PAA (version 1.7.1)

batchFilter: Remove differential features regarding array batches/lots.

Description

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

Usage

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

Arguments

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).

Value

An EList or EListRaw object without differential features regarding array batches/lots.

Details

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.

Examples

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)