clusterContigs,StrandStateMatrix-method {contiBAIT} | R Documentation

## clusterContigs – agglomeratively clusters contigs into linkage groups based on strand inheritance

### Description

clusterContigs – agglomeratively clusters contigs into linkage groups based on strand inheritance

### Usage

    ## S4 method for signature 'StrandStateMatrix'
    clusterContigs(object, similarityCutoff = 0.7,
      recluster = NULL, minimumLibraryOverlap = 5, randomise = TRUE,
      randomSeed = NULL, randomWeight = NULL, clusterParam = NULL,
      clusterBy = "hetero", verbose = TRUE)

### Arguments

`object` | `data.frame` containing strand inheritance information for every contig (rows) in every library (columns). This should be the product of strandSeqFreqTable
`similarityCutoff` | place contigs in a cluster when their strand state is at least this similar
`recluster` | Number of times to recluster and take the consensus of. If NULL, clustering is run only once.
`minimumLibraryOverlap` | for two contigs to be clustered together, the strand inheritance must be present for both contigs in at least this many libraries (in addition to their similarity being at least similarityCutoff)
`randomise` | whether to reorder contigs before clustering
`randomSeed` | random seed to initialize clustering
`randomWeight` | vector of weights for contigs for resampling. If NULL, uniform resampling is used. Typically this should be a measure of contig quality, such as library coverage, so that clustering tends to start from the better quality contigs.
`clusterParam` | optional `BiocParallelParam` specifying cluster to use for parallel execution. When `NULL`, execution will be serial.
`clusterBy` | Method for performing clustering. Default is 'hetero' (for comparing heterozygous calls to homozygous). Alternative is 'homo' (for compairson between the two homozygous calls)
`verbose` | prints function progress

### Details

Note that a more stringent similarity cutoff will result in more clusters, and a longer run time, since at every iteration a distance is computed to the existing clusters. However, in lower-quality data, a more stringent cutoff may be necessary to reduce the number of contigs that are erroneously grouped.

Note that `clusterParam` requires `BiocParallel` to be installed.

### Value

`LinkageGroupList` of vectors containing labels of contigs belonging to each linkage group

### Examples

    data("exampleWCMatrix")

    clusteredContigs <- clusterContigs(exampleWCMatrix, verbose=FALSE)
    show(clusteredContigs)
    show(clusteredContigs[[1]])

[Package contiBAIT version 1.20.0 Index]