Viash Hub

Description

Compute a nearest-neighbor graph of observations on the GPU.

Wraps rapids-singlecell's rsc.pp.neighbors, whose neighbor search relies on
cuVS for fast (approximate) KNN search. The resulting graph is the basis for
downstream embedding and clustering (e.g. UMAP, Leiden). Connectivities can be
estimated with the UMAP fuzzy simplicial set ('umap'), an adaptive Gaussian
kernel ('gauss'), or the PhenoGraph Jaccard index ('jaccard').

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	Which .obsm slot to use as a starting embedding (use_rep).

Name

Type & Properties

--input

-i

file

required

--modality

string

--obsm_input

string

Outputs

Name	Type & Properties	Description
--output -o	file required output	Output h5mu file containing the found neighbors.
--uns_output	string	In which .uns slot to store the neighbor graph metadata.
--obsp_distances	string	In which .obsp slot to store the distance matrix between the resulting neighbors.
--obsp_connectivities	string	In which .obsp slot to store the connectivities matrix between the resulting neighbors.
--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

--uns_output

string

--obsp_distances

string

--obsp_connectivities

string

--output_compression

string

Options

Name	Type & Properties	Description
--num_neighbors	integer	The size of the local neighborhood (in terms of number of neighboring data points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100.
--n_pcs	integer	Number of dimensions of the `--obsm_input` representation to use. If not set, all available dimensions are used.
--metric	string	The distance metric to use.
--algorithm	string	The KNN query algorithm to use (provided by cuVS). See https://docs.rapids.ai/api/cuvs/stable/ for details. - brute: Brute-force exact search computing all pairwise distances. Exact but the most expensive; best for smaller datasets. - cagra: GPU graph-based approximate search optimized for high query throughput on large datasets. - ivfflat: Inverted-file index that clusters vectors into lists and only searches the nearest lists. Approximate, faster than brute. - ivfpq: Inverted-file index combined with product quantization of the vectors. Approximate with a low memory footprint; scales to very large datasets. - nn_descent: Graph-based approximate method that iteratively refines a KNN graph from a random initialization.
--method	string	Method for computing connectivities. 'umap' uses the UMAP fuzzy simplicial set, 'gauss' uses an adaptive Gaussian kernel, 'jaccard' uses the PhenoGraph Jaccard index.
--random_state	integer	A random seed.

Name

Type & Properties

--num_neighbors

integer

--n_pcs

integer

--metric

string

--algorithm

string

--method

string

--random_state

integer

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"  
uns_output: [ "neighbors" ]  
obsp_distances: [ "distances" ]  
obsp_connectivities: [ "connectivities" ]  
num_neighbors: [ 15 ]  
metric: [ "euclidean" ]  
algorithm: [ "brute" ]  
method: [ "umap" ]  
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/preprocessing/neighbors/main.nf \  
  -params-file params.yaml

Relationships

Used by

2 relationships

workflows/multiomics/neighbors_leiden_umapopenpipeline_rapids v0.1.3

wrappers/preprocessing/neighborsopenpipeline_rapids v0.1.3

Current component

preprocessing/neighborsopenpipeline_rapids v0.1.3

Uses

0 relationships

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