findAllOutliers {beadarray} | R Documentation |
Function to find all beads which are outliers for their particular bead type on a given strip/array using Illumina's standard outlier detection method.
findAllOutliers(BLData, array, log=FALSE, n=3, what="G", usewts=FALSE)
BLData |
BeadLevelList |
array |
integer specifying which strip/array we want to find outliers on |
log |
if TRUE the intensities will be calculated on the log2 scale. Otherwise un-logged data is used. |
n |
numeric value defining a cut-off for the number of median absolute deviations (MADs) from the median to use for determining outliers. The default value is 3. |
what |
character string specifying which intensities to use.
See getArrayData for a list of possibilities. |
usewts |
if TRUE, then beads with weights below 1 will be discarded prior to analysis. |
We find the outliers for each bead type on the array in
turn using the findBeadStatus
function and
store the indices of the outliers found. By default, outliers
for a particular bead type are determined using a 3
MAD cut-off from the median.
numeric vector giving the row indices of BLData
(in the
range 1 to total number of beads on the array) of all beads
that are outliers for their bead type.
Mark Dunning
data(BLData) # how many outliers are there on the original scale? length(findAllOutliers(BLData, 1)) # how many outliers are there on the log2-scale? length(findAllOutliers(BLData, 1, log=TRUE)) # # how many outliers are there using a 4 MAD # cut-off from the median? length(findAllOutliers(BLData, 1, n=4))