justGCRMA: Compute GCRMA Directly from CEL Files

Description

This function converts CEL files into an ExpressionSet using the robust multi-array average (RMA) expression measure with help of probe sequences.

Usage

just.gcrma(..., filenames=character(0), phenoData=new("AnnotatedDataFrame"), description=NULL, notes="", compress=getOption("BioC")$affy$compress.cel, normalize=TRUE, bgversion=2, affinity.info=NULL, type=c("fullmodel","affinities","mm","constant"), k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast), correction=1, rho=0.7, optical.correct=TRUE, verbose=TRUE, fast=TRUE, minimum=1, optimize.by = c("speed","memory"),  cdfname = NULL, read.verbose = FALSE)
            justGCRMA(..., filenames=character(0), widget=getOption("BioC")$affy$use.widgets, compress=getOption("BioC")$affy$compress.cel, celfile.path=getwd(), sampleNames=NULL, phenoData=NULL, description=NULL, notes="", normalize=TRUE,  bgversion=2, affinity.info=NULL, type=c("fullmodel","affinities","mm","constant"), k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast), correction=1, rho=0.7, optical.correct=TRUE, verbose=TRUE, fast=TRUE, minimum=1, optimize.by = c("speed","memory"), cdfname = NULL, read.verbose = FALSE)

Arguments

...

file names separated by comma.

filenames

file names in a character vector.

widget

a logical specifying if widgets should be used.

compress

are the CEL files compressed?

phenoData

a AnnotatedDataFrame object.

description

a MIAME object.

notes

notes.

affinity.info

NULL or a list of three components: apm,amm and index, for PM probe affinities, MM probe affinities, the index of probes with known sequence, respectively.

type

"fullmodel" for sequence and MM model. "affinities" for sequence information only. "mm" for using MM without sequence information.

A tuning factor.

rho

correlation coefficient of log background intensity in a pair of pm/mm probes. Default=.7.

stretch

correction

normalize

Logical value. If TRUE, then normalize data using quantile normalization.

optical.correct

Logical value. If TRUE, then optical background correction is performed.

verbose

Logical value. If TRUE, then messages about the progress of the function is printed.

fast

Logical value. If TRUE, then a faster add-hoc algorithm is used.

optimize.by

"speed" will use a faster algorithm but more RAM, and "memory" will be slower, but require less RAM.

bgversion

integer value indicating which RMA background to use 1: use background similar to pure R rma background given in affy version 1.0 - 1.0.2 2: use background similar to pure R rma background given in affy version 1.1 and above.

minimum

celfile.path

a character denoting the path 'ReadAffy' should look for cel files.

sampleNames

a character vector of sample names to be used in the 'AffyBatch'.

cdfname

Used to specify the name of an alternative cdf package. If set to NULL, the usual cdf package based on Affymetrix' mappings will be used. Note that the name should not include the 'cdf' on the end, and that the corresponding probe package is also required to be installed. If either package is missing an error will result.

read.verbose

Logical value. If TRUE, then messages will be printed as each celfile is read in.

Value

An ExpressionSet object.

Details

This method should require much less RAM than the conventional method of first creating an AffyBatch and then running gcrma.

This is a simpler version than gcrma, so some of the arguments available in gcrma are not available here. For example, it is not possible to use the MM probes to estimate background. Instead, the internal NSB estimates are used (which is also the default for gcrma). Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

The tuning factor k will have different meanings if one uses the fast (add-hoc) algorithm or the empirical Bayes approach. See Wu et al. (2003)

fast.bkg and mem.bkg are two internal functions.