bumphunter (version 1.12.0)

locfitByCluster: Apply local regression smoothing to values within each spatially-defined cluster.

Description

Local regression smoothing with a gaussian kernal, is applied independently to each cluster of genomic locations. Locations within the same cluster are close together to warrant smoothing across neighbouring locations.

Usage

locfitByCluster(y, x = NULL, cluster, weights = NULL, minNum = 7, bpSpan = 1000, minInSpan = 0, verbose = TRUE)

Arguments

y

A vector or matrix of values to be smoothed. If a matrix, each column represents a sample.

x

The genomic location of the values in y

cluster

A vector indicating clusters of locations. A cluster is typically defined as a region that is small enough that it makes sense to smooth across neighbouring locations. Smoothing will only be applied within a cluster, not across locations from different clusters.

weights

weights used by the locfit smoother

minNum

Clusters with fewer than minNum locations will not be smoothed

bpSpan

The span used when locfit smoothing. (Expressed in base pairs.)

minInSpan

Only smooth the region if there are at least this many locations in the span.

verbose

Boolean. Should progress be reported?

Value

fitted

The smoothed data values

smoothed

A boolean vector indicating whether a given position was smoothed

smoother

always set to ‘locfit’.

Details

This function is typically called by smoother, which is in turn called by bumphunter.

See Also

smoother, runmedByCluster, loessByCluster

Examples

dat <- dummyData()
smoothed <- locfitByCluster(y=dat$mat[,1], cluster=dat$cluster, bpSpan = 1000,
                         minNum=7, minInSpan=5)