lineup (version 0.34-1)

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("blue","green","red","orange"), ...)

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 plot and points.

Value

• None.

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 pairs to produce a matrix of scatterplots.

See Also

disteg, plot.lineupdist, plot2dist, knn

Examples

##############################
# simulate an eQTL data set
##############################
# genetic map
L <- seq(120, length=8, by=-10)
map <- sim.map(L, n.mar=L/10+1, include.x=FALSE, eq.spacing=TRUE)

# physical map: make all intervals 2x longer
pmap <- rescalemap(map, 2)

# arbitrary locations of 40 local eQTL
thepos <- unlist(map)
theppos <- unlist(pmap)
thechr <- rep(seq(along=map), sapply(map, length))
eqtl.loc <- sort(sample(seq(along=thepos), 40))

x <- sim.cross(map, n.ind=250, type="f2",
               model=cbind(thechr[eqtl.loc], thepos[eqtl.loc], 0, 0))
x$pheno$id <- factor(paste("Mouse", 1:250, sep=""))

# first 20 have eQTL with huge effects
# second 20 have essentially no effect
edata <- cbind((x$qtlgeno[,1:20] - 2)*10+rnorm(prod(dim(x$qtlgeno[,1:20]))),
               (x$qtlgeno[,21:40] - 2)*0.1+rnorm(prod(dim(x$qtlgeno[,21:40]))))
dimnames(edata) <- list(x$pheno$id, paste("e", 1:ncol(edata), sep=""))

# gene locations
theloc <- data.frame(chr=thechr[eqtl.loc], pos=theppos[eqtl.loc])
rownames(theloc) <- colnames(edata)

# mix up 5 individuals in expression data
edata[1:3,] <- edata[c(2,3,1),]
edata[4:5,] <- edata[5:4,]

##############################
# now, the start of the analysis
##############################
x <- calc.genoprob(x, step=1)

# find nearest pseudomarkers
pmark <- find.gene.pseudomarker(x, pmap, theloc, "prob")

# calculate LOD score for local eQTL
locallod <- calc.locallod(x, edata, pmark)

# take those with LOD > 100 [which will be the first 20]
edatasub <- edata[,locallod>100,drop=FALSE]

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

# plot distances
plot(d)

# summary of apparent mix-ups
summary(d)

# plot of classifier for first eQTL
plotEGclass(d)