Usage
blockFinder(object, design, coef = 2, what = c("Beta", "M"), cluster = NULL, cutoff = NULL, pickCutoff = FALSE, pickCutoffQ = 0.99, nullMethod = c("permutation","bootstrap"), smooth = TRUE, smoothFunction = locfitByCluster, B = ncol(permutations), permutations = NULL, verbose = TRUE, bpSpan = 2.5*10^5,...)
Arguments
object
An object of class GenomicRatioSet.
design
Design matrix with rows representing samples and columns
representing covariates. Regression is applied to each row of mat.
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.
what
Should blockfinding be performed on M-values or Beta
values?
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
clusterMaker can be used to cluster nearby locations. 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 a cutoff be picked automatically?
pickCutoffQ
The quantile used for picking the cutoff using the
permutation distribution.
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
smoothFunction
A function to be used for smoothing the estimate of the genomic
profile. Two functions are provided by the package: loessByCluster
and runmedByCluster.
B
An integer denoting the number of resamples to use when computing
null distributions. This defaults to 0. If permutations is
supplied that defines the number of permutations/bootstraps and B is
ignored.
permutations
is a matrix with columns providing indexes to be used to
scramble the data and create a null distribution. If this matrix is not supplied and B>0 then
these indexes created using the function sample.
verbose
Should the function be verbose?
bpSpan
Smoothing span. Note that this defaults to a large value
becuase we are searching for large scale changes.
...
further arguments sent to bumphunterEngine.