graphGrouping {FindMyFriends} | R Documentation

## Use igraph to create gene grouping from a similarity matrix

### Description

This method takes a similarity matrix based on all genes in the pangenome, converts it to a graph representation and uses one of igraphs community detection algorithms to split all genes into groups. Within the FindMyFriends framework the similarity matrix would usually come from `kmerSimilarity`, but it can just as well be defined in other ways e.g. be blast derived.

### Usage

    graphGrouping(object, ...)

    ## S4 method for signature 'pgVirtual'
    graphGrouping(object, similarity, algorithm, ...)

### Arguments

`object` | A pgVirtual subclass
`...` | parameters to be passed on to the community detection algorithm
`similarity` | A similarity matrix with rows and columns corresponding to the genes in the pangenome.
`algorithm` | A string naming the algorithm. See `communities` for an overview. The trailing '.community' can be omitted from the name. Default is 'infomap', which is also the recommended.

### Value

An object of the same class as 'object'.

### Methods (by class)

- `pgVirtual`: graph grouping for all pgVirtual subclasses

### See Also

Other grouping algorithms: `cdhitGrouping`, `gpcGrouping`, `manualGrouping`

### Examples

    testPG <- .loadPgExample()

    # Too heavy to include
    ## Not run:
    # Generate similarity matrix
    simMat <- kmerSimilarity(testPG, lowerLimit=0.75)

    # Group genes
    testPG <- graphGrouping(testPG, simMat)

    ## End(Not run)

[Package FindMyFriends version 1.19.0 Index]