Description Usage Arguments Details Value Author(s) References Examples
Estimate regions for which a genomic profile deviates from its baseline value. Originally implemented to detect differentially methylated genomic regions between two populations.
1 2 3 4 5 6  ## S4 method for signature 'matrix'
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, ...)
## S3 method for class 'bumps'
print(x, ...)

object 
An object of class matrix. 
x 
An object of class 
mat 
A matrix with rows representing genomic locations and columns representing samples. 
design 
Design matrix with rows representing samples and columns representing covariates. Regression is applied to each row of mat. 
chr 
A character vector with the chromosomes of each location. 
pos 
A numeric vector representing the chromosomal position. 
cluster 
The clusters of locations that are to be analyzed
together. In the case of microarrays, the clusters are many times
supplied by the manufacturer. If not available the function

coef 
An integer denoting the column of the design matrix containing the covariate of interest. The hunt for bumps will be only be done for the estimate of this coefficient. 
cutoff 
A numeric value. Values of the estimate of the genomic profile above the cutoff or below the negative of the cutoff will be used as candidate regions. It is possible to give two separate values (upper and lower bounds). If one value is given, the lower bound is minus the value. 
pickCutoff 
Should bumphunter attempt to pick a cutoff using the permutation distribution? 
pickCutoffQ 
The quantile used for picking the cutoff using the permutation distribution. 
maxGap 
If cluster is not provided this maximum location gap will be used to define cluster
via the 
nullMethod 
Method used to generate null candidate regions, must be one of ‘bootstrap’ or ‘permutation’ (defaults to ‘permutation’). However, if covariates in addition to the outcome of interest are included in the design matrix (ncol(design)>2), the ‘permutation’ approach is not recommended. See vignette and original paper for more information. 
smooth 
A logical value. If TRUE the estimated profile will be smoothed with the
smoother defined by 
smoothFunction 
A function to be used for smoothing the estimate of the genomic
profile. Two functions are provided by the package: 
useWeights 
A logical value. If 
B 
An integer denoting the number of resamples to use when computing
null distributions. This defaults to 0. If 
permutations 
is a matrix with columns providing indexes to be used to
scramble the data and create a null distribution when

verbose 
logical value. If 
... 
further arguments to be passed to the smoother functions. 
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 bootstrapbased familywide error rates (FWER) and pvalues 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 pvalues 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.
An object of class bumps
with the following components:
tab 
The table with candidate regions and annotation for these. 
coef 
The single loci coefficients. 
fitted 
The estimated genomic profile used to determine the regions. 
pvaluesMarginal 
marginal pvalue for each genomic location. 
null 
The null distribution. 
algorithm 
details on the algorithm. 
Rafael A. Irizarry, Martin J. Aryee, Kasper D. Hansen, and Shan Andrews.
Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. International Journal of Epidemiology 41(1):2009.
1 2 3 4 5 6 7 8 9 10 11 12  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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