batchFilter.anova: Remove features which are differential regarding microarray...
In PAA: PAA (Protein Array Analyzer)

Description Usage Arguments Details Value Author(s) Examples

Finds features which are differential regarding at least two microarray batches / lots in a multi-batch scenario (i.e., > 2 batches) via one-way 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`	`EList` or `EListRaw` object (mandatory).
`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 t-test p-value for features to be considered as differential (e.g., `"0.5"`).
`fold.thresh`	float number indicating the minimum fold change for features to be considered as differential (e.g., `"1.5"`).
`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 multi-batch scenario (i.e., more than two batches). For this purpose, thresholds for p-values obtained from an one-way 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 (Ruhr-University Bochum, Bochum, Germany), michael.turewicz@rub.de

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)