Description Usage Arguments See Also Examples
The function to do MPR inference in whole chromosome by using localMPR to infer parental genotypes in hundreds of local regions and assemble them aiding with low-coverage sequences of one parent or known markers.
1 2 3 4 5 6 | globalMPRByMarkers(baseData, markers = NULL, alleleA = NULL, numTry = 3,
numBaseStep = 50, numBaseCandidateStep = numBaseStep * 2,
numKnownStep = pmax(numBaseStep/5, 10),
numKnownCandidateStep = numKnownStep * 1.5,
useMedianToFindKnown = TRUE, maxNStep = 3, scoreMin = 0.8, verbose = FALSE,
strSTART = "\r", strEND = "", ...)
|
baseData |
(Necessary input) character matrix of SNP dataset |
markers |
character vector of markers data with SNP position names |
alleleA |
(Necessary input) character vector of one parent allele |
numTry |
maximum number of the times of using one SNP from one group (or RIL). |
numBaseStep |
number of SNP to run localMPR(). |
numBaseCandidateStep |
number of SNP candidate in one step |
numKnownStep |
number of makers to run localMPR(). |
numKnownCandidateStep |
number of makers candidate in one step |
useMedianToFindKnown |
In one local genomic region (window), we will choose the nearest some makers to this region. Median of the region will be the center. |
maxNStep |
parameter of localMPR() |
scoreMin |
be used to control the accuracy of MPR in one local genomic region (one window). |
verbose |
report verbose progress |
strSTART |
part of displaying format (verbose) |
strEND |
part of displaying format (verbose) |
... |
arguments to be passed to other methods. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## load sample dataset
data(snpData)
data(markerData)
## select 30 markers randomly
set.seed(123);markers <- sample(names(markerData)[10:50],20)
## select SNP sites which contain the 30 markers
ids <- match(markers,rownames(snpData))
str(myBaseData <- snpData[min(ids):max(ids),])
## global MPR aiding with marker data
allele.MPR <- globalMPRByMarkers(myBaseData,markers=markerData,numTry=3,
numBaseStep=50,numBaseCandidateStep=100,
numMarkerStep=10,useMedianToFindKnown=TRUE,
maxNStep=3,scoreMin=0.8,verbose=TRUE)
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