iplotMScanone: Interactive LOD curve

Description

Creates an interactive graph of a set of single-QTL genome scans, as calculated by [qtl::scanone()]. If `cross` or `effects` are provided, LOD curves will be linked to a panel with estimated QTL effects.

Usage

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 [qtl::scanone()].

cross

(Optional) Object of class `"cross"`, see [qtl::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) Estimated QTL effects, as obtained with [estQTLeffects()].

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.)

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.

Details

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

Examples

Run this code

# NOT RUN {
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")

# }
# NOT RUN {
# plot with qualitative labels on y-axis
iplotMScanone(out)
# }
# NOT RUN {
# }
# NOT RUN {
# plot with quantitative y-axis
iplotMScanone(out, times=times)
# }
# NOT RUN {
# estimate QTL effect for each time point at each genomic position
eff <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5), what="effects")

# }
# NOT RUN {
# 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)"))
# }
# NOT RUN {
# }