Reduce slightly scanpy tools profile for wider instance support (#6001)

* Reduce slightly scanpy tools profile for wider instance support * replace all scanpy links on cluster_reduce... * More links * Apply suggestions from code review Co-authored-by: Pavankumar Videm <[email protected]> * Update macros.xml --------- Co-authored-by: Pavankumar Videm <[email protected]> Co-authored-by: Björn Grüning <[email protected]>
galaxyproject · May 18, 2024 · 9d49b2a · 9d49b2a
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diff --git a/tools/scanpy/cluster_reduce_dimension.xml b/tools/scanpy/cluster_reduce_dimension.xml
@@ -648,7 +648,7 @@ analysis by Levine et al, 2015.
 This requires to run `pp.neighbors`, first.
 
 More details on the `tl.louvain scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.louvain.html>`_
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.louvain.html>`_
 
 Cluster cells into subgroups (`tl.leiden`)
 ==========================================
@@ -658,23 +658,23 @@ Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved versio
 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/stable/api/scanpy.tl.leiden.html>`_
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.leiden.html>`_
 
 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
 ============================================================================================================
 
 @CMD_pca_outputs@
 
 More details on the `pp.pca scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.pca.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.pca.html>`__
 
 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
 ============================================================================================================
 
 @CMD_pca_outputs@
 
 More details on the `tl.pca scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.pca.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.pca.html>`__
 
 Diffusion Maps, using `tl.diffmap`
 ==================================
@@ -696,7 +696,7 @@ observations annotation (obsm). It is the right eigen basis of the transition ma
 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/stable/api/scanpy.tl.diffmap.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html>`__
 
 t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
 =======================================================================
@@ -708,7 +708,7 @@ 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/stable/api/scanpy.tl.tsne.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.tsne.html>`__
 
 Embed the neighborhood graph using UMAP, using `tl.umap`
 ========================================================
@@ -728,7 +728,7 @@ The UMAP coordinates of data are added to the return AnnData in the multi-dimens
 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/stable/api/scanpy.tl.umap.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.umap.html>`__
 
 Force-directed graph drawing, using `tl.draw_graph`
 ===================================================
@@ -747,7 +747,7 @@ The coordinates of graph layout are added to the return AnnData in the multi-dim
 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/stable/api/scanpy.tl.draw_graph.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html>`__
 
 Infer progression of cells through geodesic distance along the graph (`tl.dpt`)
 ===============================================================================
@@ -776,7 +776,7 @@ If `n_branchings==0`, no field `dpt_groups` will be written.
 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/stable/api/scanpy.tl.dpt.html>`_
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.dpt.html>`_
 
 
 Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)
@@ -807,7 +807,7 @@ The returned AnnData object contains:
 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/stable/api/scanpy.tl.paga.html>`_
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.paga.html>`_
     ]]></help>
     <expand macro="citations"/>
 </tool>
diff --git a/tools/scanpy/filter.xml b/tools/scanpy/filter.xml
@@ -458,7 +458,7 @@ Only provide one of the optional parameters `min_counts`, `min_genes`,
 `max_counts`, `max_genes` per call.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.filter_cells.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.filter_cells.html>`__
 
 
 Filter genes based on number of cells or counts (`pp.filter_genes`)
@@ -472,14 +472,14 @@ Only provide one of the optional parameters `min_counts`, `min_cells`,
 `max_counts`, `max_cells` per call.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.filter_genes.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.filter_genes.html>`__
 
 
 Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (`tl.filter_rank_genes_groups`)
 ==========================================================================================================================================================
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.filter_rank_genes_groups.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.filter_rank_genes_groups.html>`__
 
 
 Annotate highly variable genes (`pp.highly_variable_genes`)
@@ -494,7 +494,7 @@ Subsample to a fraction of the number of observations (`pp.subsample`)
 ======================================================================
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.subsample.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.subsample.html>`__
 
 Downsample counts (`pp.downsample_counts`)
 ==========================================
@@ -503,7 +503,7 @@ Downsample counts so that each cell has no more than `target_counts`. Cells with
 has been implemented by M. D. Luecken.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.downsample_counts.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.downsample_counts.html>`__
 
 
     ]]></help>

diff --git a/tools/scanpy/inspect.xml b/tools/scanpy/inspect.xml
@@ -893,7 +893,7 @@ And also the variable level metrics:
 - pct_dropout_by_{expr_type} (e.g. "pct_dropout_by_counts", percentage of cells this feature does not appear in)
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.calculate_qc_metrics.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.calculate_qc_metrics.html>`__
 
 Compute a neighborhood graph of observations, using `pp.neighbors`
 ==================================================================
@@ -912,7 +912,7 @@ The returned AnnData object contains:
 This data are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.neighbors.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.neighbors.html>`__
 
 Score a set of genes, using `tl.score_genes`
 ============================================
@@ -925,7 +925,7 @@ This reproduces the approach in Seurat (Satija et al, 2015) and has been impleme
 for Scanpy by Davide Cittaro.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.score_genes.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.score_genes.html>`__
 
 Score cell cycle genes, using `tl.score_genes_cell_cycle`
 =========================================================
@@ -935,7 +935,7 @@ scores and assigns a cell cycle phase (G1, S or G2M). See
 `score_genes` for more explanation.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.score_genes_cell_cycle.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.score_genes_cell_cycle.html>`__
 
 Rank genes for characterizing groups, using `tl.rank_genes_groups`
 ==================================================================
@@ -951,7 +951,7 @@ The returned AnnData object contains:
 This data are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.rank_genes_groups.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.rank_genes_groups.html>`__
 
 
 Calculate an overlap score between data-deriven marker genes and provided markers (`tl.marker_gene_overlap`)
@@ -964,13 +964,13 @@ Logarithmize the data matrix (`pp.log1p`)
 =========================================
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.log1p.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.log1p.html>`__
 
 Scale data to unit variance and zero mean (`pp.scale`)
 ======================================================
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.scale.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.scale.html>`__
 
 Computes the square root the data matrix (`pp.sqrt`)
 ====================================================

diff --git a/tools/scanpy/macros.xml b/tools/scanpy/macros.xml
@@ -1,7 +1,7 @@
 <macros>
     <token name="@TOOL_VERSION@">1.9.6</token>
-    <token name="@VERSION_SUFFIX@">2</token>
-    <token name="@profile@">22.05</token>
+    <token name="@VERSION_SUFFIX@">3</token>
+    <token name="@profile@">21.09</token>
     <xml name="requirements">
         <requirements>
             <requirement type="package" version="@TOOL_VERSION@">scanpy</requirement>

diff --git a/tools/scanpy/normalize.xml b/tools/scanpy/normalize.xml
@@ -322,7 +322,7 @@ the same total count after normalization.
 Similar functions are used, for example, by Seurat, Cell Ranger or SPRING.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.normalize_per_cell.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.normalize_per_cell.html>`__
 
 
 Normalization and filtering as of Zheng et al. (2017), the Cell Ranger R Kit of 10x Genomics (`pp.recipe_zheng17`)
@@ -341,7 +341,7 @@ The recipe runs the following steps:
 - scale to unit variance and shift to zero mean
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_zheng17.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.recipe_zheng17.html>`__
 
 
 Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`)
@@ -350,7 +350,7 @@ Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`)
 Expects non-logarithmized data. If using logarithmized data, pass `log=False`.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_weinreb17.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.recipe_weinreb17.html>`__
 
 
 Normalization and filtering as of Seurat et al (2015) (`pp.recipe_seurat`)
@@ -361,7 +361,7 @@ This uses a particular preprocessing.
 Expects non-logarithmized data. If using logarithmized data, pass `log=False`.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_seurat.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.recipe_seurat.html>`__
 
 
 Markov Affinity-based Graph Imputation of Cells (MAGIC) as of Van Dijk D et al. (2018) (`external.pp.magic`)
@@ -375,7 +375,7 @@ The algorithm implemented here has changed primarily in two ways compared to the
 - Secondly, data diffusion is applied in the PCA space, rather than the data space, for speed and memory improvements.
 
 More details on the `scanpy documentation
-<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.external.pp.magic.html>`__
+<https://scanpy.readthedocs.io/en/stable/api/scanpy.external.pp.magic.html>`__
 
     ]]></help>
     <expand macro="citations"/>