Viash Hub

Description

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique used to visualize high-dimensional data in a low-dimensional space, revealing patterns and clusters by preserving local data similarities.

Inputs

Name	Type & Properties	Description
--input	file required	Input h5mu file
--modality	string required	Which modality from the input MuData file to process.
--use_rep	string required	The `.obsm` slot to use as input for the tSNE computation.

Name

Type & Properties

--input

file

required

--modality

string

required

--use_rep

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 tSNE results.
--output_compression	string	Compression format to use for the output AnnData and/or Mudata H5 files. 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
--n_pcs	integer	The number of principal components to use for the tSNE computation.
--perplexity	double	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. Consider selecting a value between 5 and 50. Different values can result in significantly different results.
--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.
--metric	string	Distance metric to calculate neighbors on.
--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. For larger values, the space between natural clusters will be larger in the embedded space. Again, the choice of this parameter is not very critical. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high.
--learning_rate	double	The learning rate for t-SNE optimization. Typical values range between 10.0 and 1000.0.
--random_state	integer	The random seed to use for the tSNE computation.

Name

Type & Properties

--n_pcs

integer

--perplexity

double

--min_dist

double

--metric

string

--early_exaggeration

double

--learning_rate

double

--random_state

integer

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
output: "$id.$key.output.h5mu"  
obsm_output: [ "X_tsne" ]  
n_pcs: [ 50 ]  
perplexity: [ 30 ]  
min_dist: [ 0.5 ]  
metric: [ "euclidean" ]  
early_exaggeration: [ 12 ]  
learning_rate: [ 1000 ]  
random_state: [ 0 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.1.0 \  
  -main-script target/nextflow/dimred/tsne/main.nf \  
  -params-file params.yaml

Relationships

Used by

1 relationships

wrappers/tools/tsneopenpipeline_rapids v0.1.3

Current component

dimred/tsneopenpipeline v4.1.0

Uses

0 relationships

No component dependencies found.