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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit aba2a85f5da6e1094f382d1f0d94c4b8f2544a7d |
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modified:
cluster_reduce_dimension.xml macros.xml test-data/blobs.h5ad test-data/krumsiek11.h5ad test-data/pbmc68k_reduced.h5ad test-data/pl.clustermap.krumsiek11.png test-data/pl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.dotplot.krumsiek11.png test-data/pl.dpt_timeseries.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.draw_graph.png test-data/pl.heatmap.krumsiek11.png test-data/pl.highest_expr_genes.filter_genes_dispersion.krumsiek11-seurat.png test-data/pl.highly_variable_genes.seurat.blobs.png test-data/pl.matrixplot.krumsiek11.png test-data/pl.paga.paul15_gauss_braycurtis.png test-data/pl.pca.pbmc68k_reduced.CD3D_CD79A_components_2d.pdf test-data/pl.pca_loadings.pp.pca.krumsiek11.png test-data/pl.pca_overview.pp.pca.krumsiek11.png test-data/pl.pca_variance_ratio.pp.pca.krumsiek11.png test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_dotplot.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_heatmap.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_matrixplot.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_stacked_violin.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_violin.Ery.png test-data/pl.rank_genes_groups_violin.Mk.png test-data/pl.rank_genes_groups_violin.Mo.png test-data/pl.rank_genes_groups_violin.Neu.png test-data/pl.rank_genes_groups_violin.progenitor.png test-data/pl.scatter.krumsiek11.png test-data/pl.scatter.umap.pbmc68k_reduced.png test-data/pl.stacked_violin.krumsiek11.png test-data/pl.tsne.krumsiek11.png test-data/pl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.png test-data/pl.violin.pbmc68k_reduced_custom.png test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad test-data/pp.combat.blobs.h5ad test-data/pp.downsample_counts.random-randint.h5ad test-data/pp.filter_cells.krumsiek11-max_genes.h5ad test-data/pp.filter_cells.krumsiek11-min_counts.h5ad test-data/pp.filter_genes.krumsiek11-min_counts.h5ad test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad test-data/pp.highly_variable_genes.seurat.blobs.h5ad test-data/pp.log1p.krumsiek11.h5ad test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.normalize_total.krumsiek11.h5ad test-data/pp.pca.krumsiek11.h5ad test-data/pp.pca.krumsiek11_chunk.h5ad test-data/pp.recipe_seurat.recipe_zheng17.h5ad test-data/pp.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad test-data/pp.recipe_zheng17.random-randint.h5ad test-data/pp.regress_out.krumsiek11.h5ad test-data/pp.scale.krumsiek11.h5ad test-data/pp.scale_max_value.krumsiek11.h5ad test-data/pp.sqrt.krumsiek11.h5ad test-data/pp.subsample.krumsiek11_fraction.h5ad test-data/pp.subsample.krumsiek11_n_obs.h5ad test-data/sparce_csr_matrix.h5ad test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.pca.krumsiek11.h5ad test-data/tl.rank_genes_groups.krumsiek11.h5ad test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad test-data/tl.score_genes.krumsiek11.h5ad test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad test-data/tl.tsne.krumsiek11.h5ad test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad |
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test-data/pl.dpt_groups_pseudotime.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.embedding_density.pbmc68k_reduced.png test-data/pl.paga_compare.paul15_gauss_braycurtis.png test-data/pl.scatter.pbmc68k_reduced.png test-data/tl.embedding_density.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.embedding_density.umap.pbmc68k_reduced.h5ad test-data/tl.paga.neighbors.paul15_gauss_braycurtis.h5ad |
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| diff -r aaa5da8e73a9 -r edec35114f72 cluster_reduce_dimension.xml --- a/cluster_reduce_dimension.xml Wed Sep 22 21:09:55 2021 +0000 +++ b/cluster_reduce_dimension.xml Wed Nov 08 14:46:29 2023 +0000 |
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| b'@@ -1,6 +1,5 @@\n <tool id="scanpy_cluster_reduce_dimension" name="Cluster, infer trajectories and embed" version="@galaxy_version@" profile="@profile@">\n <description>with scanpy</description>\n- <expand macro="bio_tools"/>\n <macros>\n <import>macros.xml</import>\n <xml name="pca_inputs">\n@@ -20,7 +19,7 @@\n <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true"\n label="Compute standard PCA from covariance matrix?"\n help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/>\n- <expand macro="svd_solver"/> \n+ <expand macro="svd_solver"/>\n <param argument="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/>\n </when>\n </conditional>\n@@ -61,8 +60,8 @@\n use_highly_variable=$method.use_highly_variable\n ]]></token>\n </macros>\n+ <expand macro="bio_tools"/>\n <expand macro="requirements">\n- <requirement type="package" version="0.7.0">louvain</requirement>\n </expand>\n <expand macro="version_command"/>\n <command detect_errors="exit_code"><![CDATA[\n@@ -179,6 +178,19 @@\n min_group_size=$method.min_group_size,\n allow_kendall_tau_shift=$method.allow_kendall_tau_shift,\n copy=False)\n+\n+#else if $method.method == "tl.embedding_density"\n+sc.tl.embedding_density(\n+ adata=adata,\n+ basis=\'$method.basis\',\n+#if str($method.groupby) != \'\'\n+ groupby=\'$method.groupby\',\n+#end if\n+#if str($method.key_added) != \'\'\n+ key_added=\'$method.key_added\',\n+#end if\n+ )\n+\n #end if\n \n @CMD_anndata_write_outputs@\n@@ -198,6 +210,7 @@\n <option value="tl.draw_graph">Force-directed graph drawing, using \'tl.draw_graph\'</option>\n <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using \'tl.dpt\'</option>\n <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using \'tl.paga\'</option>\n+ <option value="tl.embedding_density">Calculate the density of cells in an embedding (per condition)</option>\n </param>\n <when value="tl.louvain">\n <conditional name="flavor">\n@@ -288,15 +301,26 @@\n <option value="v1.0">v1.0</option>\n </param>\n </when>\n- </conditional> \n+ <when value="tl.embedding_density">\n+ <param argument="basis" type="text" value="umap" label="The embedding over which the density will be calculated." help="This embedded representation should be found in adata.obsm[\'X_[basis]\']">\n+ <expand macro="sanitize_query" />\n+ </param>\n+ <param argument="groupby" type="text" optional="true" value="" label="Key for categorical observation/cell annotation for which densities are calculated per category." >\n+ <expand macro="sanitize_query" />\n+ </param>\n+ <param argument="key_added" type="text" optional="true" value="" label="Name of the .obs covariate that will be added with the density estimates.">\n+ <expand macro="sanitize_query" />\n+ </param>\n+ </when>\n+ </conditional>\n <expand macro="inputs_common_advanced"/>\n </inputs>\n <outputs>\n <expand macro="anndata_outputs"/>\n </outputs>\n <tests>\n- <test>\n- <!-- test 0 -->\n+ <test expect_num_outputs="2">\n+ <!-- test 1 -->\n <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" />\n <conditional name="method">\n <param name="method" value="tl.louvain"/>\n@@ -326,8 +350,8 @@\n '..b't al, 2011). \n+we use the implementation of *scikit-learn* (Pedregosa et al, 2011).\n \n It returns `X_tsne`, tSNE coordinates of data.\n \n More details on the `tl.tsne scanpy documentation\n-<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.tsne.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.tsne.html>`__\n \n Embed the neighborhood graph using UMAP, using `tl.umap`\n ========================================================\n@@ -678,30 +725,30 @@\n (McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint\n <https://doi.org/10.1101/298430>`__.\n \n-The UMAP coordinates of data are added to the return AnnData in the multi-dimensional \n+The UMAP coordinates of data are added to the return AnnData in the multi-dimensional\n observations annotation (obsm). This data is accessible using the inspect tool for AnnData\n \n More details on the `tl.umap scanpy documentation\n-<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.umap.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.umap.html>`__\n \n Force-directed graph drawing, using `tl.draw_graph`\n ===================================================\n \n-Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. \n-It has been suggested for visualizing single-cell data by Islam et al, 11. \n-Many other layouts as implemented in igraph are available. Similar approaches have been used by \n+Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs.\n+It has been suggested for visualizing single-cell data by Islam et al, 11.\n+Many other layouts as implemented in igraph are available. Similar approaches have been used by\n Zunder et al, 2015 or Weinreb et al, 2016.\n \n-This is an alternative to tSNE that often preserves the topology of the data better. \n+This is an alternative to tSNE that often preserves the topology of the data better.\n This requires to run `pp.neighbors`, first.\n \n The default layout (ForceAtlas2) uses the package fa2.\n \n-The coordinates of graph layout are added to the return AnnData in the multi-dimensional \n+The coordinates of graph layout are added to the return AnnData in the multi-dimensional\n observations annotation (obsm). This data is accessible using the inspect tool for AnnData.\n \n More details on the `tl.draw_graph scanpy documentation\n-<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.draw_graph.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.draw_graph.html>`__\n \n Infer progression of cells through geodesic distance along the graph (`tl.dpt`)\n ===============================================================================\n@@ -714,7 +761,7 @@\n `n_branchings>1`. We recommend, however, to only use\n `tl.dpt` for computing pseudotime (`n_branchings=0`) and\n to detect branchings via `paga`. For pseudotime, you need\n-to annotate your data with a root cell. \n+to annotate your data with a root cell.\n \n This requires to run `pp.neighbors`, first. In order to\n reproduce the original implementation of DPT, use `method==\'gauss\'` in\n@@ -730,7 +777,7 @@\n The tool is similar to the R package `destiny` of Angerer et al (2016).\n \n More details on the `tl.dpt scanpy documentation\n-<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.dpt.html>`_\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_\n \n \n Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)\n@@ -761,7 +808,7 @@\n These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects\n \n More details on the `tl.paga scanpy documentation\n-<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.paga.html>`_\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_\n ]]></help>\n <expand macro="citations"/>\n </tool>\n' |
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| diff -r aaa5da8e73a9 -r edec35114f72 macros.xml --- a/macros.xml Wed Sep 22 21:09:55 2021 +0000 +++ b/macros.xml Wed Nov 08 14:46:29 2023 +0000 |
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| @@ -1,12 +1,16 @@ <macros> - <token name="@version@">1.7.1</token> - <token name="@profile@">19.01</token> + <token name="@version@">1.9.6</token> + <token name="@profile@">22.05</token> <token name="@galaxy_version@"><![CDATA[@version@+galaxy0]]></token> <xml name="requirements"> <requirements> <requirement type="package" version="@version@">scanpy</requirement> - <requirement type="package" version="2.0.17">loompy</requirement> - <requirement type="package" version="0.8.3">leidenalg</requirement> + <requirement type="package" version="3.0.6">loompy</requirement> + <requirement type="package" version="0.10.1">leidenalg</requirement> + <requirement type="package" version="0.8.1">louvain</requirement> + <requirement type="package" version="1.5.3">pandas</requirement> + <requirement type="package" version="3.7">matplotlib</requirement> + <requirement type="package" version="0.12.2">seaborn</requirement> <yield /> </requirements> </xml> @@ -15,9 +19,15 @@ <xref type="bio.tools">scanpy</xref> </xrefs> </xml> + <xml name="creators"> + <creator> + <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> + </creator> + </xml> <xml name="citations"> <citations> <citation type="doi">10.1186/s13059-017-1382-0</citation> + <citation type="doi">10.1093/gigascience/giaa102</citation> </citations> </xml> <xml name="version_command"> @@ -56,7 +66,7 @@ <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/> </xml> <token name="@CMD_read_inputs@"><![CDATA[ -adata = sc.read('anndata.h5ad') +adata = sc.read_h5ad('anndata.h5ad') ]]> </token> <xml name="inputs_common_advanced"> @@ -66,12 +76,12 @@ </xml> <xml name="anndata_outputs"> <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/> - <data name="hidden_output" format="txt" label="Log file" > + <data name="hidden_output" format="txt" label="Log file" > <filter>advanced_common['show_log']</filter> </data> </xml> <token name="@CMD_anndata_write_outputs@"><![CDATA[ -adata.write('anndata.h5ad') +adata.write_h5ad('anndata.h5ad') with open('anndata_info.txt','w', encoding='utf-8') as ainfo: print(adata, file=ainfo) ]]> @@ -875,7 +885,7 @@ </xml> <xml name="param_color_map"> <param argument="color_map" type="select" label="Color map to use for continous variables" help=""> - <option value="None">Default</option> + <option value="" >Default</option> <expand macro="matplotlib_pyplot_colormap"/> </param> </xml> @@ -979,7 +989,7 @@ @CMD_param_legend_fontsize@ legend_fontweight='$method.plot.legend_fontweight', @CMD_param_size@ - #if str($method.plot.color_map) != 'None' + #if str($method.plot.color_map) != '' color_map='$method.plot.color_map', #end if #if str($method.plot.palette) != '' |
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