combinedist | R Documentation |
Combine multiple distance matrices into a single distance matrix providing an overall summary
combinedist(..., method = c("median", "mean"))
... |
Set of distance matrices, as calculated by |
method |
Indicates whether to summarize using the median or the mean. |
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.
A distance matrix, with class "lineupdist"
. The individual
IDs are in the row and column names.
Karl W Broman, broman@wisc.edu
distee()
, disteg()
,
summary.lineupdist()
library(qtl) # load example data data(f2cross, expr1, expr2, pmap, genepos) # 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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