Classification-powered Amulet-like method
Usage
clamulet(
x,
artificialDoublets = NULL,
iter = 2,
k = NULL,
minCount = 0.001,
maxN = 500,
nfeatures = 25,
max_depth = 5,
threshold = 0.75,
returnAll = FALSE,
verbose = TRUE,
...
)Arguments
- x
The path to a fragment file (see
getFragmentOverlapsfor performance/memory-related guidelines)- artificialDoublets
The number of artificial doublets to generate
- iter
The number of learning iterations (should be 1 to)
- k
The number(s) of nearest neighbors at which to gather statistics
- minCount
The minimum number of cells in which a locus is detected to be considered. If lower than 1, it is interpreted as a fraction of the number of cells.
- maxN
The maximum number of regions per cell to consider to establish windows for meta-features
- nfeatures
The number of meta-features to consider
- max_depth
The maximum tree depth
- threshold
The score threshold used during iterations
- returnAll
Logical; whether to return data also for artificial doublets
- verbose
Logical; whether to print progress information
- ...
Arguments passed to
getFragmentOverlaps
Details
`clamulet` operates similarly to the `scDblFinder` method, but generates doublets by operating on the fragment coverages. This has the advantage that the number of loci covered by more than two reads can be computed for artificial doublets, enabling the use of this feature (along with the kNN-based ones) in a classification scheme. It however has the disadvantage of being rather slow and memory hungry, and appears to be outperformed by a simple p-value combination of the two methods (see vignette).