Mercurial > repos > iuc > scanpy_cluster_reduce_dimension
diff cluster_reduce_dimension.xml @ 11:edec35114f72 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit aba2a85f5da6e1094f382d1f0d94c4b8f2544a7d
| author | iuc |
|---|---|
| date | Wed, 08 Nov 2023 14:46:29 +0000 |
| parents | aaa5da8e73a9 |
| children | 6acb08931836 |
line wrap: on
line diff
--- 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 @@ -1,6 +1,5 @@ <tool id="scanpy_cluster_reduce_dimension" name="Cluster, infer trajectories and embed" version="@galaxy_version@" profile="@profile@"> <description>with scanpy</description> - <expand macro="bio_tools"/> <macros> <import>macros.xml</import> <xml name="pca_inputs"> @@ -20,7 +19,7 @@ <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Compute standard PCA from covariance matrix?" help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/> - <expand macro="svd_solver"/> + <expand macro="svd_solver"/> <param argument="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/> </when> </conditional> @@ -61,8 +60,8 @@ use_highly_variable=$method.use_highly_variable ]]></token> </macros> + <expand macro="bio_tools"/> <expand macro="requirements"> - <requirement type="package" version="0.7.0">louvain</requirement> </expand> <expand macro="version_command"/> <command detect_errors="exit_code"><![CDATA[ @@ -179,6 +178,19 @@ min_group_size=$method.min_group_size, allow_kendall_tau_shift=$method.allow_kendall_tau_shift, copy=False) + +#else if $method.method == "tl.embedding_density" +sc.tl.embedding_density( + adata=adata, + basis='$method.basis', +#if str($method.groupby) != '' + groupby='$method.groupby', +#end if +#if str($method.key_added) != '' + key_added='$method.key_added', +#end if + ) + #end if @CMD_anndata_write_outputs@ @@ -198,6 +210,7 @@ <option value="tl.draw_graph">Force-directed graph drawing, using 'tl.draw_graph'</option> <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using 'tl.dpt'</option> <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using 'tl.paga'</option> + <option value="tl.embedding_density">Calculate the density of cells in an embedding (per condition)</option> </param> <when value="tl.louvain"> <conditional name="flavor"> @@ -288,15 +301,26 @@ <option value="v1.0">v1.0</option> </param> </when> - </conditional> + <when value="tl.embedding_density"> + <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]']"> + <expand macro="sanitize_query" /> + </param> + <param argument="groupby" type="text" optional="true" value="" label="Key for categorical observation/cell annotation for which densities are calculated per category." > + <expand macro="sanitize_query" /> + </param> + <param argument="key_added" type="text" optional="true" value="" label="Name of the .obs covariate that will be added with the density estimates."> + <expand macro="sanitize_query" /> + </param> + </when> + </conditional> <expand macro="inputs_common_advanced"/> </inputs> <outputs> <expand macro="anndata_outputs"/> </outputs> <tests> - <test> - <!-- test 0 --> + <test expect_num_outputs="2"> + <!-- test 1 --> <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.louvain"/> @@ -326,8 +350,8 @@ </output> <output name="anndata_out" file="tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 1 --> + <test expect_num_outputs="2"> + <!-- test 2 --> <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.leiden"/> @@ -352,8 +376,8 @@ </output> <output name="anndata_out" file="tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 1 --> + <test expect_num_outputs="2"> + <!-- test 3 --> <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="pp.pca"/> @@ -384,8 +408,8 @@ </output> <output name="anndata_out" file="pp.pca.krumsiek11.h5ad" ftype="h5ad" compare="sim_size" delta="100000" delta_frac="0.15"/> </test> - <!--<test> - < test 3 > + <test expect_num_outputs="2"> + <!-- test 4 --> <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="pp.pca"/> @@ -397,21 +421,24 @@ </conditional> <param name="use_highly_variable" value="false"/> </conditional> - <assert_stdout> - <has_text_matching expression="sc.pp.pca"/> - <has_text_matching expression="data=adata"/> - <has_text_matching expression="n_comps=20"/> - <has_text_matching expression="dtype='float32'"/> - <has_text_matching expression="copy=False"/> - <has_text_matching expression="chunked=True"/> - <has_text_matching expression="chunk_size=50"/> - <has_text_matching expression="use_highly_variable=False"/> - </assert_stdout> + <section name="advanced_common"> + <param name="show_log" value="true" /> + </section> + <output name="hidden_output"> + <assert_contents> + <has_text_matching expression="sc.pp.pca"/> + <has_text_matching expression="data=adata"/> + <has_text_matching expression="dtype='float32'"/> + <has_text_matching expression="copy=False"/> + <has_text_matching expression="chunked=True"/> + <has_text_matching expression="chunk_size=50"/> + <has_text_matching expression="use_highly_variable=False"/> + </assert_contents> + </output> <output name="anndata_out" file="pp.pca.krumsiek11_chunk.h5ad" ftype="h5ad" compare="sim_size"/> </test> - --> - <test> - <!-- test 2 --> + <test expect_num_outputs="2"> + <!-- test 5 --> <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="tl.pca"/> @@ -441,8 +468,8 @@ </output> <output name="anndata_out" file="tl.pca.krumsiek11.h5ad" ftype="h5ad" compare="sim_size" delta="100000" delta_frac="0.15"/> </test> - <test> - <!-- test 3 --> + <test expect_num_outputs="2"> + <!-- test 6 --> <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.diffmap"/> @@ -458,8 +485,8 @@ </output> <output name="anndata_out" file="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 4 --> + <test expect_num_outputs="2"> + <!-- test 7 --> <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="tl.tsne"/> @@ -486,8 +513,8 @@ </output> <output name="anndata_out" file="tl.tsne.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 5 --> + <test expect_num_outputs="2"> + <!-- test 8 --> <param name="adata" value="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.umap"/> @@ -524,8 +551,8 @@ </assert_contents> </output> </test> - <test> - <!-- test 6 --> + <test expect_num_outputs="2"> + <!-- test 9 --> <param name="adata" value="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad"/> <conditional name="method"> <param name="method" value="tl.draw_graph"/> @@ -544,8 +571,8 @@ </output> <output name="anndata_out" file="tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 7 --> + <test expect_num_outputs="2"> + <!-- test 10 --> <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad"/> <conditional name="method"> <param name="method" value="tl.paga"/> @@ -566,8 +593,8 @@ </output> <output name="anndata_out" file="tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <!-- test 8 --> + <test expect_num_outputs="2"> + <!-- test 11 --> <param name="adata" value="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.dpt"/> @@ -590,6 +617,26 @@ </output> <output name="anndata_out" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> + <test expect_num_outputs="2"> + <!-- test 12 --> + <param name="adata" value="tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" /> + <conditional name="method"> + <param name="method" value="tl.embedding_density"/> + <param name="basis" value="umap"/> + <param name="key_added" value="umap_density"/> + </conditional> + <section name="advanced_common"> + <param name="show_log" value="true" /> + </section> + <output name="hidden_output"> + <assert_contents> + <has_text_matching expression="sc.tl.embedding_density"/> + <has_text_matching expression="basis='umap'"/> + <has_text_matching expression="key_added='umap_density'"/> + </assert_contents> + </output> + <output name="anndata_out" file="tl.embedding_density.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> + </test> </tests> <help><![CDATA[ Cluster cells into subgroups (`tl.louvain`) @@ -602,7 +649,7 @@ This requires to run `pp.neighbors`, first. More details on the `tl.louvain scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.louvain.html>`_ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.louvain.html>`_ Cluster cells into subgroups (`tl.leiden`) ========================================== @@ -612,7 +659,7 @@ The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015. More details on the `tl.leiden scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.leiden.html>`_ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.leiden.html>`_ Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca` ============================================================================================================ @@ -620,7 +667,7 @@ @CMD_pca_outputs@ More details on the `pp.pca scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.pp.pca.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.pca.html>`__ Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca` ============================================================================================================ @@ -628,7 +675,7 @@ @CMD_pca_outputs@ More details on the `tl.pca scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.pca.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.pca.html>`__ Diffusion Maps, using `tl.diffmap` ================================== @@ -645,24 +692,24 @@ `method=='umap'`. Differences between these options shouldn't usually be dramatic. -The diffusion map representation of data are added to the return AnnData in the multi-dimensional -observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors +The diffusion map representation of data are added to the return AnnData in the multi-dimensional +observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors as colum. It can be accessed using the inspect tool for AnnData More details on the `tl.diffmap scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.diffmap.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.diffmap.html>`__ t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne` ======================================================================= t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been proposed for visualizating single-cell data by (Amir et al, 2013). Here, by default, -we use the implementation of *scikit-learn* (Pedregosa et al, 2011). +we use the implementation of *scikit-learn* (Pedregosa et al, 2011). It returns `X_tsne`, tSNE coordinates of data. More details on the `tl.tsne scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.tsne.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.tsne.html>`__ Embed the neighborhood graph using UMAP, using `tl.umap` ======================================================== @@ -678,30 +725,30 @@ (McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint <https://doi.org/10.1101/298430>`__. -The UMAP coordinates of data are added to the return AnnData in the multi-dimensional +The UMAP coordinates of data are added to the return AnnData in the multi-dimensional observations annotation (obsm). This data is accessible using the inspect tool for AnnData More details on the `tl.umap scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.umap.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.umap.html>`__ Force-directed graph drawing, using `tl.draw_graph` =================================================== -Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. -It has been suggested for visualizing single-cell data by Islam et al, 11. -Many other layouts as implemented in igraph are available. Similar approaches have been used by +Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. +It has been suggested for visualizing single-cell data by Islam et al, 11. +Many other layouts as implemented in igraph are available. Similar approaches have been used by Zunder et al, 2015 or Weinreb et al, 2016. -This is an alternative to tSNE that often preserves the topology of the data better. +This is an alternative to tSNE that often preserves the topology of the data better. This requires to run `pp.neighbors`, first. The default layout (ForceAtlas2) uses the package fa2. -The coordinates of graph layout are added to the return AnnData in the multi-dimensional +The coordinates of graph layout are added to the return AnnData in the multi-dimensional observations annotation (obsm). This data is accessible using the inspect tool for AnnData. More details on the `tl.draw_graph scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.draw_graph.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.draw_graph.html>`__ Infer progression of cells through geodesic distance along the graph (`tl.dpt`) =============================================================================== @@ -714,7 +761,7 @@ `n_branchings>1`. We recommend, however, to only use `tl.dpt` for computing pseudotime (`n_branchings=0`) and to detect branchings via `paga`. For pseudotime, you need -to annotate your data with a root cell. +to annotate your data with a root cell. This requires to run `pp.neighbors`, first. In order to reproduce the original implementation of DPT, use `method=='gauss'` in @@ -730,7 +777,7 @@ The tool is similar to the R package `destiny` of Angerer et al (2016). More details on the `tl.dpt scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.dpt.html>`_ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_ Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`) @@ -761,7 +808,7 @@ These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects More details on the `tl.paga scanpy documentation -<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.paga.html>`_ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_ ]]></help> <expand macro="citations"/> </tool>