Proportional sample exposures will be used as input into the
umap function to generate a two dimensional UMAP.
create_umap(result, n_neighbors = 30, min_dist = 0.75, spread = 1)
The size of local neighborhood used for views of
manifold approximation. Larger values result in more global the manifold,
while smaller values result in more local data being preserved.
The effective minimum distance between embedded points.
Smaller values will result in a more clustered/clumped embedding where
nearby points on the manifold are drawn closer together, while larger
values will result on a more even dispersal of points. Default
The effective scale of embedded points. In combination with
‘min_dist’, this determines how clustered/clumped the embedded points are.
musica_result object with a new UMAP
stored in the
data(res_annot) create_umap(result = res_annot)#>