blockFinder {minfi} | R Documentation

## Finds blocks of methylation differences for Illumina methylation arrays

### Description

Finds blocks (large scale regions) of methylation differences for Illumina methylation arrays

### 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`.

### Details

The approximately 170,000 open sea probes on the 450k can be used to detect long-range changes in methylation status. These large scale changes that can range up to several Mb have typically been identified only through whole-genome bisulfite sequencing. `blockFinder` groups the averaged methylation values in open-sea probe clusters (See cpgCollapse) into large regions in which the bumphunter procedure is applied with a large (250KB+) smoothing window.

Note that estimating the precise boundaries of these blocks are constrained by the resolution of the array.

### Value

FIXME

### See Also

`cpgCollapse`, and `bumphunter`

[Package minfi version 1.32.0 Index]