dimred/densmap

Description

A modification of UMAP that adds an extra cost term in order to preserve information
about the relative local density of the data. It is performed on the same inputs as UMAP.

Type

python_script

License

MIT

Links

Repository

Contributors

Launch

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: [ "X_densmap" ]  
min_dist: [ 0.5 ]  
spread: [ 1 ]  
num_components: [ 2 ]  
max_iter: [ 0 ]  
alpha: [ 1 ]  
gamma: [ 1 ]  
negative_sample_rate: [ 5 ]  
init_pos: [ "spectral" ]  
lambda: [ 2 ]  
fraction: [ 0.3 ]  
var_shift: [ 0.1 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v3.0.0 \  
  -main-script target/nextflow/dimred/densmap/main.nf \  
  -params-file params.yaml

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.
--obsm_pca	string required	The slot in `.obsm` where the PCA results are stored.

Name

Type & Properties

--input

file

required

--modality

string

--uns_neighbors

string

--obsm_pca

string

required

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--obsm_output	string	The .obsm key to use for storing the densMAP 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 UMAP

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 samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy.
--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

Arguments densMAP

Name	Type & Properties	Description
--lambda	double	Controls the regularization weight of the density correlation term in densMAP. Higher values prioritize density preservation over the UMAP objective, and vice versa for values closer to zero. Setting this parameter to zero is equivalent to running the original UMAP algorithm.
--fraction	double	Controls the fraction of epochs (between 0 and 1) where the density-augmented objective is used in densMAP. The first (1 - dens_frac) fraction of epochs optimize the original UMAP objective before introducing the density correlation term.
--var_shift	double	A small constant added to the variance of local radii in the embedding when calculating the density correlation objective to prevent numerical instability from dividing by a small number.

Name

Type & Properties

--lambda

double

--fraction

double

--var_shift

double