sigsquared (version 1.4.0)

analysisPipeline: Training of thresholds

Description

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.

Usage

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")

Arguments

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'

Value

Details

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.

Examples

## 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)