normalizeWithinArrays.SNP {beadarraySNP} | R Documentation |
Perform within array normalization on Illumina Golden Gate bead arrays.
normalizeWithinArrays.SNP(object, callscore=0.5, normprob=0.5, quantilepersample=FALSE, relative=FALSE, fixed=FALSE, useAll=FALSE, subsample="OPA", Q.scores="callProbability")
object |
class SnpSetIllumina. |
callscore |
numeric with range 0:1, threshold for probe inclusion. |
normprob |
numeric with range 0:1, target quantile for normalization. The default is to divide the sample intensities by the median of the selected subset. |
quantilepersample |
logical. If TRUE then the threshold is
determined for each sample, else it is experiment wide. This is only
relevant when fixed is FALSE . |
relative |
logical. If TRUE then the ratio of GCS and GTS is used,
else only the GCS is used as the quality score. |
fixed |
logical. If TRUE then callscore is the fixed
threshold for the quality score, else the probes above the quantile
callscore are used. |
useAll |
logical. If TRUE then all probes in the dataset are
eligible as the invariant set, else only the heterozygous SNPs. |
subsample |
factor or column name in featureData slot, the levels
of the factor are treated separately. |
Q.scores |
The function uses high quality heterozygous SNPs as an invariant set with the
assumption that these have the highest probability of coming from
unaffected regions of the genome. Most of the arguments are used to determine
the quality of the call.
This function returns a SnpSetIllumina
object.
Jan Oosting
SnpSetIllumina
,normalizeLoci.SNP
,
backgroundCorrect.SNP
,normalizeBetweenAlleles.SNP
data(chr17.260) data.nrm <- normalizeWithinArrays.SNP(chr17.260)