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umap: Uniform Manifold Approximation and Projection¶
Docstring:
Usage: qiime diversity umap [OPTIONS]
Apply Uniform Manifold Approximation and Projection.
Inputs:
--i-distance-matrix ARTIFACT
DistanceMatrix The distance matrix on which UMAP should be
computed. [required]
Parameters:
--p-number-of-dimensions INTEGER
Range(2, None) Dimensions to reduce the distance matrix to.
[default: 2]
--p-n-neighbors INTEGER
Range(1, None) Provide the balance between local and global
structure. Low values prioritize the preservation of
local structures. Large values sacrifice local
details for a broader global embedding. [default: 15]
--p-min-dist NUMBER Controls the cluster size. Low values cause clumpier
Range(0, None) clusters. Higher values preserve a broad topological
structure. To get less overlapping data points the
default value is set to 0.4. For more details visit:
https://umap-learn.readthedocs.io/en/latest/parameter
s.html [default: 0.4]
--p-random-state INTEGER
Seed used by random number generator. [optional]
Outputs:
--o-umap ARTIFACT The resulting UMAP matrix.
PCoAResults [required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr
during execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this action. If not provided, the default
cache under $TMP/qiime2/ will be used.
IMPORTANT FOR HPC USERS: If you are on an HPC system
and are using parallel execution it is important to
set this to a location that is globally accessible to
all nodes in the cluster.
--help Show this message and exit.
Import:
from qiime2.plugins.diversity.methods import umap
Docstring:
Uniform Manifold Approximation and Projection
Apply Uniform Manifold Approximation and Projection.
Parameters
----------
distance_matrix : DistanceMatrix
The distance matrix on which UMAP should be computed.
number_of_dimensions : Int % Range(2, None), optional
Dimensions to reduce the distance matrix to.
n_neighbors : Int % Range(1, None), optional
Provide the balance between local and global structure. Low values
prioritize the preservation of local structures. Large values sacrifice
local details for a broader global embedding.
min_dist : Float % Range(0, None), optional
Controls the cluster size. Low values cause clumpier clusters. Higher
values preserve a broad topological structure. To get less overlapping
data points the default value is set to 0.4. For more details visit:
https://umap-learn.readthedocs.io/en/latest/parameters.html
random_state : Int, optional
Seed used by random number generator.
Returns
-------
umap : PCoAResults
The resulting UMAP matrix.