Plot clustered heatmap of MQM scan on multiple phenotypes
Plot the results from a MQM scan on multiple phenotypes.
mqmplot.clusteredheatmap(cross, mqmresult, directed=TRUE, legend=FALSE,
Colv=NA, scale="none", verbose=FALSE,
breaks = c(-100,-10,-3,0,3,10,100),
col = c("darkblue","blue","lightblue","yellow",
"orange","red"), ...)cross |
An object of class |
mqmresult |
Result object from mqmscanall, the object needs to be of class |
directed |
Take direction of QTLs into account (takes more time because of QTL direction calculations |
legend |
If TRUE, add a legend to the plot |
Colv |
Cluster only the Rows, the columns (Markers) should not be clustered |
scale |
character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default "none" |
verbose |
If TRUE, give verbose output. |
breaks |
Color break points for the LOD scores |
col |
Colors used between breaks |
... |
Additional arguments passed to |
Danny Arends danny.arends@gmail.com
The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf
MQM - MQM description and references
mqmscan - Main MQM single trait analysis
mqmscanall - Parallellized traits analysis
mqmaugment - Augmentation routine for estimating missing data
mqmautocofactors - Set cofactors using marker density
mqmsetcofactors - Set cofactors at fixed locations
mqmpermutation - Estimate significance levels
scanone - Single QTL scanning
data(multitrait) multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) cresults <- mqmplot.clusteredheatmap(multitrait,result) groupclusteredheatmap(multitrait,cresults,10)
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