Description Usage Arguments Examples
For small-size (< 5000) datasets, we don't need to partition the datasets into several groups. Instead, we simply use ensemble random projection and weighted ensemble meta-clustering algorithms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | SHARP_small(
scExp,
ncells,
ensize.K,
reduced.ndim,
hmethod,
N.cluster,
indN.cluster,
minN.cluster,
maxN.cluster,
sil.thre,
height.Ntimes,
flashmark,
flag,
n.cores,
forview,
rN.seed
)
|
scExp |
input single-cell expression matrix |
ncells |
number of single cells |
ensize.K |
number of applications of random projection for ensemble |
reduced.ndim |
the dimension to be reduced to |
1 | enresults = SHARP_small(scExp, ncells, ensize.K, reduced.ndim)
|
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