (LIMMA), "regression" (ordinary linear regression), "permutation" (permutation test) |

`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. |

an `interactionResult`

class. The first component contains all the result
for all the genes. The second component contains the genes without the interaction
effect. The rest of the components contains genes with the interactions.

Xiwei Wu, Arthur Li

1 2 3 4 5 6 7 8 | ```
data(eSetExample)
design.int<- new("designMatrix", target=pData(eSetExample), covariates = c("Treatment", "Group"),
intIndex = c(1, 2))
contrast.int<- new("contrastMatrix", design.matrix = design.int, interaction=TRUE)
result.int<- regress(eSetExample, contrast.int)
sigResult.int <- selectSigGene(result.int)
intResult <- postInteraction(eSetExample, sigResult.int, mainVar ="Treatment",
compare1 = "Treated", compare2 = "Control")
``` |

ArrayTools documentation built on May 2, 2018, 3:04 a.m.

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