Methods for class AssayData in the oligoClasses package

Description

Batch statistics used for estimating copy number are stored as AssayData in the 'batchStatistics' slot of the CNSet class. Each element in the AssayData must have the same number of rows and columns. Rows correspond to features and columns correspond to batch.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

batchNames

signature(object = "AssayData"): ...

batchNames<-

signature(object = "AssayData"): ...

corr

signature(object = "AssayData", allele = "character"): ...

nu

signature(object = "AssayData", allele = "character"): ...

phi

signature(object = "AssayData", allele = "character"): ...

Details

lM: Extracts entire list of linear model parameters.

corr: The within-genotype correlation of log2(A) and log2(B) intensities.

nu: The intercept for the linear model. The linear model is fit to the A and B alleles independently.

phi: The slope for the linear model. The linear model is fit independently to the A and B alleles.

See Also

CNSet-class

Examples

library(crlmm)
library(Biobase)
data(cnSetExample, package="crlmm")
cnSet <- cnSetExample
isCurrent(cnSet)
assayDataElementNames(batchStatistics(cnSet))
## Accessors for linear model parameters
## -- Included here primarily as a check that accessors are working
## -- Values are all NA until CN estimation is performed using the crlmm package
##
## subsetting
cnSet[1:10, ]
## names of elements in the object
## accessors for parameters
nu(cnSet, "A")[1:10, ]
nu(cnSet, "B")[1:10, ]
phi(cnSet, "A")[1:10, ]
phi(cnSet, "B")[1:10, ]

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(oligoClasses)
Welcome to oligoClasses version 1.34.0
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/oligoClasses/AssayData-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: AssayData-methods
> ### Title: Methods for class AssayData in the oligoClasses package
> ### Aliases: corr flags,AssayData-method nu phi batchNames,AssayData-method
> ###   batchNames<-,AssayData-method nu,AssayData,character-method
> ###   phi,AssayData,character-method
> ### Keywords: classes
> 
> ### ** Examples
> 
> library(crlmm)
Loading required package: preprocessCore
Welcome to crlmm version 1.30.0
> library(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")'.

> data(cnSetExample, package="crlmm")
> cnSet <- cnSetExample
> isCurrent(cnSet)
     S4       R Biobase    eSet 
   TRUE   FALSE   FALSE    TRUE 
> assayDataElementNames(batchStatistics(cnSet))
 [1] "N.AA"       "N.AB"       "N.BB"       "corrAA"     "corrAB"    
 [6] "corrBB"     "flags"      "madA.AA"    "madA.AB"    "madA.BB"   
[11] "madB.AA"    "madB.AB"    "madB.BB"    "medianA.AA" "medianA.AB"
[16] "medianA.BB" "medianB.AA" "medianB.AB" "medianB.BB" "nuA"       
[21] "nuB"        "phiA"       "phiB"       "phiPrimeA"  "phiPrimeB" 
[26] "tau2A.AA"   "tau2A.BB"   "tau2B.AA"   "tau2B.BB"  
> ## Accessors for linear model parameters
> ## -- Included here primarily as a check that accessors are working
> ## -- Values are all NA until CN estimation is performed using the crlmm package
> ##
> ## subsetting
> cnSet[1:10, ]
CNSet (assayData/batchStatistics elements: matrix)
CNSet (storageMode: lockedEnvironment)
assayData: 10 features, 2 samples 
  element names: alleleA, alleleB, call, callProbability 
protocolData
  sampleNames: NA19007 NA19003
  varLabels: filename
  varMetadata: labelDescription
phenoData
  sampleNames: NA19007 NA19003
  varLabels: SKW SNR gender celFiles
  varMetadata: labelDescription
featureData
  featureNames: SNP_A-1991845 SNP_A-4266710 ... SNP_A-1991883 (10
    total)
  fvarLabels: isSnp position chromosome
  fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation: genomewidesnp6 
genome:  hg19 
batch:    SHELF:2 
batchStatistics:  29  elements,  10  features,  1  batches
> ## names of elements in the object
> ## accessors for parameters
> nu(cnSet, "A")[1:10, ]
 [1]  135.6732  193.2565  232.3048  326.1891 1148.2943  327.2699  988.3708
 [8]  826.5741  277.9738  662.7660
> nu(cnSet, "B")[1:10, ]
 [1] 285.3849 141.1907 148.6162 442.5685 612.6751 463.3794 562.0973 437.1798
 [9] 430.8244 835.1539
> phi(cnSet, "A")[1:10, ]
 [1]  368.6446  134.5155  686.7647  605.7064 1244.2066  433.0409 1278.4150
 [8] 1011.8632  955.9810  810.3196
> phi(cnSet, "B")[1:10, ]
 [1]  321.4181  206.9411  907.7213  419.5331 1147.0998  455.6837 2597.7767
 [8]  833.1082  464.5110  826.7954
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>