Description Usage Arguments Author(s) See Also Examples
Plot the results from a MQM scan on multiple phenotypes.
1 |
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: http://www.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
1 2 3 4 5 6 | 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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