bumphunter.xml

<tool id="bumphunter" name="Bumphunter - Methylation analysis">
    <description>Estimate regions for which a genomic profile deviates from its
     baseline value. Originally implemented to detect differentially
     methylated genomic regions between two populations.</description>
     <requirements>
        <requirement type="binary">Rscript</requirement>
        <requirement type="R-module">Bumphunter</requirement>
        <requirement type="package" version="3.1.1">R_3_1_1</requirement>
        <requirement type="package" version="1.6.0">bumphunter</requirement>
        <!--<requirement type="set_environment">DESEQ2_SCRIPT_PATH</requirement>-->
    </requirements>
    <command interpreter="Rscript">bumphunter.R $inputMatrix</command>
  <!--   <inputs>
        <param format="bed" name="input1" type="data" label="TYPE_LABEL_HERE" help="ANY_FURTHER_INFO"></param>
    </inputs> -->
    <outputs>
        <data format="tabular" name="out_file1" />
    </outputs>
<help>

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.
</help>

<citations>
    <citation type="bibtex">@Manual{,
    title = {bumphunter: Bump Hunter},
    author = {Rafael A. Irizarry and Martin Ayree and Kasper Daniel Hansen and Hector Corrada Hansen},
    note = {R package version 1.6.0},
    url = {https://github.com/ririzarr/bumphunter},
  }</citation>
</citations>
</tool>