Viash Hub

Description

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
technique for visualizing high-dimensional data. It optimizes the
low-dimensional embedding so that it best reflects the topology of the data,
as represented by a precomputed neighborhood graph (e.g. from the
find_neighbors component). See McInnes et al. (2018), arXiv:1802.03426.

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file.
--modality	string	Which modality from the input MuData file to process.
--uns_neighbors	string	The `.uns` neighbors slot as output by the `find_neighbors` component.

Name

Type & Properties

--input

-i

file

required

--modality

string

--uns_neighbors

string

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--obsm_output	string	In which .obsm slot to store the resulting UMAP embedding.
--output_compression	string	Compression format to use for the output AnnData and/or Mudata objects. By default no compression is applied.

Name

Type & Properties

--output

-o

file

required

output

--obsm_output

string

--output_compression

string

Options

Name	Type & Properties	Description
--min_dist	double	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. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.
--spread	double	The effective scale of embedded points. In combination with `min_dist` this determines how clustered/clumped the embedded points are.
--num_components	integer	The number of dimensions of the embedding.
--max_iter	integer	The number of iterations (epochs) of the optimization. Called `n_epochs` in the original UMAP.
--alpha	double	The initial learning rate for the embedding optimization.
--negative_sample_rate	integer	The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding.
--init_pos	string	How to initialize the low dimensional embedding. Called `init` in the original UMAP. Options are: * `'auto'`: chooses `'spectral'` for n_samples < 1000000, `'random'` otherwise. * `'spectral'`: use a spectral embedding of the graph. * `'random'`: assign initial embedding positions at random. * `'paga'`: use the paga() layout as initial embedding positions. * Any key from `.obsm`.
--random_state	integer	Seed used by the random number generator.

Name

Type & Properties

--min_dist

double

--spread

double

--num_components

integer

--max_iter

integer

--alpha

double

--negative_sample_rate

integer

--init_pos

string

--random_state

integer

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
uns_neighbors: [ "neighbors" ]  
output: "$id.$key.output"  
obsm_output: [ "X_umap" ]  
min_dist: [ 0.5 ]  
spread: [ 1 ]  
num_components: [ 2 ]  
alpha: [ 1 ]  
negative_sample_rate: [ 5 ]  
init_pos: [ "spectral" ]  
random_state: [ 0 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline_rapids.git \  
  -revision v0.1.3 \  
  -main-script target/nextflow/tools/umap/main.nf \  
  -params-file params.yaml

Relationships

Used by

2 relationships

wrappers/tools/umapopenpipeline_rapids v0.1.3

workflows/multiomics/neighbors_leiden_umapopenpipeline_rapids v0.1.3

Current component

tools/umapopenpipeline_rapids v0.1.3

Uses

0 relationships

No component dependencies found.