Description Usage Arguments Details Value Author(s) See Also Examples
Function usually called from within
callGenotypes.interactive
, in order to define clusters
by clicking and dragging with the mouse
1 | manualCall(marker, cntIdeal, classification, gg = NULL, close.gg = TRUE)
|
marker |
Data-frame containing the columns “Theta”, “R”, and
optionally “PedCheck” and “PedigreeID” for a single
marker. The first two correspond to |
cntIdeal |
A numeric vector of the allowed B allele ratios for a specific genotype category (see
|
classification |
Character string denoting genotype category (see
|
gg |
An instance of |
close.gg |
If |
A “GGobi” interactive scatter-plot is produced. Round dots with
colours purple, pink, red, blue, green and grey denote samples of
“Theta” values 0, 1/4, 1/2, 3/4, 1, and NA
,
respectively. Orange and brown square dots indicate offspring and
parent pedigree errors, respectively. Select (“brush”) points
by moving around the yellow rectangle visible on the screen using the
left mouse button. Change the shape of the rectangle using the right
mouse button.
If pedigree errors are found after clustering, a warning is issued, and the user is given the choice between un-assigning erroneous offspring, modifying the clusters, or disregarding the errors. Note that by setting erroneous samples to missing, the remaining calls may appear better than they are.
Depending on the value of close.gg
, a data-frame or an object
of class "ggobi"
is returned,
containing marker data including a column “Call” with the B
allele ratio for each subject
Lars Gidskehaug
ggobi
, callGenotypes.interactive
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
#Read 10 markers into an AlleleSetIllumina object
rPath <- system.file("extdata", package="beadarrayMSV")
normOpts <- setNormOptions()
dataFiles <- makeFilenames('testdata',normOpts,rPath)
beadFile <- paste(rPath,'beadData_testdata.txt',sep='/')
beadInfo <- read.table(beadFile,sep='\t',header=TRUE,as.is=TRUE)
BSRed <- createAlleleSetFromFiles(dataFiles[1:4],markers=1:10,
beadInfo=beadInfo)
#Prepare a single marker
ind <- 2
marker <- data.frame(Theta=assayData(BSRed)$theta[ind,],
R=assayData(BSRed)$intensity[ind,],
PedigreeID=pData(BSRed)$PedigreeID,
stringsAsFactors=FALSE)
#Cluster marker from scratch, assuming MSV-5
polyCent <- generatePolyCenters(ploidy="tetra")
iMSV5 <- 7
marker1 <- manualCall(marker,cntIdeal=polyCent$centers[[iMSV5]],
classification=polyCent$classification[[iMSV5]],close.gg=FALSE)
## End(Not run)
|
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