Description Usage Arguments Details Value Author(s) See Also Examples

This function formats a microarray timecourse dataset ready for the `interventionalInference`

function.

1 2 |

`d` |
A microarray spreadsheet, a |

`cellLines` |
A vector specifying a subset of cell lines to analyse (if absent, they are all used). |

`inhibitors` |
A vector specifying a subset of the inhibitors to analyse (if absent, they are all used). |

`stimuli` |
A vector specifying a subset of the stimuli to analyse (if absent, they are all used). |

`times` |
A vector specifying a subset of the times to analyse as the response (if absent, they are all used). |

`nodes` |
A vector specifying the indices of a subset of nodes to include in the analysis. |

`intercept` |
A logical value indicating whether an intercept parameter should be included in all models. |

`initialIntercept` |
A logical value indicating whether an intercept parameter should be used to estimate the level at the initial timepoint. Only used if the initial timepoint is in the response. |

`gradients` |
A logical value indicating whether the concentraion gradient should be used as the response instead of the raw concentrations.
This model has parallels with a dynamical systems viewpoint, and requires the covariance matrix to be adjusted. See |

The entries of column 4 of `d`

must be real numbers. Missing values are acceptable and are handled as follows:

Missing values in the response are ignored.

For the predictors, if a single timepoint is missing, the predictors are interpolated from the two immediate neighbours.

If one of the two immediate neighbours is missing then the response is ignored.

UNLESS the predictor in question is for the initial observation (which is always missing), in which case 0 is returned, so that the level at zero can be estimated by a second intercept parameter in the

`interventionalInferenceDBN`

function.

`y` |
The |

`X0` |
The |

`X1` |
The |

`Sigma` |
The |

`sampleInfo` |
An |

`interpolated` |
A matrix similar to |

`cond` |
A vector indexing the experimental conditions, given by the cell line, inhibitor and stimulus used in each sample. |

Simon Spencer

`interventionalInference`

, `interventionalInferenceAdvanced`

, `interventionalDBN-package`

, `interventionEffects`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(interventionalData)
# Load your own data spreadsheet using myData<-read.csv("myDataFile.csv").
# Use everything
fullData <- formatData(interventionalData)
# Use only DMSO and EGFRi samples.
halfData <- formatData(interventionalData,inhibitors=c("DMSO","EGFRi"))
# Produce gradients as response
diffData <- formatData(interventionalData,gradients=TRUE,initialIntercept=FALSE)
# Different results if we use the time between observations, rather than the timepoint.
interventionalData[,4]<-rep(c(0,5,10,20,30,60,90,120),4)
diffData2 <- formatData(interventionalData,gradients=TRUE,initialIntercept=FALSE)
# When there is missing data, interpolation also uses the time differences.
missingData <- interventionalData[-4,]
fullData2 <- formatData(missingData)
``` |

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