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)

## Arguments

result |
A `musica_result` object generated by
a mutational discovery or prediction tool. |

n_neighbors |
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.
If `n_neighbors` is larger than the number of samples,
then `n_neighbors` will automatically be set to the number of samples
in the `musica_result` . Default `30` . |

min_dist |
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 `0.2` . |

spread |
The effective scale of embedded points. In combination with
‘min_dist’, this determines how clustered/clumped the embedded points are.
Default `1` . |

## Value

A `musica_result`

object with a new UMAP
stored in the `UMAP`

slot.

## See also

See plot_umap to display the UMAP and
`umap`

for more information on the individual parameters
for generating UMAPs.

## Examples

#> The parameter 'n_neighbors' cannot be bigger than the total number of samples. Setting 'n_neighbors' to 7.