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Last data update: 2014.03.03

R: Fit a Probe Level Model to Affymetrix Genechip Data.

fitPLM

R Documentation

Fit a Probe Level Model to Affymetrix Genechip Data.

Description

This function converts an AffyBatch into an PLMset by fitting a specified robust linear model to the probe level data.

Usage

fitPLM(object,model=PM ~ -1 + probes +samples,
       variable.type=c(default="factor"),
       constraint.type=c(default="contr.treatment"),
       subset=NULL,
       background=TRUE, normalize=TRUE, background.method="RMA.2",
       normalize.method="quantile", background.param=list(),
       normalize.param=list(), output.param=verify.output.param(),
       model.param=verify.model.param(object, model),
       verbosity.level=0)

Arguments

`object`	an `AffyBatch`
`model`	A formula describing the model to fit. This is slightly different from the standard method of specifying formulae in R. Read the description below
`variable.type`	a way to specify whether variables in the model are factors or standard variables
`constraint.type`	should factor variables sum to zero or have first variable set to zero (endpoint constraint)
`subset`	a vector with the names of probesets to be used. If NULL then all probesets are used.
`normalize`	logical value. If `TRUE` normalize data using quantile normalization
`background`	logical value. If `TRUE` background correct using RMA background correction
`background.method`	name of background method to use.
`normalize.method`	name of normalization method to use.
`background.param`	A list of parameters for background routines
`normalize.param`	A list of parameters for normalization routines
`output.param`	A list of parameters controlling optional output from the routine.
`model.param`	A list of parameters controlling model procedure
`verbosity.level`	An integer specifying how much to print out. Higher values indicate more verbose. A value of 0 will print nothing

Details

This function fits robust Probe Level linear Models to all the probesets in an AffyBatch. This is carried out on a probeset by probeset basis. The user has quite a lot of control over which model is used and what outputs are stored. For more details please read the vignette.

Value

An PLMset

Author(s)

Ben Bolstad bmb@bmbolstad.com

References

Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

Examples

if (require(affydata)) {
  data(Dilution)
  Pset <- fitPLM(Dilution, model=PM ~ -1 + probes + samples)
  se(Pset)[1:5,]

  image(Pset)
  NUSE(Pset) 

  #now lets try a wider class of models
  ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,
  normalize=FALSE,background=FALSE)
## End(Not run) 
  ## Not run: coefs(Pset)[1:10,]

  ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver +
  scanner, normalize=FALSE,background=FALSE)
## End(Not run)
  coefs(Pset)[1:10,]

  #try liver as a covariate
  logliver <- log2(c(20,20,10,10))
  ## Not run: Pset <- fitPLM(Dilution, model=PM~-1+probes+logliver+scanner,
  normalize=FALSE, background=FALSE, variable.type=c(logliver="covariate"))
## End(Not run) 
  coefs(Pset)[1:10,]

  #try a different se.type
  ## Not run: Pset <- fitPLM(Dilution, model=PM~-1+probes+scanner,
  normalize=FALSE,background=FALSE,m odel.param=list(se.type=2))
## End(Not run) 
  se(Pset)[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(affyPLM)
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

Loading required package: affy
Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: gcrma
Loading required package: preprocessCore
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/affyPLM/fitPLM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: fitPLM
> ### Title: Fit a Probe Level Model to Affymetrix Genechip Data.
> ### Aliases: fitPLM
> ### Keywords: manip
> 
> ### ** Examples
> 
> if (require(affydata)) {
+   data(Dilution)
+   Pset <- fitPLM(Dilution, model=PM ~ -1 + probes + samples)
+   se(Pset)[1:5,]
+ 
+   image(Pset)
+   NUSE(Pset) 
+ 
+   #now lets try a wider class of models
+   ## Not run: 
+ ##D Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,
+ ##D   normalize=FALSE,background=FALSE)
+ ## End(Not run) 
+   ## Not run: coefs(Pset)[1:10,]
+ 
+   ## Not run: 
+ ##D Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver +
+ ##D   scanner, normalize=FALSE,background=FALSE)
+ ## End(Not run)
+   coefs(Pset)[1:10,]
+ 
+   #try liver as a covariate
+   logliver <- log2(c(20,20,10,10))
+   ## Not run: 
+ ##D Pset <- fitPLM(Dilution, model=PM~-1+probes+logliver+scanner,
+ ##D   normalize=FALSE, background=FALSE, variable.type=c(logliver="covariate"))
+ ## End(Not run) 
+   coefs(Pset)[1:10,]
+ 
+   #try a different se.type
+   ## Not run: 
+ ##D Pset <- fitPLM(Dilution, model=PM~-1+probes+scanner,
+ ##D   normalize=FALSE,background=FALSE,m odel.param=list(se.type=2))
+ ## End(Not run) 
+   se(Pset)[1:10,]
+ }
Loading required package: affydata
     Package    LibPath                            Item      
[1,] "affydata" "/home/ddbj/local/lib64/R/library" "Dilution"
     Title                        
[1,] "AffyBatch instance Dilution"

                 20A        20B        10A        10B
1000_at   0.03798884 0.03674159 0.03947771 0.03526104
1001_at   0.05850398 0.05889818 0.05917896 0.05606087
1002_f_at 0.06684381 0.06552410 0.06373685 0.06468718
1003_s_at 0.06114870 0.05960099 0.06161744 0.06088377
1004_at   0.05008044 0.04901129 0.04981253 0.04707150
1005_at   0.03306727 0.03260940 0.03616108 0.03333392
1006_at   0.06250004 0.05937301 0.05909922 0.05929328
1007_s_at 0.03227312 0.03123055 0.03342962 0.03165328
1008_f_at 0.03742424 0.03946406 0.03788041 0.03814034
1009_at   0.02842615 0.02861797 0.03055512 0.02849441
Warning messages:
1: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when loading 'hgu95av2cdf' 
2: replacing previous import 'AnnotationDbi::head' by 'utils::head' when loading 'hgu95av2cdf' 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>