"bumphunter"(object, design, chr=NULL, pos, cluster=NULL,coef=2, cutoff=NULL, pickCutoff = FALSE, pickCutoffQ = 0.99, maxGap=500, nullMethod=c("permutation","bootstrap"),smooth=FALSE,smoothFunction=locfitByCluster, useWeights=FALSE, B=ncol(permutations), permutations=NULL,verbose=TRUE, ...)
bumphunterEngine(mat, design, chr = NULL, pos, cluster = NULL, coef = 2, cutoff = NULL, pickCutoff = FALSE, pickCutoffQ = 0.99, maxGap = 500, nullMethod=c("permutation","bootstrap"), smooth = FALSE, smoothFunction = locfitByCluster, useWeights = FALSE, B=ncol(permutations), permutations=NULL, verbose = TRUE, ...)
"print"(x, ...)bumps.clusterMaker can be used to cluster nearby locations.clusterMaker function.smoothFunctionloessByCluster
and runmedByCluster.TRUE then the standard errors of the
point-wise estimates of the profile function will be used as weights
in the loess smoother loessByCluster. If the
runmedByCluster smoother is used this argument is ignored.permutations is
supplied that defines the number of permutations/bootstraps and B is
ignored.nullMethod is set to permutations. If the bootstrap approach is used this argument is
ignored. If this matrix is not supplied and B>0 then
these indexes are created using the function sample. TRUE, it writes out some messages
indicating progress. If FALSE nothing should be printed.
bumps with the following components:
This function performs the bumphunting approach described by Jaffe et al. International Journal of Epidemiology (2012). The main output is a table of candidate regions with permutation or bootstrap-based family-wide error rates (FWER) and p-values assigned.
The general idea is that for each genomic location we have a value for
several individuals. We also have covariates for each individual and
perform regression. This gives us one estimate of the coefficient of
interest (a common example is case versus control). These estimates are
then (optionally) smoothed. The smoothing occurs in clusters of
locations that are ‘close enough’. This gives us an estimate of a
genomic profile that is 0 when uninteresting. We then take values above
(in absolute value) cutoff as candidate regions. Permutations can
then performed to create null distributions for the candidate
regions.
The simplest way to use permutations or bootstraps to create a null distribution is to
set B. If the number of samples is large this can be set to a
large number, such as 1000. Note that this will be slow and we have
therefore provided parallelization capabilities. In cases were the user
wants to define the permutations or bootstraps, for example cases in which all
possible permutations/boostraps can be enumerated, these can be supplied via the
permutations argument.
Uncertainty is assessed via permutations or bootstraps. Each of the B
permutations/bootstraps will produce an estimated ‘null profile’ from which we
can define ‘null candidate regions’. For each observed candidate region we
determine how many null regions are ‘more extreme’ (longer and
higher average value). The ‘p.value’ is the percent of candidate
regions obtained from the permutations/boostraps that are as extreme as the observed
region. These p-values should be interpreted with care as the
theoretical proporties are not well understood. The ‘fwer’ is
the proportion of permutations/bootstraps that had at least one region as extreme as
the observed region. We compute p.values and FWER for the area of the
regions (as opposed to length and value as a pair) as well.
Note that for cases with more than one covariate the permutation
approach is not generally recommended; the nullMethod argument will coerce
to ‘bootstrap’ in this scenario. See vignette and original paper for more information.
Parallelization is implemented through the foreach package.
dat <- dummyData()
# Enable parallelization
require(doParallel)
registerDoParallel(cores = 2)
# Find bumps
bumps <- bumphunter(dat$mat, design=dat$design, chr=dat$chr, pos=dat$pos,
cluster=dat$cluster, coef=2, cutoff= 0.28, nullMethod="bootstrap",
smooth=TRUE, B=250, verbose=TRUE,
smoothFunction=loessByCluster)
bumps
# cleanup, for Windows
bumphunter:::foreachCleanup()
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