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Last data update: 2014.03.03

R: Discretize regularized t-scores

discretize.tscores

R Documentation

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

`scores`	a MACATevalScoring object obtained from evalScoring
`data`	a MACATData Object containing all expression values, geneLocations and labels (obtained from preprocessedLoader)
`chrom`	chromosome that is discretized
`nperms`	number of permutations for the computation of empirical p values (evalScoring)
`kernel`	kernel function used for smoothing one of rbf, kNN, basePairDistance or your own
`kernelparams`	list of parameters for the kernels
`step.width`	size of a interpolation step in basepairs

Details

The filename for the python flat files are discrete_chrom_<chrom>_class_<label>.py where <chrom> and <label> are the names of the chromosome and class label.

Value

`discretize.tscores`	a vector of discretized tscores
`discretizeAllClasses.tscores`	creates python flatfiles (see details)

Author(s)

The MACAT development team

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(macat)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: annotate
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: XML
Loading MicroArray Chromosome Analysis Tool...
Loading required packages...

Type 'demo(macatdemo)' for a quick tour...

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/macat/discretize_tscores.Rd_%03d_medium.png", width=480, height=480)
> ### Name: discretize.tscores
> ### Title: Discretize regularized t-scores
> ### Aliases: discretize.tscores discretizeAllClasses.tscores
> ### Keywords: manip
> 
> ### ** Examples
> 
>   #loaddatapkg("stjudem")
>   #data(stjude)
>   data(stjd)
>   # simple scoring with short running time
>   scores = evalScoring(stjd, "T", 1, nperms=100, cross.validate=FALSE)
Investigating 5 samples of class T ...
Compute observed test statistics...
Building permutation matrix...
Compute 100 permutation test statistics...
100 ...
Compute empirical p-values...
Compute quantiles of empirical distributions...Done.
Computing sliding values for scores...
Compute sliding values for permutations...
All done.
>   discrete = discretize.tscores(scores)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>