batchFilter: Remove differential features regarding array batches/lots.
In PAA: PAA (Protein Array Analyzer)

Description Usage Arguments Details Value Author(s) Examples

Finds differential features regarding array batches/lots and removes them.

1 2	batchFilter(elist=NULL, lot1=NULL, lot2=NULL, log=NULL, p.thresh=0.05, fold.thresh=1.5, output.path=NULL)

`elist`	`EList` or `EListRaw` object (mandatory).
`lot1`	vector of column names for group 1 (mandatory).
`lot2`	vector of column names for group 2 (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) and the batch-specific column name vectors lot1 and lot2 to find differential features regarding batches/lots. For this purpose, thresholds for p-values (Student's t-test) 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 array batches/lots.

Michael Turewicz, michael.turewicz@rub.de

cwd <- system.file(package="PAA")
load(paste(cwd, "/extdata/Alzheimer.RData", sep=""))
elist <- elist[elist$genes$Block < 10,]
lot1 <- elist$targets[elist$targets$Batch=='Batch1','ArrayID']
lot2 <- elist$targets[elist$targets$Batch=='Batch2','ArrayID']
elist <- batchFilter(elist=elist, lot1=lot1, lot2=lot2, log=FALSE,
  p.thresh=0.001, fold.thresh=3)