Mercurial > repos > iuc > scanpy_remove_confounders
changeset 1:a89ee42625ad draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
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--- a/README.md Mon Mar 04 10:16:47 2019 -0500 +++ b/README.md Wed Oct 16 06:30:25 2019 -0400 @@ -1,138 +1,115 @@ Scanpy ====== -## Classification of methods into steps +1. Inspect & Manipulate (`inspect.xml`) -Steps: + Methods | Description + --- | --- + `pp.calculate_qc_metrics` | Calculate quality control metrics + `pp.neighbors` | Compute a neighborhood graph of observations + `tl.score_genes` | Score a set of genes + `tl.score_genes_cell_cycle` | Score cell cycle gene + `tl.rank_genes_groups` | Rank genes for characterizing groups + `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**) + `pp.log1p` | Logarithmize the data matrix. + `pp.scale` | Scale data to unit variance and zero mean + `pp.sqrt` | Square root the data matrix -1. Filtering +2. Filter (`filter.xml`) Methods | Description --- | --- `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed. `pp.filter_genes` | Filter genes based on number of cells or counts. - `pp.filter_genes_dispersion` | Extract highly variable genes + `tl.filter_rank_genes_groups` | Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (**to fix**) `pp.highly_variable_genes` | Extract highly variable genes `pp.subsample` | Subsample to a fraction of the number of observations - `queries.gene_coordinates` | (Could not find...) - `queries.mitochondrial_genes` | Retrieves Mitochondrial gene symbols for specific organism through BioMart for filtering - -2. Quality Plots - - These are in-between stages used to measure the effectiveness of a Filtering/Normalisation/Conf.Removal stage either after processing or prior to. + `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts - Methods | Description | Notes - --- | --- | --- - `pp.calculate_qc_metrics` | Calculate quality control metrics - `pl.violin` | violin plot of features, lib. size, or subsets of. - `pl.stacked_violin` | Same as above but for multiple series of features or cells - -3. Normalization +3. Normalize (`normalize.xml`) Methods | Description --- | --- - `pp.normalize_per_cell` | Normalize total counts per cell + `pp.normalize_total` | Normalize counts per cell `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] - `pp.log1p` | Logarithmize the data matrix. - `pp.scale` | Scale data to unit variance and zero mean - `pp.sqrt` | - `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts -4. Conf. removal +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.dca` | Deep count autoencoder to denoise the data - `pp.magic` | Markov Affinity-based Graph Imputation of Cells (MAGIC) API to denoise - `tl.sim` | Simulate dynamic gene expression data [Wittman09] - `pp.calculate_qc_metrics` | Calculate quality control metrics - `tl.score_genes` | Score a set of genes - `tl.score_genes_cell_cycle` | Score cell cycle genes - `tl.cyclone` | Assigns scores and predicted class to observations based on cell-cycle genes [Scialdone15] - `tl.sandbag` | Calculates pairs of genes serving as markers for each cell-cycle phase [Scialdone15] + `pp.combat` | ComBat function for batch effect correction -5. Clustering and Heatmaps +5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`) Methods | Description --- | --- - `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15] - `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17] + `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...) `tl.diffmap` | Diffusion Maps `tl.tsne` | t-SNE `tl.umap` | Embed the neighborhood graph using UMAP - `tl.phate` | PHATE - `pp.neighbors` | Compute a neighborhood graph of observations - `tl.rank_genes_groups` | Rank genes for characterizing groups - `pl.rank_genes_groups` | - `pl.rank_genes_groups_dotplot` | - `pl.rank_genes_groups_heatmap` | - `pl.rank_genes_groups_matrixplot` | - `pl.rank_genes_groups_stacked_violin` | - `pl.rank_genes_groups_violin` | - `pl.matrix_plot` | - `pl.heatmap` | - `pl.highest_expr_genes` | - `pl.diffmap` | + `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 + +6. Plot (`plot.xml`) + + 1. Generic + + Methods | Description + --- | --- + `pl.scatter` | Scatter plot along observations or variables axes + `pl.heatmap` | Heatmap of the expression values of set of genes + `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 -6. Cluster Inspection and plotting + 2. Preprocessing - Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related. + 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 + + 3. PCA - Methods | Description - --- | --- - `pl.clustermap` | - `pl.phate` | - `pl.dotplot` | - `pl.draw_graph` | (really general purpose, would not implement directly) - `pl.filter_genes_dispersion` | (depreciated for 'highly_variable_genes') - `pl.matrix` | (could not find in API) - `pl.pca` | - `pl.pca_loadings` | - `pl.pca_overview` | - `pl.pca_variance_ratio` | - `pl.ranking` | (not sure what this does...) - `pl.scatter` | ([very general purpose](https://icb-scanpy.readthedocs-hosted.com/en/latest/api/scanpy.api.pl.scatter.html), would not implement directly) - `pl.set_rcParams_defaults` | - `pl.set_rcParams_scanpy` | - `pl.sim` | - `pl.tsne` | - `pl.umap` | + Methods | Description + --- | --- + `pl.pca` | Scatter plot in PCA coordinates + `pl.pca_loadings` | Rank genes according to contributions to PCs + `pl.pca_variance_ratio` | Scatter plot in PCA coordinates + `pl.pca_overview` | Plot PCA results -7. Branch/Between-Cluster Inspection + 4. Embeddings - Pseudotime analysis, relies on initial clustering. + Methods | Description + --- | --- + `pl.tsne` | Scatter plot in tSNE basis + `pl.umap` | Scatter plot in UMAP basis + `pl.diffmap` | Scatter plot in Diffusion Map basis + `pl.draw_graph` | Scatter plot in graph-drawing basis - Methods | Description - --- | --- - `tl.dpt` | Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i] - `pl.dpt_groups_pseudotime` | - `pl.dpt_timeseries` | - `tl.paga_compare_paths` | - `tl.paga_degrees` | - `tl.paga_expression_entropies` | - `tl.paga` | Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i] - `pl.paga` | - `pl.paga_adjacency` | - `pl.paga_compare` | - `pl.paga_path` | - `pl.timeseries` | - `pl.timeseries_as_heatmap` | - `pl.timeseries_subplot` | + 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 + `pl.paga_path` | Gene expression and annotation changes along paths -Methods to sort | Description ---- | --- -`tl.ROC_AUC_analysis` | (could not find in API) -`tl.correlation_matrix` | (could not find in API) -`rtools.mnn_concatenate` | (could not find in API) -`utils.compute_association_matrix_of_groups` | (could not find in API) -`utils.cross_entropy_neighbors_in_rep` | (could not find in API) -`utils.merge_groups` | (could not find in API) -`utils.plot_category_association` | (could not find in API) -`utils.select_groups` | (could not find in API) \ No newline at end of file + 6. Marker genes + + Methods | Description + --- | --- + `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot + `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons
--- a/README.rst Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,105 +0,0 @@ -The different methods from Scanpy have been grouped by themes: - -1. Filter in `filter.xml` - - Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells` - - Filter genes based on number of cells or counts, using `pp.filter_genes` - - Extract highly variable genes, using `pp.filter_genes_dispersion` - - `tl.highly_variable_genes` (need to be added) - - Subsample to a fraction of the number of observations, using `pp.subsample` - - `queries.gene_coordinates` (need to be added) - - `queries.mitochondrial_genes` (need to be added) - -2. Normalize in `normalize.xml` - - Normalize total counts per cell, using `pp.normalize_per_cell` - - Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17` - - Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17` - - Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat` - - Logarithmize the data matrix, using `pp.log1p` - - Scale data to unit variance and zero mean, using `pp.scale` - - Square root the data matrix, using `pp.sqrt` - - Downsample counts, using `pp.downsample_counts` - -3. Remove confounder in `remove_confounders.xml` - - Regress out unwanted sources of variation, using `pp.regress_out` - - `pp.mnn_correct` (need to be added) - - `pp.mnn_correct` (need to be added) - - `pp.magic` (need to be added) - - `tl.sim` (need to be added) - - `pp.calculate_qc_metrics` (need to be added) - - Score a set of genes, using `tl.score_genes` - - Score cell cycle genes, using `tl.score_genes_cell_cycle` - - `tl.cyclone` (need to be added) - - `tl.andbag` (need to be added) - -4. Cluster and reduce dimension in `cluster_reduce_dimension.xml` - - `tl.leiden` (need to be added) - - Cluster cells into subgroups, using `tl.louvain` - - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca` - - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca` - - Diffusion Maps, using `tl.diffmap` - - t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne` - - Embed the neighborhood graph using UMAP, using `tl.umap` - - `tl.phate` (need to be added) - - Compute a neighborhood graph of observations, using `pp.neighbors` - - Rank genes for characterizing groups, using `tl.rank_genes_groups` - -4. Inspect - - `tl.paga_compare_paths` (need to be added) - - `tl.paga_degrees` (need to be added) - - `tl.paga_expression_entropies` (need to be added) - - Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga` - - Infer progression of cells through geodesic distance along the graph, using `tl.dpt` - -5. Plot - 1. Generic - - Scatter plot along observations or variables axes, using `pl.scatter` - - Heatmap of the expression values of set of genes, using `pl.heatmap` - - Makes a dot plot of the expression values, using `pl.dotplot` - - Violin plot, using `pl.violin` - - `pl.stacked_violin` (need to be added) - - Heatmap of the mean expression values per cluster, using `pl.matrixplot` - - Hierarchically-clustered heatmap, using `pl.clustermap` - - `pl.ranking` - - 2. Preprocessing - - Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes` - - Plot dispersions versus means for genes, using `pl.filter_genes_dispersion` - - `pl.highly_variable_genes` (need to be added) - - `pl.calculate_qc_metrics` (need to be added) - - 3. PCA - - Scatter plot in PCA coordinates, using `pl.pca` - - Rank genes according to contributions to PCs, using `pl.pca_loadings` - - Scatter plot in PCA coordinates, using `pl.pca_variance_ratio` - - Plot PCA results, using `pl.pca_overview` - - 4. Embeddings - - Scatter plot in tSNE basis, using `pl.tsne` - - Scatter plot in UMAP basis, using `pl.umap` - - Scatter plot in Diffusion Map basis, using `pl.diffmap` - - `pl.draw_graph` (need to be added) - - 5. Branching trajectories and pseudotime, clustering - - Plot groups and pseudotime, using `pl.dpt_groups_pseudotime` - - Heatmap of pseudotime series, using `pl.dpt_timeseries` - - Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga` - - `pl.paga_compare` (need to be added) - - `pl.paga_path` (need to be added) - - 6. Marker genes: - - Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups` - - `pl.rank_genes_groups_dotplot` (need to be added) - - `pl.rank_genes_groups_heatmap` (need to be added) - - `pl.rank_genes_groups_matrixplot` (need to be added) - - `pl.rank_genes_groups_stacked_violin` (need to be added) - - `pl.rank_genes_groups_violin` (need to be added) - - 7. Misc - - `pl.phate` (need to be added) - - `pl.matrix` (need to be added) - - `pl.paga_adjacency` (need to be added) - - `pl.timeseries` (need to be added) - - `pl.timeseries_as_heatmap` (need to be added) - - `pl.timeseries_subplot` (need to be added) - - \ No newline at end of file
--- a/macros.xml Mon Mar 04 10:16:47 2019 -0500 +++ b/macros.xml Wed Oct 16 06:30:25 2019 -0400 @@ -1,10 +1,12 @@ <macros> - <token name="@version@">1.4</token> + <token name="@version@">1.4.4</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="2.9.0">h5py</requirement> + <requirement type="package" version="0.7.0">leidenalg</requirement> <yield /> </requirements> </xml> @@ -14,102 +16,33 @@ </citations> </xml> <xml name="version_command"> - <version_command><![CDATA[python -c "import scanpy.api as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> + <version_command><![CDATA[python -c "import scanpy as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> </xml> <token name="@CMD@"><![CDATA[ +cp '$adata' 'anndata.h5ad' && cat '$script_file' && -python '$script_file' +python '$script_file' && +ls . ]]> </token> <token name="@CMD_imports@"><![CDATA[ -import scanpy.api as sc +import scanpy as sc import pandas as pd import numpy as np ]]> </token> <xml name="inputs_anndata"> - <conditional name="input"> - <param name="format" type="select" label="Format for the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - <when value="loom"> - <param name="adata" type="data" format="loom" label="Annotated data matrix"/> - <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/> - <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/> - <param name="x_name" type="text" value="spliced" label="X_name"/> - <param name="obs_names" type="text" value="CellID" label="obs_names"/> - <param name="var_names" type="text" value="Gene" label="var_names"/> - </when> - <when value="h5ad"> - <param name="adata" type="data" format="h5" label="Annotated data matrix"/> - </when> - </conditional> + <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/> </xml> <token name="@CMD_read_inputs@"><![CDATA[ -#if $input.format == 'loom' -adata = sc.read_loom( - '$input.adata', - sparse=$input.sparse, - cleanup=$input.cleanup, - X_name='$input.x_name', - obs_names='$input.obs_names', - var_names='$input.var_names') -#else if $input.format == 'h5ad' -adata = sc.read_h5ad('$input.adata') -#end if +adata = sc.read('anndata.h5ad') ]]> </token> - <xml name="anndata_output_format"> - <param name="anndata_output_format" type="select" label="Format to write the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - </xml> - <xml name="anndata_modify_output_input"> - <conditional name="modify_anndata"> - <param name="modify_anndata" type="select" label="Return modify annotate data matrix?"> - <option value="true">Yes</option> - <option value="false">No</option> - </param> - <when value="true"> - <expand macro="anndata_output_format"/> - </when> - <when value="false"/> - </conditional> - </xml> <xml name="anndata_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'loom'</filter> - </data> - </xml> - <xml name="anndata_modify_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom'</filter> - </data> + <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/> </xml> <token name="@CMD_anndata_write_outputs@"><![CDATA[ -#if $anndata_output_format == 'loom' -adata.write_loom('anndata.loom') -#else if $anndata_output_format == 'h5ad' adata.write('anndata.h5ad') -#end if -]]> - </token> - <token name="@CMD_anndata_write_modify_outputs@"><![CDATA[ -#if $modify_anndata.modify_anndata == 'true' - #if $modify_anndata.anndata_output_format == 'loom' -adata.write_loom('anndata.loom') - #elif $modify_anndata.anndata_output_format == 'h5ad' -adata.write('anndata.h5ad') - #end if -#end if ]]> </token> <xml name="svd_solver"> @@ -423,7 +356,7 @@ <param argument="use_raw" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use `raw` attribute of input if present" help=""/> </xml> <xml name="param_log"> - <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?" help=""/> + <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?"/> </xml> <xml name="pl_figsize"> <conditional name="figsize"> @@ -473,7 +406,7 @@ <param argument="layer" type="text" value="" label="Name of the AnnData object layer that wants to be plotted" help="By default `adata.raw.X` is plotted. If `use_raw=False` is set, then `adata.X` is plotted. If layer is set to a valid layer name, then the layer is plotted. layer takes precedence over `use_raw`."/> </xml> <token name="@CMD_param_plot_inputs@"><![CDATA[ - adata=adata, + adata, save='.$format', show=False, ]]></token> @@ -512,9 +445,6 @@ #end for var_group_positions=$var_group_positions, var_group_labels=$var_group_labels, - #else - var_group_positions=None, - var_group_labels=None, #end if #if $method.var_group_rotation var_group_rotation=$method.var_group_rotation, @@ -729,44 +659,42 @@ linewidths=$method.matplotlib_pyplot_scatter.linewidths, edgecolors='$method.matplotlib_pyplot_scatter.edgecolors' ]]></token> - <xml name="section_violin_plots"> - <section name="violin_plot" title="Violin plot attributes"> - <conditional name="stripplot"> - <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <conditional name="jitter"> - <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> - </when> - <when value="False"/> - </conditional> - </when> - <when value="False"/> - </conditional> - <conditional name="multi_panel"> - <param argument="multi_panel" type="select" label="Display keys in multiple panels" help="Also when `groupby is not provided"> - <option value="True">Yes</option> - <option value="False" selected="true">No</option> - </param> - <when value="True"> - <param argument="width" type="integer" min="0" value="" optional="true" label="Width of the figure" help=""/> - <param argument="height" type="integer" min="0" value="" optional="true" label="Height of the figure" help=""/> - </when> - <when value="False"/> - </conditional> - <param argument="scale" type="select" label="Method used to scale the width of each violin"> - <option value="area">area: each violin will have the same area</option> - <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> - <option value="width" selected="true">width: each violin will have the same width</option> + <xml name="conditional_stripplot"> + <conditional name="stripplot"> + <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> </param> - </section> + <when value="True"> + <conditional name="jitter"> + <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> + </param> + <when value="True"> + <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> + </when> + <when value="False"/> + </conditional> + </when> + <when value="False"/> + </conditional> + </xml> + <token name="@CMD_conditional_stripplot@"><![CDATA[ + stripplot=$method.violin_plot.stripplot.stripplot, +#if $method.violin_plot.stripplot.stripplot == "True" + jitter=$method.violin_plot.stripplot.jitter.jitter, + #if $method.violin_plot.stripplot.jitter.jitter == "True" + size=$method.violin_plot.stripplot.jitter.size, + #end if +#end if + ]]></token> + <xml name="param_scale"> + <param argument="scale" type="select" label="Method used to scale the width of each violin"> + <option value="area">area: each violin will have the same area</option> + <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> + <option value="width" selected="true">width: each violin will have the same width</option> + </param> </xml> <token name="@CMD_params_violin_plots@"><![CDATA[ stripplot=$method.violin_plot.stripplot.stripplot, @@ -777,7 +705,7 @@ #end if #end if multi_panel=$method.violin_plot.multi_panel.multi_panel, -#if $method.multi_panel.violin_plot.multi_panel == "True" and $method.violin_plot.multi_panel.width and $method.violin_plot.multi_panel.height +#if $method.multi_panel.violin_plot.multi_panel == "True" and str($method.violin_plot.multi_panel.width) != '' and str($method.violin_plot.multi_panel.height) != '' figsize=($method.violin_plot.multi_panel.width, $method.violin_plot.multi_panel.height) #end if scale='$method.violin_plot.scale', @@ -813,14 +741,12 @@ saturation=$method.seaborn_violinplot.saturation, ]]></token> <xml name="param_color"> - <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes`" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> + <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> </xml> <token name="@CMD_param_color@"><![CDATA[ #if str($method.color) != '' #set $color = ([x.strip() for x in str($method.color).split(',')]) color=$color, -#else - color=None, #end if ]]></token> <xml name="pl_groups"> @@ -830,8 +756,6 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if ]]></token> <xml name="pl_components"> @@ -847,8 +771,6 @@ #silent $components.append(str($s.axis1) + ',' + str($s.axis2)) #end for components=$components, -#else - components=None, #end if ]]> </token> @@ -877,7 +799,7 @@ </param> </xml> <xml name="param_legend_fontsize"> - <param argument="legend_fontsize" type="integer" min="0" value="1" label="Legend font size" help=""/> + <param argument="legend_fontsize" type="integer" optional="true" value="" label="Legend font size" help=""/> </xml> <xml name="param_legend_fontweight"> <param argument="legend_fontweight" type="select" label="Legend font weight" help=""> @@ -910,7 +832,7 @@ <param argument="left_margin" type="float" value="1" label="Width of the space left of each plotting panel" help=""/> </xml> <xml name="param_size"> - <param argument="size" type="float" value="1" label="Point size" help=""/> + <param argument="size" type="float" optional="true" value="" label="Point size" help=""/> </xml> <xml name="param_title"> <param argument="title" type="text" value="" optional="true" label="Title for panels" help="Titles must be separated by a comma"/> @@ -937,8 +859,8 @@ <option value="False" selected="true">No</option> </param> <when value="True"> - <param name="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> - <param name="edges_color" type="select" label="Color of edges"> + <param argument="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> + <param argument="edges_color" type="select" label="Color of edges"> <expand macro="matplotlib_color"/> </param> </when> @@ -956,7 +878,7 @@ ]]> </token> <xml name="param_arrows"> - <param name="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> + <param argument="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> </xml> <xml name="param_cmap"> <param argument="cmap" type="select" label="Colors to use for plotting categorical annotation groups" help=""> @@ -982,9 +904,13 @@ <token name="@CMD_pl_attribute_section@"><![CDATA[ projection='$method.plot.projection', legend_loc='$method.plot.legend_loc', + #if str($method.plot.legend_fontsize) != '' legend_fontsize=$method.plot.legend_fontsize, + #end if legend_fontweight='$method.plot.legend_fontweight', + #if str($method.plot.size) != '' size=$method.plot.size, + #end if palette='$method.plot.palette', frameon=$method.plot.frameon, ncols=$method.plot.ncols, @@ -995,24 +921,39 @@ #end if ]]> </token> + <xml name="options_layout"> + <option value="fa">fa: ForceAtlas2</option> + <option value="fr">fr: Fruchterman-Reingold</option> + <option value="grid_fr">grid_fr: Grid Fruchterman Reingold, faster than "fr"</option> + <option value="kk">kk: Kamadi Kawai’, slower than "fr"</option> + <option value="drl">drl: Distributed Recursive Layout, pretty fast</option> + <option value="rt">rt: Reingold Tilford tree layout</option> + <option value="eq_tree">eq_tree: Equally spaced tree</option> + </xml> + <xml name="param_layout"> + <param argument="layout" type="select" label="Plotting layout" help=""> + <expand macro="options_layout"/> + </param> + </xml> + <xml name="param_root"> + <param argument="root" type="text" value="" label="Comma-separated roots" help="If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is `None` or an empty list, the root vertices are automatically calculated based on topological sorting."/> + </xml> + <xml name="param_random_state"> + <param argument="random_state" type="integer" value="0" label="Random state" help="For layouts with random initialization like 'fr', change this to use different intial states for the optimization. If `None`, the initial state is not reproducible."/> + </xml> <xml name="inputs_paga"> <param argument="threshold" type="float" min="0" value="0.01" label="Threshold to draw edges" help="Do not draw edges for weights below this threshold. Set to 0 if you want all edges. Discarding low-connectivity edges helps in getting a much clearer picture of the graph."/> <expand macro="pl_groups"/> <param argument="color" type="text" value="" label="The node colors" help="Gene name or obs. annotation, and also plots the degree of the abstracted graph when passing 'degree_dashed', 'degree_solid'."/> <param argument="pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for drawing" help=""/> <param argument="labels" type="text" value="" label="Comma-separated node labels" help="If none is provided, this defaults to the group labels stored in the categorical for which `tl.paga` has been computed."/> - <param argument="layout" type="select" value="" label="Plotting layout" help=""> - <option value="fa">fa: ForceAtlas2</option> - <option value="fr">fr: Fruchterman-Reingold</option> - <option value="fr">rt: stands for Reingold Tilford</option> - <option value="fr">eq_tree: equally spaced tree</option> - </param> + <expand macro="param_layout"/> <param argument="init_pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for initializing the layout" help=""/> - <param argument="random_state" type="integer" value="0" label="Random state" help="For layouts with random initialization like 'fr', change this to use different intial states for the optimization. If `None`, the initial state is not reproducible."/> - <param argument="root" type="text" value="" label="Comma-separated roots" help="If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is `None` or an empty list, the root vertices are automatically calculated based on topological sorting."/> + <expand macro="param_random_state"/> + <expand macro="param_root"/> <param argument="transitions" type="text" value="" label="Key corresponding to the matrix storing the arrows" help="Key for `.uns['paga']`, e.g. 'transistions_confidence'"/> - <param argument="solid_edges" type="text" value="paga_connectivities" label="Key corresponding to the matrix storing the edges to be drawn solid black" help="Key for `.uns['paga']`"/> - <param argument="dashed_edges" type="text" value="" optional="true" label="Key corresponding to the matrix storing the edges to be drawn dashed grey" help="Key for `.uns['paga']`. If not set, no dashed edges are drawn."/> + <param argument="solid_edges" type="text" value="connectivities" label="Key corresponding to the matrix storing the edges to be drawn solid black" help="Key for uns/paga"/> + <param argument="dashed_edges" type="text" value="" optional="true" label="Key corresponding to the matrix storing the edges to be drawn dashed grey" help="Key for uns/paga. If not set, no dashed edges are drawn."/> <param argument="single_component" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Restrict to largest connected component?" help=""/> <param argument="fontsize" type="integer" min="0" value="1" label="Font size for node labels" help=""/> <param argument="node_size_scale" type="float" min="0" value="1.0" label="Size of the nodes" help=""/> @@ -1031,10 +972,11 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if - color='$method.color', +#if str($method.color) != '' + #set $color=([x.strip() for x in str($method.color).split(',')]) + color=$color, +#end if #if $method.pos pos=np.fromfile($method.pos, dtype=dt), #end if @@ -1081,4 +1023,10 @@ <xml name="param_swap_axes"> <param argument="swap_axes" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Swap axes?" help="By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby` categories (if any). By setting `swap_axes` then x are the `groupby` categories and y the `var_names`."/> </xml> + <xml name="gene_symbols"> + <param argument="gene_symbols" type="text" value="" optional="true" label="Key for field in `.var` that stores gene symbols"/> + </xml> + <xml name="n_genes"> + <param argument="n_genes" type="integer" min="0" value="20" label="Number of genes to show" help=""/> + </xml> </macros>
--- a/remove_confounders.xml Mon Mar 04 10:16:47 2019 -0500 +++ b/remove_confounders.xml Wed Oct 16 06:30:25 2019 -0400 @@ -1,18 +1,7 @@ -<tool id="scanpy_remove_confounders" name="Remove confounders with scanpy" version="@version@"> - <description></description> +<tool id="scanpy_remove_confounders" name="Remove confounders" version="@version@"> + <description>with scanpy</description> <macros> <import>macros.xml</import> - <xml name="score_genes_params"> - <param argument="n_bins" type="integer" value="25" label="Number of expression level bins for sampling" help=""/> - <param argument="random_state" type="integer" value="0" label="Random seed for sampling" help=""/> - <expand macro="param_use_raw"/> - </xml> - <token name="@CMD_score_genes_inputs@"><![CDATA[ - n_bins=$method.n_bins, - random_state=$method.random_state, - use_raw=$method.use_raw, - copy=False - ]]></token> </macros> <expand macro="requirements"/> <command detect_errors="exit_code"><![CDATA[ @@ -26,30 +15,49 @@ #if $method.method == "pp.regress_out" sc.pp.regress_out( adata=adata, - keys='$method.reg_keys', + #set $keys = [str(x.strip()) for x in str($method.keys).split(',')] + keys=$keys, copy=False) -#elif $method.method == "tl.score_genes" -sc.tl.score_genes( - adata=adata, - #set $gene_list = [str(x.strip()) for x in str($method.gene_list).split(',')] - gene_list=$gene_list, - ctrl_size=$method.ctrl_size, - score_name='$method.score_name', - #if $method.gene_pool - #set $gene_pool = [str(x.strip()) for x in $method.gene_pool.split(',')] - gene_pool=$gene_pool, + +#else if $method.method == "pp.mnn_correct" + #for i, filepath in enumerate($methods.extra_adata) +adata_$i = ad.read('$filepath') + #end for + +sc.pp.mnn_correct( + adata, + #for i, filepath in enumerate($methods.extra_adata) + adata_$i, + #end for + #if str($methods.var_subset) != '' + #set $var_subset=([x.strip() for x in str($method.var_subset).split(',')]) + var_subset=$var_subset, #end if - @CMD_score_genes_inputs@) -adata.obs.to_csv('$obs', sep='\t') -#elif $method.method == "tl.score_genes_cell_cycle" -sc.tl.score_genes_cell_cycle( - adata=adata, - #set $s_genes = [str(x.strip()) for x in $method.s_genes.split(',')] - s_genes=$s_genes, - #set $g2m_genes = [str(x.strip()) for x in $method.g2m_genes.split(',')] - g2m_genes=$g2m_genes, - @CMD_score_genes_inputs@) -adata.obs.to_csv('$obs', sep='\t') + batch_key='$method.batch_key', + index_unique='$method.index_unique' + #if str($methods.batch_categories) != '' + #set $batch_categories=([x.strip() for x in str($method.batch_categories).split(',')]) + batch_categories=$batch_categories, + #end if + k=$method.k, + sigma=$method.sigma, + cos_norm_in=$method.cos_norm_in, + cos_norm_out=$method.cos_norm_out, + svd_dim=$method.svd_dim, + var_adj=$method.var_adj, + compute_angle=$method.compute_angle, + mnn_order='$method.mnn_order', + svd_mode='$method.svd_mode', + do_concatenate=True, + save_raw=True, + n_jobs=\${GALAXY_SLOTS:-4}) + +#else if $method.method == "pp.combat" +sc.pp.combat( + adata, + key='$method.key', + inplace=True) + #end if @CMD_anndata_write_outputs@ @@ -60,111 +68,84 @@ <conditional name="method"> <param argument="method" type="select" label="Method used for plotting"> <option value="pp.regress_out">Regress out unwanted sources of variation, using `pp.regress_out`</option> - <!--<option value="pp.mnn_correct">, using `pp.mnn_correct`</option>!--> - <!--<option value="pp.dca">, using `pp.mnn_correct`</option>!--> - <!--<option value="pp.magic">, using `pp.magic`</option>!--> - <!--<option value="tl.sim">, using `tl.sim`</option>!--> - <!--<option value="pp.calculate_qc_metrics">, using `pp.calculate_qc_metrics`</option>!--> - <option value="tl.score_genes">Score a set of genes, using `tl.score_genes`</option> - <option value="tl.score_genes_cell_cycle">Score cell cycle genes, using `tl.score_genes_cell_cycle`</option> - <!--<option value="tl.cyclone">, using `tl.cyclone`</option>!--> - <!--<option value="tl.andbag">, using `tl.andbag`</option>!--> + <option value="pp.mnn_correct">Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct`</option> + <option value="pp.combat">Correct batch effects with ComBat function, using `pp.combat`</option> </param> <when value="pp.regress_out"> - <param argument="reg_keys" type="text" value="" label="Keys for observation annotation on which to regress on" help=""/> + <param argument="keys" type="text" value="" label="Keys for observation annotation on which to regress on" help="Keys separated by a comma"/> </when> - <when value="tl.score_genes"> - <param argument="gene_list" type="text" value="" label="The list of gene names used for score calculation" help="Genes separated by a comma"/> - <param argument="ctrl_size" type="integer" value="50" label="Number of reference genes to be sampled" - help="If `len(gene_list)` is not too low, you can set `ctrl_size=len(gene_list)`."/> - <param argument="gene_pool" type="text" value="" optional="true" label="Genes for sampling the reference set" - help="Default is all genes. Genes separated by a comma"/> - <expand macro="score_genes_params"/> - <param argument="score_name" type="text" value="score" label="Name of the field to be added in `.obs`" help=""/> + <when value="pp.mnn_correct"> + <param name="extra_adata" type="data" multiple="true" optional="true" format="h5ad" label="Extra annotated data matrix" help="They should have same number of variables."/> + <param argument="var_subset" type="text" value="" optional="true" label="The subset of vars to be used when performing MNN correction" help="List of comma-separated key from `.var_names`. If not set, all vars are used"/> + <param argument="batch_key" type="text" value="batch" label="Batch key for the concatenate"/> + <param name="index_unique" type="select" label="Separator to join the existing index names with the batch category" help="Leave it empty to keep existing indices"> + <option value="-">-</option> + <option value="_">_</option> + <option value=" "> </option> + <option value="/">/</option> + </param> + <param argument="batch_categories" type="text" value="" optional="true" label="Batch categories for the concatenate" help="List of comma-separated key"/> + <param argument="k" type="integer" value="20" label="Number of mutual nearest neighbors"/> + <param argument="sigma" type="float" value="1" label="The bandwidth of the Gaussian smoothing kernel used to compute the correction vectors"/> + <param argument="cos_norm_in" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Should cosine normalization be performed on the input data prior to calculating distances between cells?"/> + <param argument="cos_norm_out" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Should cosine normalization be performed prior to computing corrected expression values?"/> + <param argument="svd_dim" type="integer" value="" optional="true" label="Number of dimensions to use for summarizing biological substructure within each batch" help="If not set, biological components will not be removed from the correction vectors."/> + <param argument="var_adj" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Adjust variance of the correction vectors?" help="This step takes most computing time."/> + <param argument="compute_angle" type="boolean" truevalue="True" falsevalue="False" checked="false" label="compute the angle between each cell’s correction vector and the biological subspace of the reference batch?"/> + <param argument="mnn_order" type="text" value="" optional="true" label="The order in which batches are to be corrected" help="List of comma-separated key. If not set, datas are corrected sequentially"/> + <param name="svd_mode" type="select" label="SVD mode"> + <option value="svd">svd: SVD using a non-randomized SVD-via-ID algorithm</option> + <option value="rsvd" selected="true">rsvd: SVD using a randomized SVD-via-ID algorithm</option> + <option value="irlb">irlb: truncated SVD by implicitly restarted Lanczos bidiagonalization</option> + </param> </when> - <when value="tl.score_genes_cell_cycle"> - <param name="s_genes" type="text" value="" label="List of genes associated with S phase" help="Genes separated by a comma"/> - <param name="g2m_genes" type="text" value="" label="List of genes associated with G2M phase" help="Genes separated by a comma"/> - <expand macro="score_genes_params"/> + <when value="pp.combat"> + <param argument="key" type="text" value="batch" label="Key to a categorical annotation from adata.obs that will be used for batch effect removal"/> </when> </conditional> - <expand macro="anndata_output_format"/> </inputs> <outputs> <expand macro="anndata_outputs"/> - <data name="obs" format="tabular" label="${tool.name} on ${on_string}: Observations annotation"> - <filter>method['method'] == 'tl.score_genes' or method['method'] == 'tl.score_genes_cell_cycle'</filter> - </data> </outputs> <tests> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + <!-- test 1 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="pp.regress_out"/> + <param name="keys" value="cell_type"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.pp.regress_out"/> + <has_text_matching expression="keys=\['cell_type'\]"/> + </assert_stdout> + <output name="anndata_out" file="pp.regress_out.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <!--<test> + < test 2 > + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="pp.mnn_correct"/> <param name="reg_keys" value="cell_type"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> - <has_text_matching expression="sc.pp.regress_out"/> + <has_text_matching expression="sc.pp.mnn_correct"/> <has_text_matching expression="keys='cell_type'"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.regress_out.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - </test> + <output name="anndata_out" file="pp.mnn_correct.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test>--> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + <!-- test 2 --> + <param name="adata" value="blobs.h5ad" /> <conditional name="method"> - <param name="method" value="tl.score_genes"/> - <param name="gene_list" value="Gata2, Fog1"/> - <param name="ctrl_size" value="2"/> - <param name="n_bins" value="2"/> - <param name="random_state" value="2"/> - <param name="use_raw" value="False"/> - <param name="score_name" value="score"/> + <param name="method" value="pp.combat"/> + <param name="key" value="blobs"/> </conditional> - <param name="anndata_output_format" value="h5ad"/> <assert_stdout> - <has_text_matching expression="sc.tl.score_genes" /> - <has_text_matching expression="gene_list=\['Gata2', 'Fog1'\]" /> - <has_text_matching expression="ctrl_size=2" /> - <has_text_matching expression="score_name='score'" /> - <has_text_matching expression="n_bins=2" /> - <has_text_matching expression="random_state=2" /> - <has_text_matching expression="use_raw=False" /> - <has_text_matching expression="copy=False" /> + <has_text_matching expression="sc.pp.combat"/> + <has_text_matching expression="key='blobs'"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.score_genes.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="obs" file="tl.score_genes.krumsiek11.obs.tabular" ftype="tabular" compare="sim_size"/> - </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="tl.score_genes_cell_cycle"/> - <param name="s_genes" value="Gata2, Fog1, EgrNab"/> - <param name="g2m_genes" value="Gata2, Fog1, EgrNab"/> - <param name="n_bins" value="2"/> - <param name="random_state" value="1"/> - <param name="use_raw" value="False"/> - </conditional> - <param name="anndata_output_format" value="h5ad"/> - <assert_stdout> - <has_text_matching expression="sc.tl.score_genes_cell_cycle"/> - <has_text_matching expression="s_genes=\['Gata2', 'Fog1', 'EgrNab'\]"/> - <has_text_matching expression="g2m_genes=\['Gata2', 'Fog1', 'EgrNab'\]"/> - <has_text_matching expression="n_bins=2"/> - <has_text_matching expression="random_state=1"/> - <has_text_matching expression="use_raw=False"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="tl.score_genes_cell_cycle.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="obs" file="tl.score_genes_cell_cycle.krumsiek11.obs.tabular" ftype="tabular" compare="sim_size"/> + <output name="anndata_out" file="pp.combat.blobs.h5ad" ftype="h5ad" compare="sim_size"/> </test> </tests> <help><![CDATA[ @@ -175,30 +156,29 @@ inspired by Seurat's `regressOut` function in R. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.regress_out.html#scanpy.api.pp.regress_out>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.regress_out.html>`__ -Score a set of genes, using `tl.score_genes` -============================================ +Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct` +================================================================================== -The score is the average expression of a set of genes subtracted with the -average expression of a reference set of genes. The reference set is -randomly sampled from the `gene_pool` for each binned expression value. +This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the +original order containing corrected expression values or a concatenated matrix or AnnData object. -This reproduces the approach in Seurat (Satija et al, 2015) and has been implemented -for Scanpy by Davide Cittaro. +Be reminded that it is not advised to use the corrected data matrices for differential expression testing. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.score_genes.html#scanpy.api.tl.score_genes>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.api.pp.mnn_correct.html>`__ + -Score cell cycle genes, using `tl.score_genes_cell_cycle` -========================================================= +Correct batch effects with ComBat function (`pp.combat`) +======================================================== -Given two lists of genes associated to S phase and G2M phase, calculates -scores and assigns a cell cycle phase (G1, S or G2M). See -`score_genes` for more explanation. +Corrects for batch effects by fitting linear models, gains statistical power via an EB framework where information is borrowed across genes. This uses the implementation of ComBat More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.score_genes_cell_cycle.html#scanpy.api.tl.score_genes_cell_cycle>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.combat.html>`__ + + ]]></help> <expand macro="citations"/> </tool>
--- a/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ - cell_subset number_per_cell -0 True 9 -1 True 9 -2 True 9 -3 True 8 -4 True 8 -5 True 8 -6 True 8 -7 True 7 -8 True 8 -9 True 8 -10 True 7 -11 True 7 -12 True 7 -13 True 7 -14 True 8 -15 True 10 -16 True 10 -17 True 10 -18 True 11 -19 True 11 -20 True 11 -21 True 11 -22 True 11 -23 True 11 -24 True 11 -25 True 11 -26 True 11 -27 True 11 -28 True 11 -29 True 11 -30 True 11 -31 True 11 -32 True 11 -33 True 11 -34 True 11 -35 True 11 -36 True 11 -37 True 11 -38 True 11 -39 True 11 -40 True 11 -41 True 11 -42 True 11 -43 True 11 -44 True 11 -45 True 11 -46 True 11 -47 True 11 -48 True 10 -49 True 10 -50 True 10 -51 True 10 -52 True 10 -53 True 10 -54 True 10 -55 True 10 -56 True 11 -57 True 11 -58 True 11 -59 True 10 -60 True 10 -61 True 11 -62 True 10 -63 True 11 -64 True 10 -65 True 10 -66 True 11 -67 True 11 -68 True 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--- a/test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,12 +0,0 @@ -index n_counts -Gata2 163.95355 -Gata1 203.95117 -Fog1 83.94181 -EKLF 70.69286 -Fli1 57.56072 -SCL 202.67444 -Cebpa 469.87094 -Pu.1 250.78569 -cJun 188.10158 -EgrNab 164.99693 -Gfi1 159.99155
--- a/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,222 +0,0 @@ - gene_subset number_per_gene -0 True 34 -1 True 123 -2 True 281 -3 True 54 -4 True 253 -5 True 63 -6 True 9 -7 True 266 -8 True 101 -9 True 233 -10 True 267 -11 True 285 -12 True 332 -13 True 197 -14 True 158 -15 True 64 -16 True 285 -17 True 229 -18 True 43 -19 True 199 -20 True 271 -21 True 318 -22 True 132 -23 True 83 -24 True 88 -25 True 87 -26 True 71 -27 True 258 -28 True 58 -29 True 348 -30 True 280 -31 True 150 -32 True 121 -33 True 237 -34 True 29 -35 True 220 -36 True 103 -37 True 87 -38 True 115 -39 True 100 -40 True 139 -41 True 23 -42 True 162 -43 True 76 -44 True 180 -45 True 51 -46 True 244 -47 True 132 -48 True 244 -49 True 82 -50 True 172 -51 True 27 -52 True 100 -53 True 327 -54 True 277 -55 True 282 -56 True 245 -57 True 21 -58 True 52 -59 True 19 -60 True 227 -61 True 288 -62 True 274 -63 True 301 -64 True 316 -65 True 314 -66 True 271 -67 True 270 -68 True 283 -69 True 245 -70 True 263 -71 True 312 -72 True 285 -73 True 228 -74 True 170 -75 True 11 -76 True 228 -77 True 192 -78 True 140 -79 True 15 -80 True 22 -81 True 10 -82 True 233 -83 True 129 -84 True 12 -85 True 297 -86 True 295 -87 True 127 -88 True 208 -89 True 281 -90 True 265 -91 True 254 -92 True 122 -93 True 76 -94 True 237 -95 True 74 -96 True 65 -97 True 45 -98 True 90 -99 True 147 -100 True 189 -101 True 170 -102 True 207 -103 True 14 -104 True 307 -105 True 267 -106 True 111 -107 True 94 -108 True 306 -109 True 126 -110 True 269 -111 True 116 -112 True 140 -113 True 260 -114 True 201 -115 True 198 -116 True 155 -117 True 256 -118 True 214 -119 True 70 -120 True 304 -121 True 336 -122 True 201 -123 True 305 -124 True 301 -125 True 301 -126 True 338 -127 True 81 -128 True 256 -129 True 277 -130 True 237 -131 True 173 -132 True 228 -133 True 64 -134 True 52 -135 True 34 -136 True 333 -137 True 285 -138 True 132 -139 True 32 -140 True 275 -141 True 31 -142 True 244 -143 True 15 -144 True 54 -145 True 289 -146 True 186 -147 True 283 -148 True 333 -149 True 53 -150 True 26 -151 True 173 -152 True 19 -153 True 109 -154 True 138 -155 True 264 -156 True 293 -157 True 225 -158 True 150 -159 True 62 -160 True 350 -161 True 13 -162 True 341 -163 True 223 -164 True 177 -165 True 15 -166 True 202 -167 True 101 -168 True 203 -169 True 271 -170 True 305 -171 True 45 -172 True 322 -173 True 164 -174 True 213 -175 True 55 -176 True 143 -177 True 112 -178 True 266 -179 True 168 -180 True 9 -181 True 300 -182 True 249 -183 True 101 -184 True 55 -185 True 312 -186 True 181 -187 True 256 -188 True 27 -189 True 242 -190 True 210 -191 True 12 -192 True 203 -193 True 41 -194 True 205 -195 True 315 -196 True 94 -197 True 262 -198 True 316 -199 True 13 -200 True 94 -201 True 204 -202 True 245 -203 True 11 -204 True 238 -205 True 301 -206 True 219 -207 True 106 -208 True 253 -209 True 134 -210 True 262 -211 True 222 -212 True 82 -213 True 153 -214 True 122 -215 True 211 -216 True 49 -217 True 211 -218 True 176 -219 True 329 -220 True 8
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,12 +0,0 @@ - gene_subset means dispersions dispersions_norm -0 False 0.22807331 -1.513815 -1 False 0.27662647 -0.6374868 -2 False 0.12324284 -1.1931922 -3 True 0.10477218 -0.8270577 0.67448974 -4 True 0.08612139 -0.880823 0.67448974 -5 False 0.2751125 -0.6042374 -6 False 0.55053085 -1.5924454 -7 False 0.3306357 -0.91260546 -8 False 0.25766766 -0.86990273 -9 False 0.22937028 -0.7354343 -10 False 0.223133 -0.96748924
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -index means dispersions dispersions_norm -Fog1 0.12324284 -1.1931922 1.0 -EKLF 0.10477218 -0.8270577 0.70710677 -SCL 0.2751125 -0.6042374 0.707108 -Cebpa 0.55053085 -1.5924454 1.0 -Pu.1 0.3306357 -0.91260546 1.0 -cJun 0.25766766 -0.86990273 1.0 -EgrNab 0.22937028 -0.7354343 0.7071069 -Gfi1 0.223133 -0.96748924 1.0
Binary file test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 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Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -index paul15_clusters dpt_groups dpt_order dpt_order_indices -578 13Baso 2 53 27 -2242 3Ery 1 30 46 -2690 10GMP 2 66 45 -70 5Ery 1 32 65 -758 15Mo 2 67 8 -465 16Neu 2 68 80 -245 16Neu 2 69 87 -2172 10GMP 2 70 90 -2680 10GMP 0 4 36 -1790 7MEP 2 71 59 -855 11DC 2 72 82 -2721 10GMP 2 73 30 -104 2Ery 1 38 62 -1106 2Ery 1 40 32 -2367 15Mo 3 93 35 -124 2Ery 1 41 37 -2477 8Mk 2 74 31 -1968 2Ery 1 42 78 -563 1Ery 1 43 28 -276 2Ery 1 44 56 -192 16Neu 2 75 42 -2409 2Ery 1 45 44 -2054 15Mo 3 95 75 -720 8Mk 2 76 48 -2225 14Mo 3 97 98 -878 6Ery 1 29 54 -156 7MEP 2 77 79 -1244 8Mk 0 0 40 -10 2Ery 1 18 83 -1108 6Ery 2 65 25 -353 5Ery 1 11 1 -182 5Ery 1 16 97 -2053 3Ery 1 13 3 -2291 16Neu 3 92 96 -2056 10GMP 2 79 95 -1047 2Ery 1 14 94 -1947 14Mo 0 8 92 -1390 3Ery 1 15 60 -2317 14Mo 2 90 12 -2348 11DC 2 82 69 -953 5Ery 1 27 13 -628 9GMP 2 83 15 -2691 5Ery 1 20 17 -1499 16Neu 3 96 18 -1083 2Ery 1 21 19 -831 14Mo 0 2 21 -15 7MEP 0 1 86 -2005 7MEP 2 87 66 -1662 3Ery 1 23 84 -2457 7MEP 2 64 89 -757 7MEP 2 81 70 -1642 14Mo 2 91 68 -2520 10GMP 2 89 67 -1393 7MEP 2 88 0 -2170 6Ery 1 25 73 -988 14Mo 2 86 76 -1338 2Ery 1 19 77 -2189 16Neu 2 85 81 -446 13Baso 2 84 85 -2276 14Mo 0 9 88 -317 2Ery 1 37 91 -1540 16Neu 3 99 93 -2164 4Ery 1 12 72 -227 15Mo 2 78 64 -906 12Baso 2 63 49 -716 15Mo 0 3 29 -912 14Mo 1 47 2 -2688 11DC 2 52 4 -1678 7MEP 2 51 5 -1063 6Ery 1 39 6 -1041 5Ery 1 50 7 -2279 15Mo 3 98 9 -558 13Baso 2 62 10 -2196 14Mo 2 54 11 -1270 13Baso 3 94 16 -2259 3Ery 1 22 20 -2410 13Baso 2 55 23 -886 7MEP 2 56 26 -2072 13Baso 1 17 63 -443 5Ery 1 26 34 -910 13Baso 0 5 99 -2608 15Mo 2 57 50 -2645 1Ery 1 10 39 -616 6Ery 1 28 41 -1866 2Ery 1 48 58 -923 7MEP 2 58 57 -1716 4Ery 1 46 55 -2476 11DC 0 6 47 -1872 10GMP 2 59 53 -1009 4Ery 1 49 52 -1680 6Ery 0 7 38 -1490 14Mo 2 60 51 -1454 2Ery 1 36 33 -2580 9GMP 2 61 14 -958 1Ery 1 35 74 -2626 2Ery 1 34 22 -1677 3Ery 1 33 43 -982 4Ery 1 31 24 -202 2Ery 1 24 71 -891 10GMP 2 80 61
Binary file test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type score -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor 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--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:16:47 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type S_score G2M_score phase -0 progenitor 0.2681 0.20055 S -1 progenitor 0.24346666 0.15855001 S -2 progenitor 0.2276 0.13482499 S -3 progenitor 0.21043333 0.12637499 S -4 progenitor 0.19113334 0.1272 S -5 progenitor 0.17531666 0.13072497 S -6 progenitor 0.16073334 0.13242501 S -7 progenitor 0.15353334 0.13672501 S -8 progenitor 0.14314999 0.1399 S -9 progenitor 0.1337 0.14515 G2M -10 progenitor 0.12695001 0.15165001 G2M -11 progenitor 0.11726667 0.16077498 G2M -12 progenitor 0.11081667 0.16735 G2M -13 progenitor 0.104849994 0.17429999 G2M -14 progenitor 0.09816667 0.18152499 G2M -15 progenitor 0.095350005 0.186625 G2M -16 progenitor 0.09528333 0.19447501 G2M -17 progenitor 0.09463333 0.199675 G2M -18 progenitor 0.0947 0.205275 G2M -19 progenitor 0.0947 0.20802501 G2M -20 progenitor 0.097733326 0.21100001 G2M -21 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