Access results of 'evalScoring'

Description

This function processes the result of the evalScoring function and returns a list of probe sets within chromosome regions deemed significant by MACAT. Additional annotation for these probe sets is provided along with their identifiers.

Usage

getResults(MACATevalScoringOBJ)

Arguments

Details

The p-values have been computed individually for probe sets (genes), not for whole chromosome regions. Thus, regions deemed significant by sliding window approach do not have to consist only of probe sets with low p-values. These probe-set p-values are not used to determine whether a region is considered significant or not. Instead the comparison between actual and interpolated scores to actual and interpolated boundaries determines whether a region is considered significant.

This function is called within the plot function for the results of evalScoring, when HTML output is desired.

Value

A list with the following components, describing probe sets within chromosome regions deemed significant:

Author(s)

MACAT development team

See Also

evalScoring

Examples

data(stjd)
myevalres <- evalScoring(stjd, class="T", chromosome=6, nperms=10,
                         cross.validate=FALSE) 
results <- getResults(myevalres)
summary(results)
results$probeID[1:20]

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
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/get_results.Rd_%03d_medium.png", width=480, height=480)
> ### Name: getResults
> ### Title: Access results of 'evalScoring'
> ### Aliases: getResults
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(stjd)
> myevalres <- evalScoring(stjd, class="T", chromosome=6, nperms=10,
+                          cross.validate=FALSE) 
Investigating 5 samples of class T ...
Compute observed test statistics...
Building permutation matrix...
Compute 10 permutation test statistics...
10 ...
Compute empirical p-values...
Compute quantiles of empirical distributions...Done.
Computing sliding values for scores...
Compute sliding values for permutations...
All done.
> results <- getResults(myevalres)
Loading required package: hgu95av2.db
Loading required package: org.Hs.eg.db


> summary(results)
                Length Class  Mode     
probeID         24     -none- character
cytoband        22     -none- character
geneSYM         22     -none- character
pvalue          24     -none- numeric  
locusid         22     -none- character
genedescription 22     -none- character
probeScore      24     -none- numeric  
chromosome       1     -none- numeric  
class            1     -none- function 
> results$probeID[1:20]
 [1] "32623_at"   "40369_f_at" "37420_i_at" "37421_f_at" "40370_f_at"
 [6] "41237_at"   "38412_at"   "39412_at"   "32321_at"   "1181_at"   
[11] "37904_s_at" "37903_at"   "1162_g_at"  "37905_r_at" "38672_at"  
[16] "32221_at"   "39896_at"   "32662_at"   "151_s_at"   "709_at"    
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>