plotEGclass: Plot classifier of eQTL genotype from expression data

Description

Diagnostic plot of one of the eQTL classifiers from the results of disteg(): generally expression phenotype against observed eQTL genotype, colored by inferred eQTL genotype.

Usage

plotEGclass(
  d,
  eqtl = 1,
  outercol = "inferred",
  innercol = "observed",
  thecolors = c("#7B68ED", "#1B9E78", "#CA3767", "#E59E00"),
  ...
)

Value

None.

Arguments

d: Output of disteg().
eqtl: Numeric index or a character vector (of the form "1@102.35") indicating the eQTL to consider.
outercol: Indicates how to color the outer edge of the points: "observed" indicates to color based on observed genotypes; "inferred" indicates to color based on inferred genotypes; otherwise, give a color.
innercol: Like outercol, but indicating the interior of the points.
thecolors: The colors to use in the plot. The last element (after the number of genotypes) indicates the color to use for missing values.
...: Passed to base::plot() and graphics::points().

Author

Karl W Broman, broman@wisc.edu

Details

The function produces a diagnostic plot for studying one of the k-nearest neighbor classifiers underlying the output from disteg().

In the case of one expression phenotype attached to the selected eQTL, the plot is a dot plot of gene expression against observed eQTL genotype.

In the case of two expression phenotypes, the plot is a scatterplot of the two expression phenotypes against each other.

In the case of more than two expression phenotypes, we use graphics::pairs() to produce a matrix of scatterplots.

Examples

Run this code

library(qtl)

# load example data
data(f2cross, expr1, pmap, genepos)
# \dontshow{
keep <- c(1:20, 197, 553, 573, 740, 794, 822, 1474, 1522,
          1591, 1645, 2080, 2643, 2984, 3089, 3672, 4010, 4039,
          4159, 4191, 4198, 4213, 4401, 4544, 4593, 4925)
expr1 <- expr1[,keep]
genepos <- genepos[keep,]# }

# calculate QTL genotype probabilities
f2cross <- calc.genoprob(f2cross, step=1)

# find nearest pseudomarkers
pmark <- find.gene.pseudomarker(f2cross, pmap, genepos)

# line up individuals
id <- findCommonID(f2cross, expr1)

# calculate LOD score for local eQTL
locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark)

# take those with LOD > 25
expr1s <- expr1[,locallod>25,drop=FALSE]

# calculate distance between individuals
#     (prop'n mismatches between obs and inferred eQTL geno)
d <- disteg(f2cross, expr1s, pmark)

# plot of classifier for and second eQTL
par(mfrow=c(2,1), las=1)
plotEGclass(d)
plotEGclass(d, 2)

Run the code above in your browser using DataLab

The Learning Leader's Guide to AI Literacy