feature_annotation/xenium_spatial_statistics

Description

Calculate various spatial statistics from Xenium data including cell morphology
ratios, position-based features, local density metrics, and global spatial patterns.
This component expects a MuData object with a pre-computed spatial neighborhood graph
in .obsp.

Type

python_script

Links

Repository

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
obsm_spatial_coordinates: [ "spatial" ]  
output: "$id.$key.output"  
output_prefix: [ "spatial_" ]  
density_bandwidth: [ 50 ]  
calculate_ripley_l: [ false ]  
n_subsample_ripley: [ -1 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline_spatial.git \  
  -revision v0.5.0 \  
  -main-script target/nextflow/feature_annotation/xenium_spatial_statistics/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input	file required	A MuData file containing spatial transcriptomics data and a pre-computed spatial neighborhood graph in `.obsp`.
--modality	string required	The name of the modality to use from the MuData object. This specifies which AnnData object contains the spatial data.
--obsm_spatial_coordinates	string	The key in `.obsm` where spatial coordinates are stored. Expected shape is (n_cells, 2) for 2D coordinates.

Name

Type & Properties

--input

file

required

--modality

string

required

--obsm_spatial_coordinates

string

Outputs

Name	Type & Properties	Description
--output	file output	The output MuData file with calculated spatial statistics. Per-cell metrics are stored in `.obs` and global statistics in `.uns`.
--output_prefix	string	Prefix to add to all generated column names in `.obs`.

Name

Type & Properties

--output

file

output

--output_prefix

string

Parameters

Name	Type & Properties	Description
--density_bandwidth	double	Bandwidth parameter for Gaussian kernel density estimation in spatial units (typically microns). Larger values create smoother density estimates.
--calculate_ripley_l	boolean	Whether to calculate Ripley's L statistic. Warning: This is an O(N^2) operation and can be very slow for large datasets (>5000 cells).
--n_subsample_ripley	integer	Number of cells to subsample for Ripley's L calculation. If -1, use all cells. Recommended to keep this below 5000 for performance.

Name

Type & Properties

--density_bandwidth

double

--calculate_ripley_l

boolean

--n_subsample_ripley

integer