If the object is x, seqnames(x) and ranges(x)
slots demarcate the clusters discovered. There will be one element for
each cluster (aka ‘clump’) discovered.
Using the default argument pruneFun=prune.loglik or
pruneFun=noprune, mcols(x) will have these
columns:
value1 and
value2
are the counts of the two classes of
insertion sites for the clusters of object x
clump.id
numbers each cluster.
If the user supplies a custom pruneFun, it should return a
GRanges with those columns and one element for each unique
clump.id. The column target.min has the smallest nominal
False Discoveries Expected for each cluster and is added to (or
replaces) the mcols(x) produced by the argument supplied as
pruneFun.
metadata(x) will include these components:
criticalValues
A list object such as supplied by
critVal.target whose elements each give the cutpoints
to be used for a window with k sites.
attributes(metadata(object)$criticalValues[[i]]) will
contain elements
fdr
with dimension c(k+1,4) of target false
discovery expectations and and the one-sided p-values
target
the target for false discovery which sometimes
is specified a priori and sometimes results from calculation
n
an upper bound on the number of windows to screen, if
this number is needed.
In some cases, an attribute is attached to
metadata(object)$criticalValues, see
critVal.power for an example.
kvals
the number of sites, k, to include in a window
perm_cluster_best
a list whose canonical element is a vector of
values like x$target.min obtained from a permutation of the
class indicators
summary_matrix
a matrix giving the start, end, depth, and
counts in each class for every cluster and depth in sequential order
call
the call invoking gRxCluster
which may
include some arguments added by default.
providing 
.