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lineup (version 0.44)

combinedist: Combine distance matrices into a single such

Description

Combine multiple distance matrices into a single distance matrix providing an overall summary

Usage

combinedist(..., method = c("median", "mean"))

Value

A distance matrix, with class "lineupdist". The individual IDs are in the row and column names.

Arguments

...

Set of distance matrices, as calculated by distee() or disteg().

method

Indicates whether to summarize using the median or the mean.

Author

Karl W Broman, broman@wisc.edu

Details

The row and column names of the input distance matrices define the individual IDs.

If the input distance matrices all have an attribute "denom" (for denominator) and method="mean", we use a weighted mean, weighted by the denominators. This could be used to calculate an overall proportion.

See Also

distee(), disteg(), summary.lineupdist()

Examples

Run this code
library(qtl)

# load example data
data(f2cross, expr1, expr2, 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]
expr2 <- expr2[,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
id1 <- findCommonID(f2cross, expr1)
id2 <- findCommonID(f2cross, expr2)

# calculate LOD score for local eQTL
locallod1 <- calc.locallod(f2cross[,id1$first], expr1[id1$second,], pmark)
locallod2 <- calc.locallod(f2cross[,id2$first], expr2[id2$second,], pmark)

# take those with LOD > 25
expr1s <- expr1[,locallod1>25,drop=FALSE]
expr2s <- expr2[,locallod2>25,drop=FALSE]

# calculate distance between individuals
#     (prop'n mismatches between obs and inferred eQTL geno)
d1 <- disteg(f2cross, expr1s, pmark)
d2 <- disteg(f2cross, expr2s, pmark)

# combine distances
d <- combinedist(d1, d2)

# summary of problem samples
summary(d)

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