R: Plot clustered heatmap of MQM scan on multiple phenotypes

mqmplot.clusteredheatmap {qtl} | R Documentation

## 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 danny.arends@gmail.com

### 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)

[Package qtl version 1.46-2 Index]