neighbors/find_neighbors

Description

Compute a neighborhood graph of observations [McInnes18].

The neighbor search efficiency of this heavily relies on UMAP [McInnes18], which also provides a method for estimating connectivities of data points - the connectivity of the manifold (method=='umap'). If method=='gauss', connectivities are computed according to [Coifman05], in the adaption of [Haghverdi16].

Type

python_script

Contributors

Launch

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.h5mu"  
uns_output: [ "neighbors" ]  
obsp_distances: [ "distances" ]  
obsp_connectivities: [ "connectivities" ]  
metric: [ "euclidean" ]  
num_neighbors: [ 15 ]  
seed: [ 0 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision 1.0.3 \  
  -main-script target/nextflow/neighbors/find_neighbors/main.nf \  
  -params-file params.yaml

Arguments

Name	Type & Properties	Description
--input -i	file required	Input h5mu file
--modality	string
--obsm_input	string	Which .obsm slot to use as a starting PCA embedding.
--output -o	file output	Output h5mu file containing the found neighbors.
--output_compression	string	The compression format to be used on the output h5mu object.
--uns_output	string	Mandatory .uns slot to store various neighbor output objects.
--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.
--metric	string	The distance metric to be used in the generation of the nearest neighborhood network.
--num_neighbors	integer	The size of 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. If knn is True, number of nearest neighbors to be searched. If knn is False, a Gaussian kernel width is set to the distance of the n_neighbors neighbor.
--seed	integer	A random seed.

Name

Type & Properties

--input

-i

file

required

--modality

string

--obsm_input

string

--output

-o

file

output

--output_compression

string

--uns_output

string

--obsp_distances

string

--obsp_connectivities

string

--metric

string

--num_neighbors

integer

--seed

integer