Viash Hub

Description

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. Besides tending to be faster than tSNE, it optimizes the embedding such that it best reflects the topology of the data, which we represent throughout Scanpy using a neighborhood graph. tSNE, by contrast, optimizes the distribution of nearest-neighbor distances in the embedding such that these best match the distribution of distances in the high-dimensional space. We use the implementation of umap-learn [McInnes18]. For a few comparisons of UMAP with tSNE, see this preprint.

Inputs

Name	Type & Properties	Description
--input	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

file

required

--modality

string

--uns_neighbors

string

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--obsm_output	string	The pre/postfix under which to store the UMAP results.
--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

Arguments

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. Default is set to 500 if neighbors['connectivities'].shape[0] <= 10000, else 200.
--alpha	double	The initial learning rate for the embedding optimization.
--gamma	double	Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.
--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: * Any key from `.obsm` * `'paga'`: positions from `paga()` * `'spectral'`: use a spectral embedding of the graph * `'random'`: assign initial embedding positions at random.

Name

Type & Properties

--min_dist

double

--spread

double

--num_components

integer

--max_iter

integer

--alpha

double

--gamma

double

--negative_sample_rate

integer

--init_pos

string

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.h5mu"  
obsm_output: [ "umap" ]  
min_dist: [ 0.5 ]  
spread: [ 1 ]  
num_components: [ 2 ]  
alpha: [ 1 ]  
gamma: [ 1 ]  
negative_sample_rate: [ 5 ]  
init_pos: [ "spectral" ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.0.0 \  
  -main-script target/nextflow/dimred/umap/main.nf \  
  -params-file params.yaml

Relationships

Used by

2 relationships

workflows/integration/bbknn_leidenopenpipeline v4.0.0

workflows/multiomics/neighbors_leiden_umapopenpipeline v4.0.0

Current component

dimred/umapopenpipeline v4.0.0

Uses

0 relationships

No component dependencies found.