R: Plot phenotypes versus marker genotypes

plotPXG {qtl} | R Documentation

## Plot phenotypes versus marker genotypes

### Description

Plot the phenotype values versus the genotypes at a marker or markers.

### Usage

    plotPXG(x, marker, pheno.col=1, jitter=1, infer=TRUE,
             pch, ylab, main, col, ...)

### Arguments

`x` | An object of class `cross`. See `read.cross` for details.
`marker` | Marker name (a character string; can be a vector).
`pheno.col` | Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations.
`jitter` | A positive number indicating how much to spread out the points horizontally. (Larger numbers correspond to greater spread.)
`infer` | If TRUE, missing genotypes are filled in with a single random imputation and plotted in red; if FALSE, only individuals typed at the specified marker are plotted.
`pch` | Plot symbol.
`ylab` | Label for y-axis.
`main` | Main title for the plot. If missing, the names of the markers are used.
`col` | A vector of colors to use for the confidence intervals (optional).
`...` | Passed to `plot`.

### Details

Plots the phenotype data against the genotypes at the specified marker. If infer=TRUE, the genotypes of individuals that were not typed is inferred based the genotypes at linked markers via a single imputation from `sim.geno`; these points are plotted in red. For each genotype, the phenotypic mean is plotted, with error bars at +/- 1 SE.

### Value

A data.frame with initial columns the marker genotypes, then the phenotype data, then a column indicating whether any of the marker genotypes were inferred (1=at least one genotype inferred, 0=none were inferred).

### Author(s)

Karl W Broman, kbroman@biostat.wisc.edu; Brian Yandell

### See Also

`find.marker`, `effectplot`, `find.flanking`, `effectscan`

### Examples

    data(listeria)
    mname <- find.marker(listeria, 5, 28) # marker D5M357
    plotPXG(listeria, mname)

    mname2 <- find.marker(listeria, 13, 26) # marker D13Mit147
    plotPXG(listeria, c(mname, mname2))
    plotPXG(listeria, c(mname2, mname))

    # output of the function contains the raw data
    output <- plotPXG(listeria, mname)
    head(output)

    # another example
    data(fake.f2)
    mname <- find.marker(fake.f2, 1, 37) # marker D1M437
    plotPXG(fake.f2, mname)

    mname2 <- find.marker(fake.f2, "X", 14) # marker DXM66
    plotPXG(fake.f2, mname2)

    plotPXG(fake.f2, c(mname,mname2))
    plotPXG(fake.f2, c(mname2,mname))

[Package qtl version 1.41-6 Index]