Mercurial > repos > goeckslab > squidpy
changeset 0:be0e7952e229 draft
planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/squidpy commit ee16860018eba110ff845d62b18396db22abd91e
| author | goeckslab |
|---|---|
| date | Mon, 29 Aug 2022 23:20:54 +0000 |
| parents | |
| children | bad34051dc8a |
| files | main_macros.xml squidpy_spatial.py squidpy_spatial.xml test-data/imc.h5ad test-data/imc_centrality_scores.png test-data/imc_co_occurrence.png test-data/imc_interaction_matrix.png test-data/imc_kmeans.h5ad test-data/imc_nhood_enrichment.png test-data/imc_ripley.png test-data/imc_sn.h5ad |
| diffstat | 11 files changed, 520 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main_macros.xml Mon Aug 29 23:20:54 2022 +0000 @@ -0,0 +1,64 @@ +<macros> + <token name="@TOOL_VERSION@">1.1.2</token> + <token name="@PROFILE@">20.01</token> + <token name="@VERSION_SUFFIX@">0</token> + + <xml name="macro_stdio"> + <stdio> + <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error" /> + </stdio> + </xml> + + <xml name="squidpy_requirements"> + <requirements> + <container type="docker">quay.io/goeckslab/squidpy:@TOOL_VERSION@</container> + </requirements> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">10.1038/s41592-021-01358-2</citation> + </citations> + </xml> + + <xml name="squidpy_spatial_options"> + <section name="options" title="Advanced Graph Options" expanded="false"> + <yield/> + <!-- <param argument="copy" type="hidden" value="false" help="If True, return the result, otherwise save it to the adata object." /> --> + </section> + </xml> + + <xml name="squidpy_plotting_options"> + <section name="plotting_options" title="Plotting Options" expanded="false"> + <yield /> + <!-- <param argument="legend_kwargs" type="text" value="" optional="true" label="Keyword arguments for matplotlib.pyplot.legend()" /> --> + <param argument="figsize" type="text" value="" optional="true" label="Size of the figure in inches" help="Optional. e.g.: (12, 12)." /> + <param argument="dpi" type="integer" value="" optional="true" label="Dots per inch" help="Optional" /> + </section> + </xml> + + <xml name="squidpy_plotting_option_palette"> + <param argument="palette" type="text" value="" optional="true" label="Categorical colormap for the clusters" help="Comma delimited for multiple. If None, use anndata.AnnData.uns ['{cluster_key}_colors'], if available." /> + </xml> + + <xml name="squidpy_plotting_options_more"> + <expand macro="squidpy_plotting_options"> + <yield /> + <param argument="annotate" type="boolean" checked="false" label="Whether to annotate the cells of the heatmap?" /> + <param argument="method" type="select" label="The linkage method to be used for dendrogram/clustering" help="see scipy.cluster.hierarchy.linkage()."> + <option value="none" selected="true">None</option> + <option value="single">single</option> + <option value="complete">complete</option> + <option value="average">average</option> + <option value="weighted">weighted</option> + <option value="centroid">centroid</option> + <option value="median">median</option> + <option value="ward">ward</option> + </param> + <param argument="title" type="text" value="" optional="true" label="The title of the plot" help="Optional." /> + <param argument="cmap" type="text" value="viridis" label="Continuous colormap to use" help="Refer to `matplotlib.pyplot.cmap`." /> + <!-- <param argument="cbar_kwargs" type="text" value="" label="Keyword arguments for matplotlib.figure.Figure.colorbar()" /> --> + <expand macro="squidpy_plotting_option_palette" /> + </expand> + </xml> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/squidpy_spatial.py Mon Aug 29 23:20:54 2022 +0000 @@ -0,0 +1,110 @@ +import argparse +import ast +import json +import warnings + +import pandas as pd +import squidpy as sq +from anndata import read_h5ad + + +def main(inputs, anndata, output, output_plot): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter. + anndata : str + File path to anndata containing phenotyping info. + output : str + File path to output. + output_plot: str or None + File path to save the plotting image. + """ + warnings.simplefilter('ignore') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + adata = read_h5ad(anndata) + + if 'spatial' not in adata.obsm: + try: + adata.obsm['spatial'] = adata.obs[['X_centroid', 'Y_centroid']].values + except Exception as e: + print(e) + + tool = params['analyses']['selected_tool'] + tool_func = getattr(sq.gr, tool) + + options = params['analyses']['options'] + + for k, v in options.items(): + if not isinstance(v, str): + continue + + if v in ('', 'none'): + options[k] = None + continue + + if k == 'genes': # for spatial_autocorr and sepal + options[k] = [e.strip() for e in v.split(',')] + elif k == 'radius': # for spatial_neighbors + options[k] = ast.literal_eval(v) + elif k == 'numba_parallel': # for nhood_enrichment and ligrec + if v == 'false': + options[k] = False + elif v == 'true': + options[k] = True + elif k == 'interactions': # for ligrec + options[k] = pd.read_csv(v, sep="\t") + elif k == 'max_neighs': + options[k] = int(v) # for sepal + + cluster_key = params['analyses'].get('cluster_key') + if cluster_key: + tool_func(adata, cluster_key, **options) + else: + tool_func(adata, **options) + + if output_plot: + plotting_options = params['analyses']['plotting_options'] + for k, v in plotting_options.items(): + if not isinstance(v, str): + continue + + if v in ('', 'none'): + plotting_options[k] = None + continue + + if k == 'figsize': + options[k] = ast.literal_eval(v) + elif k in ('palette', 'score', 'source_groups', 'target_groups'): + options[k] = [e.strip() for e in v.split(',')] + elif k == 'means_range': # ligrec + v = v.strip() + if v[0] == '(': + v = v[1:] + if v[-1] == ')': + v = v[:-1] + options[k] = tuple([float(e.strip()) for e in v.split(',', 1)]) + + plotting_func = getattr(sq.pl, tool) + if cluster_key: + plotting_func(adata, cluster_key, save=output_plot, **plotting_options) + else: # TODO Remove this, since all plottings need cluster key + plotting_func(adata, save=output_plot, **plotting_options) + + adata.write(output) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-e", "--output", dest="output", required=True) + aparser.add_argument("-a", "--anndata", dest="anndata", required=True) + aparser.add_argument("-p", "--output_plot", dest="output_plot", required=False) + + args = aparser.parse_args() + + main(args.inputs, args.anndata, args.output, args.output_plot)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/squidpy_spatial.xml Mon Aug 29 23:20:54 2022 +0000 @@ -0,0 +1,346 @@ +<tool id="squidpy_spatial" name="Squidpy Graph and Plotting" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@"> + <description>for spatial analysis </description> + <macros> + <import>main_macros.xml</import> + </macros> + <expand macro="squidpy_requirements"/> + <expand macro="macro_stdio" /> + <version_command>echo "@TOOL_VERSION@"</version_command> + <command> + <![CDATA[ + python '$__tool_directory__/squidpy_spatial.py' + --inputs '$inputs' + --anndata '$anndata' + --output '$output' + #if $analyses.selected_tool in ['nhood_enrichment', 'centrality_scores', 'interaction_matrix', 'ligrec', 'ripley', 'co_occurrence']: + --output_plot "`pwd`/image.png" + #end if + + ]]> + </command> + <configfiles> + <inputs name="inputs" data_style="paths"/> + </configfiles> + <inputs> + <param name="anndata" type="data" format="h5ad" label="Select the input anndata" /> + <conditional name="analyses"> + <param name="selected_tool" type="select" label="Select an analysis"> + <option value="spatial_neighbors" selected="true">Spatial neighbors -- Create a graph from spatial coordinates</option> + <option value="nhood_enrichment">nhood_enrichment -- Compute neighborhood enrichment by permutation test.</option> + <option value="co_occurrence" >co_occurrence -- Compute co-occurrence probability of clusters</option> + <option value="centrality_scores">centrality_scores -- Compute centrality scores per cluster or cell type</option> + <option value="interaction_matrix">interaction_matrix -- Compute interaction matrix for clusters</option> + <option value="ripley">ripley -- Calculate various Ripley’s statistics for point processes</option> + <option value="ligrec">ligrec -- Perform the permutation test as described in [Efremova et al., 2020]</option> + <option value="spatial_autocorr">spatial_autocorr -- Calculate Global Autocorrelation Statistic (Moran’s I or Geary’s C)</option> + <option value="sepal">sepal -- Identify spatially variable genes with Sepal</option> + </param> + <when value="spatial_neighbors"> + <expand macro="squidpy_spatial_options"> + <param argument="spatial_key" type="text" value="spatial" optional="false" label="The Key where spatial coordinates are stored" help="Key in `anndata.AnnData.obsm`." /> + <param argument="coord_type" type="select" label="Type of coordinate system"> + <option value="grid" selected="true">grid</option> + <option value="generic">generic</option> + <option value="none">None - use `grid` when `spatial_key` is in `anndata.AnnData.uns with `n_neighs` = 6 (Visium), otherwise `generic`</option> + </param> + <param argument="n_neighs" type="integer" value="6" label="Number of neighboring tiles" help="When the `coord_type` is generic, this's number of neighborhoods for non-grid data and only used when `delaunay` is False." /> + <param argument="radius" type="text" value="" optional="true" label="Radius" help="Only available when coord_type = 'generic'. If float, this is the neighborhood radius to compute the graph; if tuple, this is edge range [min(radius), max(radius)] used prune the final graph." /> + <param argument="delaunay" type="boolean" checked="false" optional="true" label="Whether to compute the graph from Delaunay triangulation" help="Only used when coord_type = 'generic'." /> + <param argument="n_rings" type="integer" value="1" label="Number of rings of neighbors for grid data" help="Only used when coord_type = 'grid'." /> + <param argument="transform" type="select" label="Type of adjacency matrix transform"> + <option value="none" selected="true">None</option> + <option value="spectral">spectral</option> + <option value="cosine">cosine</option> + </param> + <param argument="set_diag" type="boolean" checked="false" label="Whether to set the diagonal of the spatial connectivities to 1.0" /> + <param argument="key_added" type="text" value="spatial" label="The column name used in anndata to store the returned data" /> + </expand> + </when> + <when value="nhood_enrichment"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="connectivity_key" type="text" value="" optional="true" label="Key in anndata.AnnData.obsp where spatial connectivities are stored" /> + <param argument="n_perms" type="integer" value="1000" label="Number of permutations for the permutation test" /> + <param argument="numba_parallel" type="select" label="Whether to use numba.prange or not"> + <option value="false" selected="true">No -- Recommended for small datasets or small number of interactions</option> + <option value="true">Yes</option> + <option value="none">Auto</option> + </param> + <param argument="seed" type="integer" value="" optional="true" label="Randomness seed" /> + </expand> + <expand macro="squidpy_plotting_options_more"> + <param argument="mode" type="select" label="Choose one result from gr.nhood_enrichment to plot"> + <option value="zscore" selected="true">zscore</option> + <option value="count">count</option> + </param> + </expand> + </when> + <when value="co_occurrence"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="spatial_key" type="text" value="spatial" optional="false" label="The Key where spatial coordinates are stored" help="Key in `anndata.AnnData.obsm`." /> + <param argument="n_steps" type="integer" value="50" label="Number of distance thresholds at which co-occurrence is computed" /> + <param argument="n_splits" type="integer" value="" optional="true" label="Number of splits in which to divide the spatial coordinates" help="In anndata.AnnData.obsm ['{spatial_key}']." /> + </expand> + <expand macro="squidpy_plotting_options"> + <expand macro="squidpy_plotting_option_palette" /> + </expand> + </when> + <when value="centrality_scores"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="score" type="select" label="Centrality measures as described in networkx.algorithms.centrality" help="Refer to [Hagberg et al., 2008]." > + <option value="none" selected="true">None</option> + <option value="closeness_centrality">closeness_centrality -- measure of how close the group is to other nodes</option> + <option value="average_clustering">average_clustering -- measure of the degree to which nodes cluster together</option> + <option value="degree_centrality">degree_centrality -- fraction of non-group members connected to group members</option> + </param> + <param argument="connectivity_key" type="text" value="" optional="true" label="Key in anndata.AnnData.obsp where spatial connectivities are stored" /> + </expand> + <expand macro="squidpy_plotting_options"> + <param argument="score" type="text" value="" optional="true" label="The scores to plot" help="Comma delimited for multiple. If None, plot all scores." /> + <expand macro="squidpy_plotting_option_palette" /> + </expand> + </when> + <when value="interaction_matrix"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="connectivity_key" type="text" value="" optional="true" label="Key in anndata.AnnData.obsp where spatial connectivities are stored" /> + <param argument="normalized" type="boolean" checked="false" label="Whether to normalize the sum of each row to 1" /> + <param argument="weights" type="boolean" checked="false" label="Whether to use edge weights or binarize" /> + </expand> + <expand macro="squidpy_plotting_options_more"/> + </when> + <when value="ripley"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="mode" type="select" label="Which Ripley’s statistic to compute"> + <option value="F" selected="true">F</option> + <option value="G">G</option> + <option value="L">L</option> + </param> + <param argument="spatial_key" type="text" value="spatial" optional="false" label="The Key where spatial coordinates are stored" help="Key in `anndata.AnnData.obsm`." /> + <param argument="metric" type="select" label="Which metric to use for computing distances" help="Refer to `sklearn.neighbors.DistanceMetric`." > + <option value="euclidean" selected="true">euclidean</option> + <option value="manhattan">manhattan</option> + <option value="chebyshev">chebyshev</option> + <option value="minkowski">minkowski</option> + <option value="wminkowski">wminkowski</option> + <option value="seuclidean">seuclidean</option> + <option value="mahalanobis">mahalanobis</option> + <option value="haversine">haversine</option> + <option value="hamming">hamming</option> + <option value="canberra">canberra</option> + <option value="braycurtis">braycurtis</option> + </param> + <param argument="n_neigh" type="integer" value="2" label="Number of neighbors to consider for the KNN graph" /> + <param argument="n_simulations" type="integer" value="100" label="Number of simulations to run for computing p-values" /> + <param argument="n_observations" type="integer" value="1000" label="Number of observations to generate for the Spatial Poisson Point Process" /> + <param argument="max_dist" type="float" value="" optional="true" label="Maximum distances for the support" help="If None, max_dist is the square root of area/2."/> + <param argument="n_steps" type="integer" value="50" label="Number of steps for the support" /> + <param argument="seed" type="integer" value="" optional="true" label="Randomness seed" /> + </expand> + <expand macro="squidpy_plotting_options"> + <param argument="mode" type="select" label="Ripley’s statistics to be plotted"> + <option value="F" selected="true">F</option> + <option value="G">G</option> + <option value="L">L</option> + </param> + <param argument="plot_sims" type="boolean" checked="true" label="Whether to overlay simulations in the plot" /> + <expand macro="squidpy_plotting_option_palette" /> + </expand> + </when> + <when value="ligrec"> + <param argument="cluster_key" type="text" value="" optional="false" label="Key in anndata.AnnData.obs where clustering is stored" /> + <expand macro="squidpy_spatial_options"> + <param argument="use_raw" type="boolean" checked="true" label="Whether to access anndata.AnnData.raw" /> + <param argument="interactions" type="data" format="tabular" optional="true" label="Select the dataset containing interaction to test" help="Must contain at least 2 columns named ‘source’ and ‘target’. Can be null, a condition under which the interactions are extracted from omnipath." /> + <param argument="complex_policy" type="select" label="Policy on how to handle complexes"> + <option value="min" selected="true">min -- select gene with the minimum average expression</option> + <option value="all">all -- select all possible combinations between ‘source’ and ‘target’ complexes</option> + </param> + <param argument="n_perms" type="integer" value="" optional="true" label="Number of permutations for the permutation test." /> + <param argument="threshold" type="float" value="0.01" label="Do not perform permutation test if any of the interacting components is being expressed in less than threshold percent of cells within a given cluster" /> + <param argument="corr_method" type="select" label="Correction method for multiple testing"> + <option value="none" selected="true">None</option> + <option value="bonferroni">bonferroni</option> + <option value="sidak">sidak</option> + <option value="holm-sidak">holm-sidak</option> + <option value="holm">holm</option> + <option value="simes-hochberg">simes-hochberg</option> + <option value="hommel">hommel</option> + <option value="fdr_bh">fdr_bh</option> + <option value="fdr_by">fdr_by</option> + <option value="fdr_tsbh">fdr_tsbh</option> + <option value="fdr_tsbky">fdr_tsbky</option> + </param> + <param argument="corr_axis" type="select" label="Axis over which to perform the FDR correction" help="Only used when corr_method != None."> + <option value="clusters" selected="true">clusters -- correct clusters by performing FDR correction across the interactions</option> + <option value="interactions">interactions -- correct interactions by performing FDR correction across the clusters</option> + </param> + <param argument="numba_parallel" type="select" label="Whether to use numba.prange or not"> + <option value="false" selected="true">No -- Recommended for small datasets or small number of interactions</option> + <option value="true">Yes</option> + <option value="none">Auto</option> + </param> + <param argument="key_added" type="text" value="" optional="true" label="Key in anndata to store the result" help="In `anndata.AnnData.uns`. If None, '{cluster_key}_ligrec' will be used." /> + <param argument="gene_symbols" type="text" value="" optional="true" label="Key in anndata.AnnData.var to use instead of anndata.AnnData.var_names" help="Optional." /> + <param argument="seed" type="integer" value="" label="Randomness seed" /> + </expand> + <expand macro="squidpy_plotting_options"> + <param argument="source_groups" type="text" value="" optional="true" label="Source interaction clusters" help="Comma delimited. If None, select all clusters." /> + <param argument="target_groups" type="text" value="" optional="true" label="Target interaction clusters" help="Comma delimited. If None, select all clusters." /> + <param argument="means_range" type="text" value="(-inf, inf)" label="Only show interactions whose means are within this closed interval" help="Tuple of floats. e.g.: (-10, 10)." /> + <param argument="pvalue_threshold" type="float" value="1.0" label="Only show interactions with p-value less than this threshold" /> + <param argument="remove_empty_interactions" type="boolean" checked="true" label="Whether to remove rows and columns that only contain interactions with NaN values?" /> + <param argument="remove_nonsig_interactions" type="boolean" checked="false" label="Whether to remove rows and columns that only contain interactions that are larger than `alpha`? " /> + <param argument="dendrogram" type="select" label="How to cluster based on the p-values?"> + <option value="none" selected="true">None</option> + <option value="interacting_molecules">interacting_molecules</option> + <option value="interacting_clusters">interacting_clusters</option> + <option value="both">both</option> + </param> + <param argument="alpha" type="float" value="0.001" label="Significance threshold" help="All elements with p-values less than alpha will be marked by tori instead of dots." /> + <param argument="swap_axes" type="boolean" checked="false" label="Whether to show the cluster combinations as rows and the interacting pairs as columns" /> + </expand> + </when> + <when value="spatial_autocorr"> + <expand macro="squidpy_spatial_options"> + <param argument="connectivity_key" type="text" value="spatial_connectivities" label="Key in anndata.AnnData.obsp where spatial connectivities are stored" /> + <param argument="genes" type="text" value="" optional="true" label="List of gene names, as stored in anndata.AnnData.var_names" help="Comma delitimited. Used to compute global spatial autocorrelation statistic. If None, it’s computed anndata.AnnData.var ['highly_variable']." /> + <param argument="mode" type="select"> + <option value="moran" selected="true">moran</option> + <option value="geary">geary</option> + </param> + <param argument="transformation" type="boolean" checked="true" label="Whether to perform row-normalization on weights in anndata.AnnData.obsp ['spatial_connectivities']" /> + <param argument="n_perms" type="integer" value="" optional="true" label="Number of permutations for the permutation test." help="If None, only p-values under normality assumption are computed"/> + <param argument="two_tailed" type="boolean" checked="false" label="Are the p-values two-tailed?" help="One-tailed, if False." /> + <param argument="corr_method" type="select" label="Correction method for multiple testing"> + <option value="none" selected="true">None</option> + <option value="bonferroni">bonferroni</option> + <option value="sidak">sidak</option> + <option value="holm-sidak">holm-sidak</option> + <option value="holm">holm</option> + <option value="simes-hochberg">simes-hochberg</option> + <option value="hommel">hommel</option> + <option value="fdr_bh">fdr_bh</option> + <option value="fdr_by">fdr_by</option> + <option value="fdr_tsbh">fdr_tsbh</option> + <option value="fdr_tsbky">fdr_tsbky</option> + </param> + <param argument="layer" type="text" value="" optional="true" label="Layer in anndata.AnnData.layers to use" help="If None, use anndata.AnnData.X." /> + <param argument="seed" type="integer" value="" optional="true" label="Randomness seed" /> + </expand> + </when> + <when value="sepal"> + <expand macro="squidpy_spatial_options"> + <param argument="max_neighs" type="select" label="Maximum number of neighbors of a node in the graph"> + <option value="4" selected="true">4 -- for a square-grid (ST, Dbit-seq)</option> + <option value="6">6 -- for a hexagonal-grid (Visium)</option> + </param> + <param argument="genes" type="text" value="" optional="true" label="List of gene names, as stored in anndata.AnnData.var_names" help="Comma delitimited. Used to compute global spatial autocorrelation statistic. If None, it’s computed anndata.AnnData.var ['highly_variable']." /> + <param argument="n_iter" type="integer" value="30000" optional="true" label="Maximum number of iterations for the diffusion simulation" /> + <param argument="dt" type="float" value="0.001" label="Time step in diffusion simulation" /> + <param argument="thresh" type="float" value="1e-8" label="Entropy threshold for convergence of diffusion simulation" /> + <param argument="spatial_key" type="text" value="spatial" optional="false" label="The Key where spatial coordinates are stored" help="Key in `anndata.AnnData.obsm`." /> + <param argument="connectivity_key" type="text" value="spatial_connectivities" label="Key in anndata.AnnData.obsp where spatial connectivities are stored" /> + <param argument="layer" type="text" value="" optional="true" label="Layer in anndata.AnnData.layers to use" help="If None, use anndata.AnnData.X." /> + <param argument="use_raw" type="boolean" checked="true" label="Whether to access anndata.AnnData.raw" /> + </expand> + </when> + </conditional> + </inputs> + <outputs> + <data format="h5ad" name="output" label="Squidpy.gr.${analyses.selected_tool} on ${on_string}" /> + <data from_work_dir="image.png" format="png" name="output_plot" label="Squidpy.pl.${analyses.selected_tool} on ${on_string}" > + <filter>analyses['selected_tool'] in ['nhood_enrichment', 'centrality_scores', 'interaction_matrix', 'ligrec', 'ripley', 'co_occurrence']</filter> + </data> + </outputs> + <tests> + <test> + <param name="anndata" value="imc.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="spatial_neighbors" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/spatial_neighbors" /> + </assert_contents> + </output> + </test> + <test> + <param name="anndata" value="imc_sn.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="nhood_enrichment" /> + <param name="cluster_key" value="cell type" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/cell type_nhood_enrichment" /> + </assert_contents> + </output> + <output name="output_plot" file="imc_nhood_enrichment.png" compare="sim_size" delta="2000" /> + </test> + <test> + <param name="anndata" value="imc_sn.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="co_occurrence" /> + <param name="cluster_key" value="cell type" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/cell type_co_occurrence" /> + </assert_contents> + </output> + <output name="output_plot" file="imc_co_occurrence.png" compare="sim_size" delta="2000" /> + </test> + <test> + <param name="anndata" value="imc_sn.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="centrality_scores" /> + <param name="cluster_key" value="cell type" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/cell type_centrality_scores" /> + </assert_contents> + </output> + <output name="output_plot" file="imc_centrality_scores.png" compare="sim_size" delta="2000" /> + </test> + <test> + <param name="anndata" value="imc_sn.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="interaction_matrix" /> + <param name="cluster_key" value="cell type" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/cell type_interactions" /> + </assert_contents> + </output> + <output name="output_plot" file="imc_interaction_matrix.png" compare="sim_size" delta="2000" /> + </test> + <test> + <param name="anndata" value="imc_sn.h5ad" ftype="h5ad" /> + <param name="selected_tool" value="ripley" /> + <param name="cluster_key" value="cell type" /> + <output name="output"> + <assert_contents> + <has_h5_keys keys="uns/cell type_ripley_F" /> + </assert_contents> + </output> + <output name="output_plot" file="imc_ripley.png" compare="sim_size" delta="2000" /> + </test> + </tests> + <help> + <![CDATA[ +**What it does** + +This tool includes various of single cell spatial analysis utils provided by Squidpy. + +**Input** + +*AnnData* + +**Output** + +*Anndata* + +*Plotting (PNG) if applicable* + + + ]]> + </help> + <citations> + </citations> +</tool>