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")
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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)
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