expFarms: Factor Analysis for Robust Microarray Summarization
In farms: FARMS - Factor Analysis for Robust Microarray Summarization

Description Usage Arguments Details Value See Also Examples

This function converts an instance of AffyBatch into an instance of exprSet-class using a factor analysis model for which a Bayesian Maximum a Posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise.

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          expFarms(object, bgcorrect.method = "none", pmcorrect.method = "pmonly", 
        normalize.method = "quantiles",  weight, mu,  weighted.mean, laplacian, robust, correction, centering, spuriousCorrelation, ...)

`object`	An instance of `AffyBatch`.
`weight`	Hyperparameter value in the range of [0,1] which determines the influence of the prior. The default value is 0.5
`bgcorrect.method`	the name of the background adjustment method
`pmcorrect.method`	the name of the PM adjustment method
`normalize.method`	the normalization method to use
`mu`	Hyper-parameter value which allows to quantify different aspects of potential prior knowledge. Values near zero assumes that most genes do not contain a signal, and introduces a bias for loading matrix elements near zero. Default value is 0
`weighted.mean`	Boolean flag, that indicates whether a weighted mean or a least square fit is used to summarize the loading matrix. The default value is set to FALSE.
`laplacian`	Boolean flag, indicates whether a Laplacian prior for the factor is employed or not. Default value is FALSE.
`robust`	Boolean flag, that ensures non-constant results. Default value is TRUE.
`correction`	Value that indicates whether the covariance matrix should be corrected for negative eigenvalues which might emerge from the non-negative correlation constraints or not. Default = O (means that no correction is done), 1 (minimal noise (0.0001) is added to the diagonal elements of the covariance matrix to force positive definiteness), 2 (Maximum Likelihood solution to compute the nearest positive definite matrix under the given non-negative correlation constraints of the covariance matrix)
`centering`	Indicates whether the data is "median" or "mean" centered. Default value is "median".
`spuriousCorrelation`	Numeric value in the range of [0,1] that quantifies the suppression of spurious correlation when using the Laplacian prior. Default value is 0 (no suppression). Note, that this parameter is only active when the laplacian parameter is set to TRUE.
`...`	other arguments to be passed to `expresso`.

This function is a wrapper for expresso.

exprSet-class

expresso, qFarms, lFarms.

1 2	data(testAffyBatch) eset <- expFarms(testAffyBatch, bgcorrect.method = "none", pmcorrect.method = "pmonly", normalize.method = "constant", weight=0.5)

Loading required package: affy
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, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

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: MASS
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Citation: S. Hochreiter et al.,
A new summarization method for affymetrix probe level data,
Bioinformatics, 22, 8, 943-949, 2006

Citation: W. Talloen et al.,
I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data,
Bioinformatics, 23, 21, 2897-2902, 2007
BibTex: enter 'toBibtex(citation("farms"))'

Homepage: http://www.bioinf.jku.at/software/farms/farms.html

FARMS Package Version 1.42.0


Changes in FARMS:
For all changes previous to 1.3.0, see the farms vignette.
Version 1.3.0: Added I/NI-calls for filtering
               Adjusted Hyperparameters for alternative CDFs,
               probes set standardized, weighted mean
               Works now with R >= 2.8 and Bioconductor 2.3,
               Changed termination criterion, initialization values,
               factors and loadings scaled, added argument robust
               Update for R-2.11
               Updated I/NI-Call for Laplace-FARMS version,
               Maximum likelihood correlation structure given
               non-negative constraints
Version 1.4.0: Default centering changed to median
Version 1.8.x: Suppression of spurious correlation (Laplace-FARMS)

background correction: none 
normalization: constant 
PM/MM correction : pmonly 
expression values: farms 
background correcting...done.
normalizing...done.
2 ids to be processed
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