smoother: Smooth genomic profiles
In bumphunter: Bump Hunter

Description Usage Arguments Details Value Author(s) See Also Examples

Apply smoothing to values typically representing the difference between two populations across genomic regions.

1 2	smoother(y, x = NULL, cluster, weights = NULL, smoothFunction, verbose = TRUE, ...)

`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 smoother.
`smoothFunction`	A function to be used for smoothing the estimate of the genomic profile. Two functions are provided by the package: `loessByCluster` and `runmedByCluster`.
`verbose`	Boolean. Should progress be reported?
`...`	Further arguments to be passed to `smoothFunction`

This function is typically called by bumphunter prior to identifying candidate bump regions. Smoothing is carried out within regions defined by the cluster argument.

`fitted`	The smoothed data values
`smoothed`	A boolean vector indicating whether a given position was smoothed
`spans`	The span used by the loess smoother. One per cluster.
`clusterL`	The number of locations in each cluster.
`smoother`	The name of the smoother used

Rafael A. Irizarry and Martin J. Aryee

loessByCluster, runmedByCluster

dat <- dummyData()

# Enable parallelization
require(doParallel)
registerDoParallel(cores = 2)

## loessByCluster
smoothed <- smoother(y=dat$mat[,1], cluster=dat$cluster, smoothFunction=loessByCluster, 
                       bpSpan = 1000, minNum=7, minInSpan=5, maxSpan=1)
## runmedByCluster
smoothed <- smoother(y=dat$mat[,1], cluster=dat$cluster, smoothFunction=runmedByCluster, 
                     k=5, endrule="constant")
# cleanup, for Windows
bumphunter:::foreachCleanup()