Description Usage Arguments Details Value Author(s) Examples

The analysisPipeline function is used to train a set of thresholds for predicting survival outcome within the context of a given signaling environment. This signaling environment is encoded in a geneSignature object.

1 | ```
analysisPipeline(dataSet, geneSig, iterPerK=2500, k=3, rand=TRUE, newjpdf=FALSE, jpdf=FALSE, nJPDF=12500, disc=c(0.005, 0.01, 0.03, 0.05), MFS="MFS", met="met", optMeth="Nelder-Mead")
``` |

`dataSet` |
ExpressionSet object containing both expression data (exprs) and phenotypic survival data (pData) |

`geneSig` |
geneSignature object containing directions, thresholds, and gene symbols |

`iterPerK` |
integer number of optimization iterations for each k |

`k` |
integer k for k-fold cross-validation |

`rand` |
boolean determining whether the k subsets are randomly drawn (otherwise k subsets are selected ordinally) |

`newjpdf` |
boolean for generating a joint probability function for alternate smoothed cost function (not recommended) |

`jpdf` |
solnSpace object containing empirical joint probability function for alternate smoothed cost function (not recommended) |

`nJPDF` |
value determining the number of samples with which to estimate the empirical joint probability function for alternate smoothed cost function (not recommended) |

`disc` |
vector of discretation thresholds for discretized cost function |

`MFS` |
variable name for survival-time data in dataSet object |

`met` |
variable name for metastasis event data in dataSet object |

`optMeth` |
optimization method used by R function 'optim' |

The analysisPipeline function optimizes over a cost function designed to minize both type I and II error. There is a discretized and smoothed cost function available, however implementation of the smoothed cost function relies on sampling of the solution space. This sampling may be pre-computed and implemented through the 'jpdf' argument, however overall usage of the smoothed cost function is not recommended.

A geneSignature object containing newly trained thresholds

UnJin Lee

1 2 3 4 5 6 7 8 9 | ```
## Load in example data
data("BrCa443")
## Create initial geneSignature object
## Note it is not necessary to define thresholds at this point
gs <- setGeneSignature(g=new("geneSignature"), direct=c(-1,1,1,1,1,1,1), genes=c("RKIP", "HMGA2", "SPP1", "CXCR4", "MMP1", "MetaLET7", "MetaBACH1"))
## Generate thresholds
gs <- analysisPipeline(dataSet=BrCa443, geneSig=gs, iterPerK=50, k=2, rand=FALSE)
``` |

sigsquared documentation built on May 31, 2017, 2:54 p.m.

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