Create MACAT list from objects in workspace

Description

This is a wrapper around the preprocessedLoader function. Use it, when you want to build a MACAT-list structure from objects already in your workspace.

Usage

  buildMACAT(matrix, chip, labels = NULL, chromLocObj = NULL)

Arguments

Details

This is only a convenience wrapper around the function preprocessedLoader for the case, that you want to build a MACAT-list from objects in your workspace.

Value

A MACAT-list structure. For an example and a description of the format see data stjude in package 'stjudem'.

Author(s)

MACAT development team

See Also

preprocessedLoader,stjude in package 'stjudem'

Examples

 X <- matrix(rnorm(200),nrow=20,ncol=10)
 rownames(X) <- c('34916_s_at','34917_at','34462_at','163_at','35219_at',
	'31641_s_at','33300_at','33301_g_at','38950_r_at','41249_at',
	'294_s_at','32004_s_at','33299_at','41243_at','33341_at','362_at',
	'1918_at','41499_at','41500_at','41282_s_at')
 colnames(X) <- paste("Sample",1:10,sep="")
 y <- rep(c("A","B"),c(5,5))
 toy <- buildMACAT(X,"hgu95av2.db",y)
 summary(toy)

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 'demo()' for some demos, 'help()' for on-line help, or
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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/buildMACAT.Rd_%03d_medium.png", width=480, height=480)
> ### Name: buildMACAT
> ### Title: Create MACAT list from objects in workspace
> ### Aliases: buildMACAT
> ### Keywords: manip
> 
> ### ** Examples
> 
>  X <- matrix(rnorm(200),nrow=20,ncol=10)
>  rownames(X) <- c('34916_s_at','34917_at','34462_at','163_at','35219_at',
+ 	'31641_s_at','33300_at','33301_g_at','38950_r_at','41249_at',
+ 	'294_s_at','32004_s_at','33299_at','41243_at','33341_at','362_at',
+ 	'1918_at','41499_at','41500_at','41282_s_at')
>  colnames(X) <- paste("Sample",1:10,sep="")
>  y <- rep(c("A","B"),c(5,5))
>  toy <- buildMACAT(X,"hgu95av2.db",y)


Reading expression matrix...
Reading labels...
Assessing chromosome information for genes on chip...
Locating genes on chromosome 1 .... 21 
Locating genes on chromosome 10 .... 0
Locating genes on chromosome 11 .... 0
Locating genes on chromosome 12 .... 0
Locating genes on chromosome 13 .... 0
Locating genes on chromosome 14 .... 0
Locating genes on chromosome 15 .... 0
Locating genes on chromosome 16 .... 0
Locating genes on chromosome 17 .... 0
Locating genes on chromosome 18 .... 0
Locating genes on chromosome 19 .... 0
Locating genes on chromosome 2 .... 0
Locating genes on chromosome 20 .... 0
Locating genes on chromosome 21 .... 0
Locating genes on chromosome 22 .... 0
Locating genes on chromosome 3 .... 0
Locating genes on chromosome 4 .... 1 
Locating genes on chromosome 5 .... 0
Locating genes on chromosome 6 .... 0
Locating genes on chromosome 7 .... 0
Locating genes on chromosome 8 .... 0
Locating genes on chromosome 9 .... 0
Locating genes on chromosome X .... 0
Locating genes on chromosome Y .... 0
Locating genes on chromosome 17_ctg5_hap1 .... 0
Locating genes on chromosome 6_cox_hap2 .... 0
Locating genes on chromosome 6_ssto_hap7 .... 0
Locating genes on chromosome 6_mcf_hap5 .... 0
Locating genes on chromosome 4_ctg9_hap1 .... 0
Locating genes on chromosome 1_gl000191_random .... 0
Locating genes on chromosome 19_gl000209_random .... 0
Locating genes on chromosome 6_qbl_hap6 .... 0
Locating genes on chromosome 6_dbb_hap3 .... 0
Locating genes on chromosome Un_gl000223 .... 0
Locating genes on chromosome 6_apd_hap1 .... 0
Locating genes on chromosome 6_mann_hap4 .... 0
>  summary(toy)
             Length Class  Mode     
geneName      22    -none- character
geneLocation  22    -none- numeric  
chromosome    22    -none- character
expr         200    -none- numeric  
labels        10    -none- character
chip           1    -none- character
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>