mqmplot.clusteredheatmap {qtl}

Plot clustered heatmap of MQM scan on multiple phenotypes

Description

Plot the results from a MQM scan on multiple phenotypes.

Usage

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"), ...)

Arguments

cross

An object of class cross. See read.cross for details.

mqmresult

Result object from mqmscanall, the object needs to be of class mqmmulti

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 heatmap.

Author(s)

Danny Arends [e-mail]

See Also

• 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

Examples

data(multitrait)

multitrait <- fill.geno(multitrait) # impute missing genotype data
result <- mqmscanall(multitrait, logtransform=TRUE)
cresults <- mqmplot.clusteredheatmap(multitrait,result)
groupclusteredheatmap(multitrait,cresults,10)