plot_genoprob: Plot genotype probabilities for one individual on one...

In qtl2: Quantitative Trait Locus Mapping in Experimental Crosses

Plot genotype probabilities for one individual on one chromosome.

Description

Plot the genotype probabilities for one individual on one chromosome, as a heat map.

Usage

plot_genoprob(
  probs,
  map,
  ind = 1,
  chr = NULL,
  geno = NULL,
  color_scheme = c("gray", "viridis"),
  col = NULL,
  threshold = 0,
  swap_axes = FALSE,
  ...
)

## S3 method for class 'calc_genoprob'
plot(x, ...)

Arguments

probs	Genotype probabilities (as produced by calc_genoprob()) or allele dosages (as produced by genoprob_to_alleleprob()).
map	Marker map (a list of vectors of marker positions).
ind	Individual to plot, either a numeric index or an ID.
chr	Selected chromosome to plot; a single character string.
geno	Optional vector of genotypes or alleles to be shown (vector of integers or character strings)
color_scheme	Color scheme for the heatmap (ignored if col is provided).
col	Optional vector of colors for the heatmap.
threshold	Threshold for genotype probabilities; only genotypes that achieve this value somewhere on the chromosome will be shown.
swap_axes	If TRUE, swap the axes, so that the genotypes are on the x-axis and the chromosome position is on the y-axis.
...	Additional graphics parameters passed to graphics::image().
x	Genotype probabilities (as produced by calc_genoprob()) or allele dosages (as produced by genoprob_to_alleleprob()). (For the S3 type plot function, this has to be called x.)

Value

None.

Hidden graphics parameters

A number of graphics parameters can be passed via .... For example, hlines, hlines_col, hlines_lwd, and hlines_lty to control the horizontal grid lines. (Use hlines=NA to avoid plotting horizontal grid lines.) Similarly vlines, vlines_col, vlines_lwd, and vlines_lty for vertical grid lines. You can also use many standard graphics parameters like xlab and xlim. These are not included as formal parameters in order to avoid cluttering the function definition.

See Also

plot_genoprobcomp()

Examples

# load data and calculate genotype probabilities
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron <- iron[,"2"] # subset to chr 2
map <- insert_pseudomarkers(iron$gmap, step=1)
pr <- calc_genoprob(iron, map, error_prob=0.002)

# plot the probabilities for the individual labeled "262"
#  (white = 0, black = 1)
plot_genoprob(pr, map, ind="262")

# change the x-axis label
plot_genoprob(pr, map, ind="262", xlab="Position (cM)")

# swap the axes so that the chromosome runs vertically
plot_genoprob(pr, map, ind="262", swap_axes=TRUE, ylab="Position (cM)")

# This is more interesting for a Diversity Outbred mouse example
## Not run: 
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/main/DOex/DOex.zip")
DOex <- read_cross2(file)
# subset to chr 2 and X and individuals labeled "232" and "256"
DOex <- DOex[c("232", "256"), c("2", "X")]
pr <- calc_genoprob(DOex, error_prob=0.002)
# plot individual "256" on chr 2 (default is to pick first chr in the probs)
plot_genoprob(pr, DOex$pmap, ind="256")

# omit states that never have probability >= 0.5
plot_genoprob(pr, DOex$pmap, ind="256", threshold=0.05)

# X chr male 232: just show the AY-HY genotype probabilities
plot_genoprob(pr, DOex$pmap, ind="232", chr="X", geno=paste0(LETTERS[1:8], "Y"))
# could also indicate genotypes by number
plot_genoprob(pr, DOex$pmap, ind="232", chr="X", geno=37:44)
# and can use negative indexes
plot_genoprob(pr, DOex$pmap, ind="232", chr="X", geno=-(1:36))

# X chr female 256: just show the first 36 genotype probabilities
plot_genoprob(pr, DOex$pmap, ind="256", chr="X", geno=1:36)

# again, can give threshold to omit genotypes whose probabilities never reach that threshold
plot_genoprob(pr, DOex$pmap, ind="256", chr="X", geno=1:36, threshold=0.5)

# can also look at the allele dosages
apr <- genoprob_to_alleleprob(pr)
plot_genoprob(apr, DOex$pmap, ind="232")

## End(Not run)

qtl2 documentation built on May 29, 2024, 11:46 a.m.