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Changeset 17:b70219da4a96 (2024-09-14)

Previous changeset 16:a39040ae0a38 (2024-08-20) Next changeset 18:9fb627495ec2 (2024-09-19)

Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 91121b1e72696f17478dae383badaa71e9f96dbb

added:
test-data/external.pp.bbknn.krumsiek11.h5ad
test-data/pl.rank_genes_groups_tracksplot.newton-cg.pbmc68k_highly_reduced_marker_filtered_1.png
test-data/pl.scrublet_score_distribution.png
test-data/pl.stacked_violin_pp.filter_genes_dispersion.krumsiek11-seurat.png
test-data/pp.pca.krumsiek11.batch.h5ad
test-data/pp.scrublet.krumsiek11.h5ad

diff -r a39040ae0a38 -r b70219da4a96 README.md
--- a/README.md Tue Aug 20 09:51:42 2024 +0000
+++ b/README.md Sat Sep 14 12:43:38 2024 +0000

[

@@ -25,6 +25,7 @@
     `pp.highly_variable_genes` | Extract highly variable genes
     `pp.subsample` | Subsample to a fraction of the number of observations
     `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts
+    `pp.scrublet` | Predict doublets

3. Normalize (`normalize.xml`)

@@ -34,14 +35,18 @@
     `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]
     `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]
     `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15]
+    `external.pp.magic` | Denoising using Markov Affinity-based Graph Imputation of Cells (MAGIC) API

4. Remove confounders (`remove_confounder.xml`)

     Methods | Description
     --- | ---
    `pp.regress_out` | Regress out unwanted sources of variation
-   `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors
+   
    `pp.combat` | ComBat function for batch effect correction
+    `external.pp.bbknn` | Batch effect removal with Batch balanced KNN (BBKNN)
+    `external.pp.harmony_integrate` | Integrate multiple single-cell experiments with Harmony
+    `external.pp.scanorama_integrate` | Integrate multiple single-cell experiments with Scanorama

5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`)

@@ -49,14 +54,14 @@
     --- | ---
     `tl.louvain` | Cluster cells into subgroups
     `tl.leiden` | Cluster cells into subgroups
-    `tl.pca` | Principal component analysis
-    `pp.pca` | Principal component analysis (appears to be the same func...)
+    `pp.pca` | Principal component analysis
     `tl.diffmap` | Diffusion Maps
     `tl.tsne` | t-SNE
     `tl.umap` | Embed the neighborhood graph using UMAP
     `tl.draw_graph` | Force-directed graph drawing
     `tl.dpt` | Infer progression of cells through geodesic distance along the graph
     `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds
+    `tl.embedding_density` | Calculate the density of cells in an embedding (per condition)

6. Plot (`plot.xml`)

@@ -66,18 +71,20 @@
         --- | ---
         `pl.scatter` | Scatter plot along observations or variables axes
         `pl.heatmap` | Heatmap of the expression values of set of genes
+        `pl.tracksplot` | Tracks plot of the expression values per cell
         `pl.dotplot` | Makes a dot plot of the expression values
         `pl.violin` | Violin plot
         `pl.stacked_violin` | Stacked violin plots
         `pl.matrixplot` | Heatmap of the mean expression values per cluster
         `pl.clustermap` | Hierarchically-clustered heatmap
-
+
     2. Preprocessing

         Methods | Description
         --- | ---
         `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells
         `pl.highly_variable_genes` | Plot dispersions versus means for genes
+        `pl.scrublet_score_distribution` | Histogram of doublet scores

     3. PCA

@@ -96,12 +103,13 @@
         `pl.umap` | Scatter plot in UMAP basis
         `pl.diffmap` | Scatter plot in Diffusion Map basis
         `pl.draw_graph` | Scatter plot in graph-drawing basis
+        `pl.embedding_density` | Density of cells in an embedding (per condition)

     5. Branching trajectories and pseudotime, clustering

         Methods | Description
         --- | ---
-        `pl.dpt_groups_pseudotime` | Plot groups and pseudotime
+        
         `pl.dpt_timeseries` | Heatmap of pseudotime series
         `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges
         `pl.paga_compare` | Scatter and PAGA graph side-by-side
@@ -113,3 +121,8 @@
         --- | ---
         `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot
         `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons
+        `pl.rank_genes_groups_stacked_violin` | Plot ranking of genes as stacked violin plot
+        `pl.rank_genes_groups_heatmap` | Plot ranking of genes as heatmap plot
+        `pl.rank_genes_groups_dotplot` | Plot ranking of genes as dotplot plot
+        `pl.rank_genes_groups_matrixplot` | Plot ranking of genes as matrixplot plot
+        `pl.rank_genes_groups_tracksplot` | Plot ranking of genes as tracksplot plot

diff -r a39040ae0a38 -r b70219da4a96 macros.xml
--- a/macros.xml Tue Aug 20 09:51:42 2024 +0000
+++ b/macros.xml Sat Sep 14 12:43:38 2024 +0000

[

b'@@ -1,17 +1,15 @@\n <macros>\n- <token name="@TOOL_VERSION@">1.9.6</token>\n- <token name="@VERSION_SUFFIX@">4</token>\n- <token name="@profile@">21.09</token>\n+ <token name="@TOOL_VERSION@">1.10.2</token>\n+ <token name="@VERSION_SUFFIX@">0</token>\n+ <token name="@PROFILE@">21.09</token>\n <xml name="requirements">\n <requirements>\n <requirement type="package" version="@TOOL_VERSION@">scanpy</requirement>\n- <requirement type="package" version="3.0.6">loompy</requirement>\n- <requirement type="package" version="0.10.1">leidenalg</requirement>\n- <requirement type="package" version="0.8.1">louvain</requirement>\n- <requirement type="package" version="1.5.3">pandas</requirement>\n- <requirement type="package" version="3.7">matplotlib</requirement>\n- <requirement type="package" version="0.12.2">seaborn</requirement>\n- <requirement type="package" version="3.0.0">magic-impute</requirement>\n+ <requirement type="package" version="0.10.3">anndata</requirement>\n+ <requirement type="package" version="1.26.4">numpy</requirement>\n+ <requirement type="package" version="2.2.2">pandas</requirement>\n+ <requirement type="package" version="1.14.1">scipy</requirement>\n+ <requirement type="package" version="0.14.2">statsmodels</requirement> \n <yield />\n </requirements>\n </xml>\n@@ -22,7 +20,7 @@\n </xml>\n <xml name="creators">\n <creator>\n- <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />\n+ <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/"/>\n </creator>\n </xml>\n <xml name="citations">\n@@ -31,28 +29,13 @@\n <citation type="doi">10.1093/gigascience/giaa102</citation>\n </citations>\n </xml>\n- <xml name="version_command">\n- <version_command><![CDATA[python -c "import scanpy as sc;print(\'scanpy version: %s\' % sc.__version__)"]]></version_command>\n- </xml>\n- <token name="@CMD@"><![CDATA[\n-cp \'$adata\' \'anndata.h5ad\' &&\n-cat \'$script_file\' > \'$hidden_output\' &&\n-python \'$script_file\' >> \'$hidden_output\' &&\n-ls . >> \'$hidden_output\' &&\n-touch \'anndata_info.txt\' &&\n-cat \'anndata_info.txt\' @CMD_prettify_stdout@\n- ]]>\n- </token>\n- <token name="@CMD_imports@"><![CDATA[\n-import scanpy as sc\n-import pandas as pd\n-import numpy as np\n- ]]>\n- </token>\n+ \n+ \n+ \n <xml name="sanitize_query" token_validinitial="string.printable">\n <sanitizer>\n <valid initial="@VALIDINITIAL@">\n- <remove value="'" />\n+ <remove value="'"/>\n </valid>\n </sanitizer>\n </xml>\n@@ -62,58 +45,55 @@\n <add value=","/>\n </valid>\n </sanitizer>\n- </xml>\n+ </xml> \n <xml name="inputs_anndata">\n <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/>\n </xml>\n- <token name="@CMD_read_inputs@"><![CDATA[\n-adata = sc.read_h5ad(\'anndata.h5ad\')\n-]]>\n- </token>\n <xml name="inputs_common_advanced">\n <section name="advanced_common" title="Advanced Options" expanded="false">\n- <param name="show_log" type="boolean" checked="false" label="Output Log?" />\n+ <param name="show_log" type="boolean" checked="false" label="Output Log?"/>\n </section>\n </xml>\n <xml name="anndata_outputs">\n- <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/>\n- <data name="hidden_output" format="txt" label="Log file" >\n+ <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix">\n+ <yield />\n+ </data>\n+ '..b'row_palette" type="select" optional="true" label="Colors to use in each of the stacked violin plots">\n <option value="muted">muted</option>\n <expand macro="seaborn_color_palette_options"/>\n </param>\n- <param argument="standard_scale" type="select" label="Standardize a dimension between 0 and 1" help="Each variable or observation is subtracted by the minimum and divided each by its maximum.">\n- <option value="None">No standardization</option>\n- <option value="var">Standardization on variable</option>\n- <option value="obs">Standardization on observation</option>\n- </param>\n- <expand macro="seaborn_violinplot"/>\n+ <expand macro="param_standard_scale"/>\n+ <expand macro="params_seaborn_violinplot"/>\n+ <param argument="yticklabels" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Set to view the y tick labels"/>\n+ <expand macro="param_vmin" label="The value representing the lower limit of the color scale" help="Values smaller than vmin are plotted with the same color as vmin."/>\n+ <expand macro="param_vmax" label="The value representing the upper limit of the color scale" help="Values larger than vmax are plotted with the same color as vmax."/>\n+ <expand macro="param_vcenter"/>\n </xml>\n- <token name="@CMD_pl_stacked_violin@"><![CDATA[\n+ <token name="@CMD_PARAMS_PL_STACKED_VIOLIN@"><![CDATA[\n swap_axes=$method.swap_axes,\n- @CMD_conditional_stripplot@\n- scale=\'$method.violin_plot.scale\',\n- #if $method.row_palette\n+ @CMD_CONDITIONAL_STRIPPLOT@\n+ density_norm=\'$method.violin_plot.density_norm\',\n+ #if $method.row_palette:\n row_palette=\'$method.row_palette\',\n #end if\n- #if str($method.standard_scale) != \'None\'\n- standard_scale=\'$method.standard_scale\',\n+ @CMD_STANDARD_SCALE@\n+ @CMD_PARAMS_SEABORN_VIOLINPLOT@\n+ yticklabels=$method.yticklabels,\n+ #if str($method.vmin) != \'\':\n+ vmin=$method.vmin,\n #end if\n- @CMD_params_seaborn_violinplot@\n+ #if str($method.vmax) != \'\':\n+ vmax=$method.vmax,\n+ #end if\n+ #if str($method.vcenter) != \'\':\n+ vcenter=$method.vcenter,\n+ #end if\n ]]>\n </token>\n+\n+ <xml name="params_scatter_outine">\n+ <param argument="add_outline" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Add a thin border around groups of dots" help="In some situations this can enhance the aesthetics of the resulting image"/>\n+ <param name="outline_color_border" type="select" optional="true" label="Border color around the scatter dot" help="Default: black">\n+ <expand macro="matplotlib_color"/>\n+ </param>\n+ <param name="outline_color_gap" type="select" optional="true" label="Gap color between the border color and the scatter dot" help="Default: white">\n+ <expand macro="matplotlib_color"/>\n+ </param>\n+ <param argument="outline_width_border" type="float" value="0.3" optional="true" label=" width of the border color as a fraction of the scatter dot size "/>\n+ <param argument="outline_width_gap" type="float" value="0.05" optional="true" label="The width of the gap color"/>\n+ </xml>\n+ <token name="@CMD_SCATTER_OUTINE@"><![CDATA[\n+ add_outline=$method.add_outline,\n+ #if $method.outline_color_border and $method.outline_color_gap:\n+ outline_color=($method.outline_color_border, $method.outline_color_gap),\n+ #end if\n+ outline_width=($method.outline_width_border, $method.outline_width_gap),\n+ ]]>\n+ </token>\n+\n+\n+ \n+ \n </macros>\n'

diff -r a39040ae0a38 -r b70219da4a96 remove_confounders.xml
--- a/remove_confounders.xml Tue Aug 20 09:51:42 2024 +0000
+++ b/remove_confounders.xml Sat Sep 14 12:43:38 2024 +0000

[

b'@@ -1,67 +1,164 @@\n-<tool id="scanpy_remove_confounders" name="Remove confounders" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@profile@">\n+<tool id="scanpy_remove_confounders" name="Scanpy remove confounders" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">\n <description>with scanpy</description>\n <macros>\n <import>macros.xml</import>\n </macros>\n <expand macro="bio_tools"/>\n- <expand macro="requirements"/>\n+ <expand macro="requirements">\n+ <requirement type="package" version="1.6.0">bbknn</requirement>\n+ <requirement type="package" version="0.0.10">harmonypy</requirement>\n+ <requirement type="package" version="1.7.4">scanorama</requirement>\n+ <requirement type="package" version="0.5.13">pynndescent</requirement>\n+ </expand>\n <command detect_errors="exit_code"><![CDATA[\n @CMD@\n ]]></command>\n <configfiles>\n <configfile name="script_file"><![CDATA[\n-@CMD_imports@\n-@CMD_read_inputs@\n+@CMD_IMPORTS@\n+@CMD_READ_INPUTS@\n \n-#if $method.method == "pp.regress_out"\n+#if str($method.method) == \'pp.regress_out\':\n+print("stats before regress_out:", "min=", adata.X.min(), "max=", adata.X.max(), "mean=", adata.X.mean())\n+\n+import os\n sc.pp.regress_out(\n adata=adata,\n+ #if str($method.layer) != \'\':\n+ layer=\'$method.layer\',\n+ #end if\n #set $keys = [str(x.strip()) for x in str($method.keys).split(\',\')]\n keys=$keys,\n+ n_jobs = int(os.getenv("GALAXY_SLOTS", 4)),\n copy=False)\n \n-#else if $method.method == "pp.mnn_correct"\n- #for i, filepath in enumerate($methods.extra_adata)\n-adata_$i = ad.read(\'$filepath\')\n- #end for\n+print("stats after regress_out:", "min=", adata.X.min(), "max=", adata.X.max(), "mean=", adata.X.mean())\n \n-sc.pp.mnn_correct(\n- adata,\n- #for i, filepath in enumerate($methods.extra_adata)\n- adata_$i,\n- #end for\n- #if $methods.var_subset\n- #set $var_subset=([x.strip() for x in str($method.var_subset).split(\',\')])\n- var_subset=$var_subset,\n- #end if\n- batch_key=\'$method.batch_key\',\n- index_unique=\'$method.index_unique\'\n- #if $methods.batch_categories\n- #set $batch_categories=([x.strip() for x in str($method.batch_categories).split(\',\')])\n- batch_categories=$batch_categories,\n- #end if\n- k=$method.k,\n- sigma=$method.sigma,\n- cos_norm_in=$method.cos_norm_in,\n- cos_norm_out=$method.cos_norm_out,\n- svd_dim=$method.svd_dim,\n- var_adj=$method.var_adj,\n- compute_angle=$method.compute_angle,\n- mnn_order=\'$method.mnn_order\',\n- svd_mode=\'$method.svd_mode\',\n- do_concatenate=True,\n- save_raw=True,\n- n_jobs=\\${GALAXY_SLOTS:-4})\n+## This function is commented out because the conda package is not working. Please add this if there is user demand and the conda package is fixed. If not please remove in the next update.\n+## #else if str($method.method) == \'external.pp.mnn_correct\':\n+## #if $method.extra_adata:\n+## #for i, filepath in enumerate($method.extra_adata)\n+## adata_$i = sc.read_h5ad(\'$filepath\')\n+## #end for\n+## #end if\n+## import os\n+## corrected = sc.external.pp.mnn_correct(\n+## adata,\n+## #if $method.extra_adata:\n+## #for i, filepath in enumerate($method.extra_adata)\n+## adata_$i,\n+## #end for\n+## #end if\n+## #if str($method.var_subset) != \'\':\n+## #set $var_subset=([x.strip() for x in str($method.var_subset).split(\',\')])\n+## var_subset=$var_subset,\n+## #end if\n+## batch_key=\'$method.batch_key\',\n+## index_unique=\'$method.index_unique\',\n+## #if str($method.batch_categories) != \'\':\n+## #set $batch_categories=([x.strip() for x in str($method.batch_categories).split(\',\')])\n+## batch_categories=$batch_categories,\n+## #end if\n+## k=$method.k,\n+## '..b'ents>\n+ <has_h5_keys keys="obsm/X_scanorama"/>\n+ </assert_contents>\n+ </output>\n </test>\n </tests>\n <help><![CDATA[\n Regress out unwanted sources of variation, using `pp.regress_out`\n =================================================================\n \n-Regress out unwanted sources of variation, using simple linear regression. This is \n+Regress out unwanted sources of variation, using simple linear regression. This is\n inspired by Seurat\'s `regressOut` function in R.\n \n More details on the `scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.regress_out.html>`__\n \n-Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct`\n-==================================================================================\n+.. This function is commented out because the conda package is not working. Please add this if there is user demand and the conda package is fixed. If not please remove in the next update.\n+.. Correct batch effects by matching mutual nearest neighbors, using `external.pp.mnn_correct`\n+.. ===========================================================================================\n \n-This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the \n-original order containing corrected expression values or a concatenated matrix or AnnData object.\n+.. This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the\n+.. original order containing corrected expression values or a concatenated matrix or AnnData object.\n \n-Be reminded that it is not advised to use the corrected data matrices for differential expression testing.\n+.. Be reminded that it is not advised to use the corrected data matrices for differential expression testing.\n \n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.mnn_correct.html>`__\n+.. More details on the `scanpy documentation\n+.. <https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.mnn_correct.html>`__\n \n \n Correct batch effects with ComBat function (`pp.combat`)\n@@ -203,6 +531,33 @@\n <https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.combat.html>`__\n \n \n+Correct batch effects with bbknn function (`external.pp.bbknn`)\n+===============================================================\n+\n+Batch balanced kNN alters the kNN procedure to identify each cell\xe2\x80\x99s top neighbours in each batch separately instead of the entire cell pool with no accounting for batch. The nearest neighbours for each batch are then merged to create a final list of neighbours for the cell. Aligns batches in a quick and lightweight manner.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.bbknn.html>`__\n+\n+\n+Correct batch effects with harmony function (`external.pp.harmony_integrate`)\n+=============================================================================\n+\n+Harmony is an algorithm for integrating single-cell data from multiple experiments.\n+As Harmony works by adjusting the principal components, this function should be run after performing PCA but before computing the neighbor graph.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.harmony_integrate.html>`__\n+\n+\n+Correct batch effects with scanprama function (`external.pp.scanorama_integrate`)\n+=================================================================================\n+\n+Scanprama is an algorithm for integrating single-cell data from multiple experiments stored in an AnnData object. This function should be run after performing PCA but before computing the neighbor graph.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.scanorama_integrate.html>`__\n+\n ]]></help>\n <expand macro="citations"/>\n </tool>\n'

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