Create a de novo genetic map from offspring phenotype data stored in a population object
cross.denovo(population, n.chr, map=c("none", "genetic", "physical"),
comparisonMethod = c(sumMajorityCorrelation, majorityCorrelation, meanCorrelation,
majorityOfMarkers), assignFunction=c(assignMaximumNoConflicts, assignMaximum),
reOrder=TRUE, use.orderMarkers=FALSE, verbose=FALSE, debugMode=0, ...)An object of class population. See create.population for details.
Number of chromosomes expected on the map.
Which map ( from ones stored in population$maps) should be used for assigning chromosomes on the created map. If none is selected - assigning is not performed.
Method used to compare chromosomes from the new map to the original ones while assigning:
sumMajorityCorrelation - For each chromosome in cross for every marker checks the marker it is having highest correlation with. Checks on which chromosome this marker is placed in old map. For each of new chromosomes one or more of chromosomes from old map will be represented. Function sums correlations for each pair of those and for every new chromosomes assigns old chromosome with highest cumulative cor.
majorityCorrelation - For each chromosome in cross for every marker checks the marker it is having highest correlation with. Checks on which chromosome this marker is placed in old map. For each of new chromosomee, old chromosome with most markers with high correlation is assigned.
meanCorrelation - Assigning chromosome from new map to old ones using sum of the mean correlation between their markers.
majorityOfMarkers - For each chromosome in the cross object (either created inside the function or provided by user) chromosome from original map, where most markers from new chromosome are is assigned.
function used to assign chromosomes on the created map, in both cases for every chromosome from the new map, original chromosome with maximal score is assigned, but if one of the original chromosomes is assigned to more then one of new ones:
assignMaximumNoConflictsadditional step is performed to make sure each of the original chromosomes is used only once
assignMaximumthose two are being merged
if TRUE, cross object is returned, FALSE - vector showing how chromosomes should be assigned
should markers on the newly created map be ordered using R/qtl orderMarkers funtion
be verbose
1: Print our checks, 2: print additional time information
parameters passed directly to the formLinkageGroups function
When reordering this will produce an object of class cross, otherwise (reOrder=FALSE)
a chromosomes assignment vector (See assignChrToMarkers ) is produced which can be used to manual reorder the markers.
cross.denovo function creates new genetic map using genotypes simulated by generate.biomarkers function. Then it uses information provided by user to
assign number to newly created chromosomes.
reorganizeMarkersWithin - Apply new ordering on the cross object usign ordering vector.
assignChrToMarkers - Create ordering vector from chromosome assignment vector.
cross.saturate - Saturate existing map.
reduceChromosomesNumber - Number of routines to reduce number of chromosomes of cross object.
generate.biomarkers - Creating genotype markers out of gene expression data.
# NOT RUN {
data(testPopulation)
cross <- cross.denovo(testPopulation,n.chr=5,verbose=TRUE,map="genetic",
comparisonMethod=sumMajorityCorrelation, use.orderMarkers=FALSE)
# }
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