Interactive LOD curve

Description

Creates an interactive graph of a set of single-QTL genome scans, as calculated by scanone. If cross or effects are provide, LOD curves will be linked to a panel with estimated QTL effects.

Usage

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iplotMScanone(scanoneOutput, cross = NULL, lodcolumn = NULL,
  pheno.col = NULL, times = NULL, effects = NULL, chr = NULL,
  chartOpts = NULL, digits = 5)

Arguments

scanoneOutput

Object of class "scanone", as output from scanone.

cross

(Optional) Object of class "cross", see read.cross.

lodcolumn

Numeric value indicating LOD score column to plot.

pheno.col

(Optional) Phenotype column in cross object.

times

(Optional) Vector (length equal to the number of LOD score columns) with quantitative values to which the different LOD score columns correspond (times of measurements, or something like age or dose). These need to be ordered and equally-spaced. If omitted, the names of the columns in scanoneOutput are used and treated as qualitative.

effects

(Optional)

chr

(Optional) Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used.

chartOpts

A list of options for configuring the chart (see the coffeescript code). Each element must be named using the corresponding option.

digits

Round data to this number of significant digits before passing to the chart function. (Use NULL to not round.)

Details

If cross is provided, Haley-Knott regression is used to estimate QTL effects at each pseudomarker.

Value

An object of class htmlwidget that will intelligently print itself into HTML in a variety of contexts including the R console, within R Markdown documents, and within Shiny output bindings.

See Also

iplotScanone

Examples

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data(grav)
library(qtl)
grav <- calc.genoprob(grav, step=1)
grav <- reduce2grid(grav)

# we're going to subset the phenotypes
phecol <- seq(1, nphe(grav), by=5)

# the times were saved as an attributed
times <- attr(grav, "time")[phecol]

# genome scan
out <- scanone(grav, phe=phecol, method="hk")


# plot with qualitative labels on y-axis
iplotMScanone(out)


# plot with quantitative y-axis
iplotMScanone(out, times=times)

# estimate QTL effect for each time point at each genomic position
eff <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5), what="effects")


# plot with QTL effects included (and with quantitative y-axis)
iplotMScanone(out, effects=eff, times=times,
              chartOpts=list(eff_ylab="QTL effect", eff_xlab="Time (hrs)"))