Description Usage Arguments Details Value Author(s) Examples
Finds features which are differential regarding at least two microarray batches / lots in a multibatch scenario (i.e., > 2 batches) via oneway analysis of variance (ANOVA) and removes them.
1 2  batchFilter.anova(elist=NULL, log=NULL, p.thresh=0.05, fold.thresh=1.5,
output.path=NULL)

elist 

log 
logical indicating whether the data is in log scale (mandatory; note: if TRUE log2 scale is expected). 
p.thresh 
positive float number between 0 and 1 indicating the maximum
Student's ttest pvalue for features to be considered as differential (e.g.,

fold.thresh 
float number indicating the minimum fold change for
features to be considered as differential (e.g., 
output.path 
string indicating a path for saving results (optional). 
This function takes an EList
or EListRaw
object (see limma
documentation) to find features which are differential regarding at least two
microarray batches / lots in a multibatch scenario (i.e., more than two
batches). For this purpose, thresholds for pvalues obtained from an oneway
analysis of variance (ANOVA) and fold changes can be defined. To visualize the
differential features a volcano plot is drawn. Then, differential features are
removed and the remaining data are returned. When an output path is defined
(via output.path
) volcano plots and result files are saved on the hard
disk.
An EList
or EListRaw
object without differential features
regarding at least two microarray batches / lots.
Ivan Grishagin (Rancho BioSciences LLC, San Diego, CA, USA), John Obenauer (Rancho BioSciences LLC, San Diego, CA, USA) and Michael Turewicz (RuhrUniversity Bochum, Bochum, Germany), michael.turewicz@rub.de
1 2 3 4 5  cwd < system.file(package="PAA")
load(paste(cwd, "/extdata/Alzheimer.RData", sep=""))
elist < elist[elist$genes$Block < 10,]
elist < batchFilter.anova(elist=elist, log=FALSE, p.thresh=0.001,
fold.thresh=3)

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