Viash Hub

Description

t-SNE (t-distributed Stochastic Neighbor Embedding) is a non-linear
dimensionality reduction technique for visualizing high-dimensional data.
It computes a low-dimensional embedding from a precomputed representation
in .obsm (e.g. X_pca) and writes the result to .obsm. Wraps the
rapids_singlecell.tl.tsne function.

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file.
--modality	string	Which modality from the input MuData file to process.
--obsm_input	string	The `.obsm` slot to use as the representation to embed.
--n_pcs	integer	The number of dimensions of the representation to use. If not set, all dimensions of `--obsm_input` are used.

Name

Type & Properties

--input

-i

file

required

--modality

string

--obsm_input

string

--n_pcs

integer

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file.
--obsm_output	string	In which .obsm slot to store the resulting t-SNE embedding.
--overwrite	boolean	Allow overwriting the .obsm output slot if it already exists.
--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

--overwrite

boolean

--output_compression

string

Options

Name	Type & Properties	Description
--perplexity	integer	The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity.
--early_exaggeration	double	Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them.
--learning_rate	double	The learning rate for the optimization. If the learning rate is too high, the data may look like a 'ball' with any point approximately equidistant from its nearest neighbours. If it is too low, most points may look compressed in a dense cloud with few outliers.
--metric	string	The metric to use when computing distances in the representation.
--method	string	The t-SNE method to use.

Name

Type & Properties

--perplexity

integer

--early_exaggeration

double

--learning_rate

double

--metric

string

--method

string

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
obsm_input: [ "X_pca" ]  
output: "$id.$key.output"  
obsm_output: [ "X_tsne" ]  
overwrite: [ false ]  
perplexity: [ 30 ]  
early_exaggeration: [ 12 ]  
learning_rate: [ 200 ]  
metric: [ "euclidean" ]  
method: [ "barnes_hut" ]  
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/tsne/main.nf \  
  -params-file params.yaml

Relationships

Used by

1 relationships

wrappers/tools/tsneopenpipeline_rapids v0.1.3

Current component

tools/tsneopenpipeline_rapids v0.1.3

Uses

0 relationships

No component dependencies found.