\name{discretize.tscores} \alias{discretize.tscores} \alias{discretizeAllClasses.tscores} \title{Discretize regularized t-scores} \description{ discretize.tscores returns a discretized version of the scores in the MACATevalScoring object. Discretization is performed by comparing the value gene-wise (location-wise) with the symmetric upper and lower quantile given by margin (in percent margin/2 lower and upper quantile). discretizeAllClasses produces a flatfile readable by PYTHON. } \usage{ discretize.tscores(scores) discretizeAllClasses.tscores(data, chrom, nperms=10, kernel=rbf, kernelparams=NULL, step.width=100000) } \arguments{ \item{scores}{ a MACATevalScoring object obtained from evalScoring } \item{data}{ a MACATData Object containing all expression values, geneLocations and labels (obtained from preprocessedLoader) } \item{chrom}{ chromosome that is discretized } \item{nperms}{ number of permutations for the computation of empirical p values (evalScoring) } \item{kernel}{ kernel function used for smoothing one of rbf, kNN, basePairDistance or your own } \item{kernelparams}{ list of parameters for the kernels } \item{step.width}{ size of a interpolation step in basepairs } } \details{ The filename for the python flat files are \code{discrete_chrom_<chrom>_class_<label>.py} where <chrom> and <label> are the names of the chromosome and class label. } \value{ \item{discretize.tscores}{ a vector of discretized tscores } \item{discretizeAllClasses.tscores}{ creates python flatfiles (see details) } } \author{ The MACAT development team } \seealso{ \code{\link{evalScoring}}, \code{\link{kernels}}, \code{\link{pythondata}}} \examples{ #loaddatapkg("stjudem") #data(stjude) data(stjd) # simple scoring with short running time scores = evalScoring(stjd, "T", 1, nperms=100, cross.validate=FALSE) discrete = discretize.tscores(scores) } \keyword{manip}