batchFilter {PAA} | R Documentation |
Finds differential features regarding array batches/lots and removes them.
batchFilter(elist=NULL, lot1=NULL, lot2=NULL, log=NULL, p.thresh=0.05, fold.thresh=1.5, output.path=NULL)
elist |
|
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.,
|
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) 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)