Description Usage Arguments Value Methods (by class) Examples
This method takes a pangenome and calculate a similarity matrix based on
cosine similarity of kmer feature vectors in an all-vs-all fashion. The
result can subsequently be used to group genes either using
graphGrouping
or homebrewed grouping scheme. In case of the
latter manualGrouping
should be used to add the grouping back
to the pangenome.
1 2 3 4 5 | kmerSimilarity(object, ...)
## S4 method for signature 'pgVirtual'
kmerSimilarity(object, lowMem, kmerSize, lowerLimit,
rescale, transform, pParam)
|
object |
A pgVirtual subclass |
... |
parameters passed on. |
lowMem |
logical. Should low memory footprint be ensured over computation speed |
kmerSize |
The size of kmers to use for similarity calculations. |
lowerLimit |
The lower threshold for similarity below which it is set to 0 |
rescale |
Should Similarities be normalised between lowerLimit and 1 |
transform |
Transformation function to apply to similarities |
pParam |
An optional BiocParallelParam object that defines the workers used for parallelisation. |
A matrix (sparse or normal) with cosine similarity for each gene pair
pgVirtual
: Kmer based similarities for pgVirtual subclasses
1 2 3 4 5 6 7 | testPG <- .loadPgExample()
# Too heavy to include
## Not run:
kmerSim <- kmerSimilarity(testPG, lowerLimit=0.75)
## End(Not run)
|
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