Filtering/Masking expression data

Description

Identyfying probes with binding affinity difference (BAD probes) between two groups of samples on the basis of expression data.

Usage

mask(affy,exprlist=NULL,useExpr=TRUE,ind,PM=FALSE,verbose=TRUE)

Arguments

Details

The function mask identifies in expression data probes which binding affinity (BAD probes) differs between two groups of samples, e.g two species. The basic input data is AffyBatch object(expresssion data prepared using the function ReadAffy from the library Affy) and a vector defining group assignement of samples. As masking is based on expression values, only expressed probes should be used. As a default they are defined by the affy function mas5calls and condition of being expressed (having "P" value) in at least 90% of samples from each group, but any set of probesets might be submitted with exprlist argument. Probes are analyzed for difference in binding affinity between groups. Each probe is assigned a quality score, based on all pairwise probes' correlations within probesets (for details see vignette or paper). Probes' quality scores, their x and y coordinates on the microarray and the probeset names are stored in a matrix.

Value

A list of two objects will be returned.

Author(s)

Michael Dannemann, Michael Lachmann

References

Dannemann et al, The effects of probe binding affinity differences on gene expression measurements and how to deal with them. Bioinformatics 2009 Khaitovich et al,Parallel Patterns of Evolution in the Genomes and Transcriptomes of Humans and Chimpanzees, Science 2005

See Also

overlapExprExtMasks, prepareMaskedAffybatch, mas5calls, plotProbe

Examples

data(AffyBatch)
## we provide 20 samples (10 for both human and chimpanzee)
## the first 10 entries are chimpanzee samples the last 10 from human
ind.vec=rep(1:2,each=10)
## mask on AffyBatch with all genes 
exmask <-
mask(newAffyBatch,ind=ind.vec,PM=TRUE,useExpr=FALSE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(maskBAD)
Loading required package: gcrma
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, 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: Biobase
Welcome to Bioconductor

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

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/maskBAD/mask.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mask
> ### Title: Filtering/Masking expression data
> ### Aliases: mask
> ### Keywords: internal
> 
> ### ** Examples
> 
> data(AffyBatch)
> ## we provide 20 samples (10 for both human and chimpanzee)
> ## the first 10 entries are chimpanzee samples the last 10 from human
> ind.vec=rep(1:2,each=10)
> ## mask on AffyBatch with all genes 
> exmask <-
+ mask(newAffyBatch,ind=ind.vec,PM=TRUE,useExpr=FALSE)
extract expression information
mask probes
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> 
> dev.off()
null device 
          1 
>