MDSMap-package: High density Genetic Linkage Mapping using Multidimensional...
In MDSMap: High Density Genetic Linkage Mapping using Multidimensional Scaling
Description
Details
Author(s)
Examples
Description
MDSmap provides functions for estimating genetic linkage maps for
markers from a single linkage group from pairwise intermarker map
distances using the Haldane or Kosambi map function; or recombination
fractions. It either uses constrained weighted metric multidimensional
scaling (cMDS) in 2 dimensions or unconstrained weighted metric
multidimensional scaling (MDS) followed by fitting a principal curve
(PC) in either 2 or 3 dimensions. Pairwise distances can be weighted
either by the LOD score or LOD2. There are functions for diagnostic
plots, estimating the difference between the observed and estimated
difference between points and their nearest informative neighbour,
which may be useful in deciding which weights to use and also for
testing estimated maps against a map estimated externally.
Details
The main top level functions to use: calc.maps.pc and
calc.maps.sphere, and use plot.pcmap,
plot.spheremap or plot.pcmap3d to visualize
the result.
Author(s)
Katharine F. Preedy <Katharine.preedy@bioss.ac.uk>
Examples
1
2
3
map<-calc.maps.pc(system.file("extdata", "lgV.txt", package="MDSMap"),
ndim=2,weightfn='lod2',mapfn='kosambi')
plot(map)
MDSMap documentation built on May 1, 2019, 6:51 p.m.
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Description Details Author(s) Examples
Description
MDSmap provides functions for estimating genetic linkage maps for markers from a single linkage group from pairwise intermarker map distances using the Haldane or Kosambi map function; or recombination fractions. It either uses constrained weighted metric multidimensional scaling (cMDS) in 2 dimensions or unconstrained weighted metric multidimensional scaling (MDS) followed by fitting a principal curve (PC) in either 2 or 3 dimensions. Pairwise distances can be weighted either by the LOD score or LOD2. There are functions for diagnostic plots, estimating the difference between the observed and estimated difference between points and their nearest informative neighbour, which may be useful in deciding which weights to use and also for testing estimated maps against a map estimated externally.
Details
The main top level functions to use: calc.maps.pc and
calc.maps.sphere, and use plot.pcmap,
plot.spheremap or plot.pcmap3d to visualize
the result.
Author(s)
Katharine F. Preedy <Katharine.preedy@bioss.ac.uk>
Examples
1 2 3 | map<-calc.maps.pc(system.file("extdata", "lgV.txt", package="MDSMap"),
ndim=2,weightfn='lod2',mapfn='kosambi')
plot(map)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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