# HG changeset patch # User iuc # Date 1571221737 14400 # Node ID e4c0f5ee8e17adbde9167563840dae70b31ab258 # Parent 397d2c97af057eab99f17a86ff9feea3a06aaa74 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5" diff -r 397d2c97af05 -r e4c0f5ee8e17 README.md --- a/README.md Mon Mar 04 10:14:25 2019 -0500 +++ b/README.md Wed Oct 16 06:28:57 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 diff -r 397d2c97af05 -r e4c0f5ee8e17 README.rst --- a/README.rst Mon Mar 04 10:14:25 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 diff -r 397d2c97af05 -r e4c0f5ee8e17 macros.xml --- a/macros.xml Mon Mar 04 10:14:25 2019 -0500 +++ b/macros.xml Wed Oct 16 06:28:57 2019 -0400 @@ -1,10 +1,12 @@ - 1.4 + 1.4.4 scanpy loompy + h5py + leidenalg @@ -14,102 +16,33 @@ - + - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - - anndata_output_format == 'h5ad' - - - anndata_output_format == 'loom' - - - - - modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad' - - - modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom' - + - - @@ -423,7 +356,7 @@ - + @@ -473,7 +406,7 @@ @@ -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' ]]> - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + -
+ + + + + + + + + + + + + + +
+ + + + + + + - + @@ -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 ]]> @@ -847,8 +771,6 @@ #silent $components.append(str($s.axis1) + ',' + str($s.axis2)) #end for components=$components, -#else - components=None, #end if ]]> @@ -877,7 +799,7 @@ - + @@ -910,7 +832,7 @@ - + @@ -937,8 +859,8 @@ - - + + @@ -956,7 +878,7 @@ ]]> - + @@ -982,9 +904,13 @@ + + + + + + + + + + + + + + + + + + + + - - - - - - + - - + + - - + + @@ -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 @@ + + + + + +
diff -r 397d2c97af05 -r e4c0f5ee8e17 plot.xml --- a/plot.xml Mon Mar 04 10:14:25 2019 -0500 +++ b/plot.xml Wed Oct 16 06:28:57 2019 -0400 @@ -1,5 +1,5 @@ - - + + with scanpy macros.xml @@ -25,14 +25,14 @@ #if $method.type.type == 'xy' x='$method.type.x', y='$method.type.y', - layers=('$method.type.layer_x', '$method.type.layer_y', '$method.type.layer_color'), + #if str('$method.type.layers.use_layers') == 'true' + layers=('$method.type.layers.layer_x', '$method.type.layers.layer_y', '$method.type.layers.layer_color'), + #end if #else basis='$method.type.basis', #if str($method.type.color) != '' #set $color = ([x.strip() for x in str($method.type.color).split(',')]) color=$color, - #else - color=None, #end if #end if use_raw=$method.use_raw, @@ -51,14 +51,16 @@ #end if size=$method.plot.size) -#elif $method.method == 'pl.heatmap' +#else if $method.method == 'pl.heatmap' sc.pl.heatmap( @CMD_param_plot_inputs@ @CMD_params_plots@ swap_axes=$method.swap_axes, show_gene_labels=$method.show_gene_labels, cmap='$method.matplotlib_pyplot_imshow.cmap', + #if str($method.matplotlib_pyplot_imshow.interpolation) != 'None' interpolation='$method.matplotlib_pyplot_imshow.interpolation', + #end if #if $method.matplotlib_pyplot_imshow.alpha alpha=$method.matplotlib_pyplot_imshow.alpha, #end if @@ -70,7 +72,7 @@ #end if origin='$method.matplotlib_pyplot_imshow.origin') -#elif $method.method == 'pl.dotplot' +#else if $method.method == 'pl.dotplot' sc.pl.dotplot( @CMD_param_plot_inputs@ @CMD_params_plots@ @@ -83,14 +85,14 @@ #end if @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.violin' +#else if $method.method == 'pl.violin' sc.pl.violin( @CMD_param_plot_inputs@ #if $method.key_variables.type == "var_names" #set $key_list = adata.var_names - #elif $method.key_variables.type == "obs" + #else if $method.key_variables.type == "obs" #set $key_list = adata.obs.select_dtypes(exclude=['category']).columns - #elif $method.key_variables.type == "custom" + #else if $method.key_variables.type == "custom" #set $key_list=([x.strip() for x in str($method.key_variables.keys).split(',')]) #end if keys=$key_list, @@ -99,14 +101,11 @@ #end if log=$method.log, use_raw=$method.use_raw, - sstripplot=$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 + @CMD_conditional_stripplot@ + multi_panel=$method.violin_plot.multi_panel.multi_panel, + #if $method.violin_plot.multi_panel.multi_panel == "True" and str($method.violin_plot.multi_panel.width) != '' and str($method.violin_plot.multi_panel.height) != '' + figsize=($method.violin_plot.multi_panel.width, $method.violin_plot.multi_panel.height), #end if - multi_panel=$method.violin_plot.multi_panel.multi_panel, scale='$method.violin_plot.scale', #if $method.xlabel xlabel='$method.xlabel', @@ -116,7 +115,20 @@ #end if @CMD_params_seaborn_violinplot@) -#elif $method.method == 'pl.matrixplot' +#else if $method.method == 'pl.stacked_violin' +sc.pl.stacked_violin( + @CMD_param_plot_inputs@ + @CMD_params_plots@ + swap_axes=$method.swap_axes, + @CMD_conditional_stripplot@ + scale='$method.violin_plot.scale', + row_palette='$method.row_palette', + #if str($method.standard_scale) != 'None' + standard_scale='$method.standard_scale', + #end if + @CMD_params_seaborn_violinplot@) + +#else if $method.method == 'pl.matrixplot' sc.pl.matrixplot( @CMD_param_plot_inputs@ @CMD_params_plots@ @@ -134,7 +146,7 @@ #end if snap=$method.matplotlib_pyplot_pcolor.snap) -#elif $method.method == 'pl.clustermap' +#else if $method.method == 'pl.clustermap' sc.pl.clustermap( #if str($method.use_raw) == 'True' adata=adata.X, @@ -142,24 +154,22 @@ adata=adata, #end if show=False, + #if str($method.obs_keys) != '' obs_keys='$method.obs_keys', + #end if use_raw=$method.use_raw, method='$method.seaborn_clustermap.method', metric='$method.seaborn_clustermap.metric', - #if str($method.seaborn_clustermap.z_score) == 'None' - z_score=None, - #else + #if str($method.seaborn_clustermap.z_score) != 'None' z_score=$method.seaborn_clustermap.z_score, #end if - #if str($method.seaborn_clustermap.standard_scale) == 'None' - standard_scale=None, - #else + #if str($method.seaborn_clustermap.standard_scale) != 'None' standard_scale=$method.seaborn_clustermap.standard_scale, #end if col_cluster=$method.seaborn_clustermap.col_cluster, row_cluster=$method.seaborn_clustermap.row_cluster).savefig(fname="output.$format") -#elif $method.method == 'pl.highest_expr_genes' +#else if $method.method == 'pl.highest_expr_genes' sc.pl.highest_expr_genes( @CMD_param_plot_inputs@ n_top=$method.n_top, @@ -174,7 +184,16 @@ #end if saturation=$method.setseaborn_boxplot.saturation) -#elif $method.method == 'pl.pca' +#else if $method.method == 'pl.highly_variable_genes' +sc.pl.highly_variable_genes( + adata_or_result=adata, + log=$method.log, + save='.$format', + show=False, + highly_variable_genes=$method.highly_variable_genes + ) + +#else if $method.method == 'pl.pca' sc.pl.pca( @CMD_param_plot_inputs@ @CMD_param_color@ @@ -185,19 +204,19 @@ @CMD_pl_attribute_section@ @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.pca_loadings' +#else if $method.method == 'pl.pca_loadings' sc.pl.pca_loadings( @CMD_param_plot_inputs@ #set $components = ([int(x.strip()) for x in str($method.components).split(',')]) components=$components) -#elif $method.method == 'pl.pca_variance_ratio' +#else if $method.method == 'pl.pca_variance_ratio' sc.pl.pca_variance_ratio( @CMD_param_plot_inputs@ n_pcs=$method.n_pcs, log=$method.log) -#elif $method.method == 'pl.pca_overview' +#else if $method.method == 'pl.pca_overview' sc.pl.pca_overview( @CMD_param_plot_inputs@ @CMD_param_color@ @@ -208,7 +227,7 @@ @CMD_pl_attribute_section@ @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.tsne' +#else if $method.method == 'pl.tsne' sc.pl.tsne( @CMD_param_plot_inputs@ @CMD_param_color@ @@ -221,7 +240,7 @@ @CMD_pl_attribute_section@ @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.umap' +#else if $method.method == 'pl.umap' sc.pl.umap( @CMD_param_plot_inputs@ @CMD_param_color@ @@ -234,7 +253,7 @@ @CMD_pl_attribute_section@ @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.diffmap' +#else if $method.method == 'pl.diffmap' sc.pl.diffmap( @CMD_param_plot_inputs@ @CMD_param_color@ @@ -245,12 +264,35 @@ @CMD_pl_attribute_section@ @CMD_params_matplotlib_pyplot_scatter@) -#elif $method.method == 'pl.dpt_groups_pseudotime' +#else if $method.method == 'pl.draw_graph' +sc.pl.draw_graph( + @CMD_param_plot_inputs@ + @CMD_param_color@ + #if str($method.gene_symbols) != '' + gene_symbols='$method.gene_symbols', + #end if + use_raw=$method.use_raw, + #if str($method.layout) != 'None' + layout='$method.layout', + #end if + edges=$method.edges.edges, + #if str($method.edges.edges) == 'True' + edges_width=$method.edges.edges_width, + edges_color='$method.edges.edges_color', + #end if + arrows=$method.arrows, + sort_order=$method.sort_order, + @CMD_params_pl_groups@ + @CMD_params_pl_components@ + @CMD_pl_attribute_section@ + @CMD_params_matplotlib_pyplot_scatter@) + +#else if $method.method == 'pl.dpt_groups_pseudotime' sc.pl.dpt_groups_pseudotime( @CMD_param_plot_inputs@ color_map='$method.color_map') -#elif $method.method == 'pl.dpt_timeseries' +#else if $method.method == 'pl.dpt_timeseries' sc.pl.dpt_timeseries( @CMD_param_plot_inputs@ #if $method.heatmap.as_heatmap == "True" @@ -258,10 +300,73 @@ #end if as_heatmap=$method.heatmap.as_heatmap) -#elif $method.method == 'pl.paga' +#else if $method.method == 'pl.paga' sc.pl.paga( @CMD_param_plot_inputs@ @CMD_params_pl_paga@) + +#else if $method.method == 'pl.paga_compare' +sc.pl.paga_compare( + @CMD_param_plot_inputs@ + @CMD_params_pl_paga@) + +#else if $method.method == 'pl.paga_path' +sc.pl.paga_path( + @CMD_param_plot_inputs@ + #set $nodes = ([int(x.strip()) for x in str($method.nodes).split(',')]) + nodes=$nodes, + #set $keys=([x.strip() for x in str($method.keys).split(',')]) + keys=$keys, + use_raw=$method.use_raw, + #if str($method.annotations) != '' + #set $annotations=([x.strip() for x in str($method.annotations).split(',')]) + annotations=$annotations, + #end if + color_map='$method.color_map', + n_avg=$method.n_avg, + #if str($method.annotations) != '' + groups_key='$method.groups_key', + #end if + as_heatmap=$method.as_heatmap, + show_node_names=$method.show_node_names, + show_colorbar=$method.show_colorbar, + show_yticks=$method.show_yticks, + normalize_to_zero_one=$method.normalize_to_zero_one) + +#else if $method.method == 'pl.rank_genes_groups' +sc.pl.rank_genes_groups( + @CMD_param_plot_inputs@ + @CMD_params_pl_groups@ + n_genes=$method.n_genes, + #if str($method.gene_symbols) != '' + gene_symbols='$method.gene_symbols', + #end if + fontsize=$method.fontsize, + ncols=$method.ncols, + sharey=$method.sharey) + +#else if $method.method == 'pl.rank_genes_groups_violin' +sc.pl.rank_genes_groups_violin( + @CMD_param_plot_inputs@ + @CMD_params_pl_groups@ + #if $method.genes.select == 'n_genes' + n_genes=$method.genes.n_genes, + #else + #set $gene_names = ([x.strip() for x in str($method.genes.gene_names).split(',')]) + gene_names=$gene_names, + #end if + gene_symbols='$method.gene_symbols', + use_raw=$method.use_raw, + split=$method.split, + strip=$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 + scale='$method.violin_plot.scale') + #end if ]]> @@ -274,13 +379,13 @@ - + - + @@ -289,18 +394,18 @@ - + - - - + + + - + @@ -317,9 +422,18 @@ - - - + + + + + + + + + + + + @@ -355,7 +469,7 @@
- + @@ -396,7 +510,7 @@ - + @@ -407,11 +521,43 @@ - +
+ + + + + + + + + + + + + +
+ + + +
+ + +
+ + + + + + + + + + +
@@ -427,7 +573,7 @@
- +
@@ -456,6 +602,10 @@
+ + + + @@ -518,6 +668,20 @@ + + + + + + + + + + + + + + @@ -536,35 +700,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
- - format == 'png' + + format == 'png' and method['method'] != 'pl.rank_genes_groups_violin' + + + + format == 'png' and method['method'] == 'pl.rank_genes_groups_violin' + + + format == 'pdf' and method['method'] != pl.rank_genes_groups_violin - - format == 'pdf' + + + format == 'pdf' and method['method'] == 'pl.rank_genes_groups_violin' + + + format == 'svg' and method['method'] != pl.rank_genes_groups_violin - - format == 'svg' - + + + format == 'svg' and method['method'] == 'pl.rank_genes_groups_violin' + - - - - - + + - - + + - +
@@ -579,12 +800,10 @@ - - - + + + - - @@ -594,14 +813,11 @@ - + - - - - - + + @@ -609,9 +825,9 @@ - - - + + + @@ -630,11 +846,8 @@ - - - @@ -648,11 +861,8 @@ - - - - - + + @@ -673,7 +883,7 @@
- +
@@ -685,23 +895,17 @@ - - -
- - - - - + + @@ -714,10 +918,6 @@ - - - - @@ -728,6 +928,9 @@ + + + @@ -743,7 +946,7 @@ - + @@ -755,11 +958,8 @@ - - - - - + + @@ -795,10 +995,11 @@ - + + @@ -810,11 +1011,65 @@ - - - - + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + +
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + +
+ + + @@ -844,8 +1099,6 @@ - - @@ -854,19 +1107,15 @@ - - - - - + + -
- + @@ -876,23 +1125,17 @@ - - - - + - - - - - + + @@ -913,11 +1156,25 @@ - - - - + + + + + + + + + + + + + + + + + + @@ -955,7 +1212,6 @@ - @@ -973,11 +1229,8 @@ - - - - - + + @@ -987,14 +1240,11 @@ - + - - - - - + + @@ -1006,22 +1256,18 @@ - + - - - - - + + -
- + @@ -1032,22 +1278,12 @@
-
- - - - - -
- - - - + @@ -1057,19 +1293,12 @@ - - - -
- - - - - + + @@ -1098,13 +1327,10 @@ - - - @@ -1121,11 +1347,8 @@ - - - - - + + @@ -1163,8 +1386,6 @@ - - @@ -1181,11 +1402,8 @@ - - - - - + + @@ -1214,8 +1432,6 @@ - - @@ -1231,12 +1447,59 @@ + + + + + + + + + + + + + + +
+ + + + + + + + +
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + +
- - - - - + + @@ -1271,10 +1531,7 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + + +
`__ + +Generic: Stacked violin plots (`pl.stacked_violin`) +=================================================== + +Makes a compact image composed of individual violin plots (from `seaborn.violinplot`) +stacked on top of each other. Useful to visualize gene expression per cluster. + +More details on the `scanpy documentation `__ Generic: Heatmap of the mean expression values per cluster (`pl.matrixplot`) @@ -1353,7 +1684,7 @@ category. More details on the `scanpy documentation -`__ +`__ Generic: Hierarchically-clustered heatmap (`pl.clustermap`) =========================================================== @@ -1371,7 +1702,7 @@ Column indices, use: clustergrid.dendrogram_col.reordered_ind More details on the `scanpy documentation -`__ +`__ Preprocessing: Plot the fraction of counts assigned to each gene over all cells (`pl.highest_expr_genes`) ========================================================================================================= @@ -1380,30 +1711,36 @@ a cell. The `n_top` genes with the highest mean fraction over all cells are plotted as boxplots. -This plot is similar to the `scater` package function `plotHighestExprs(type= "highest-expression")`, see `here -`__. --- Davis McCarthy and Aaron Lun +This plot is similar to the `scater` package function `plotHighestExprs(type= "highest-expression")` More details on the `scanpy documentation -`__ +`__ + +Preprocessing: Plot dispersions versus means for genes (`pl.highly_variable_genes`) +=================================================================================== + +It produces Supp. Fig. 5c of Zheng et al. (2017) and MeanVarPlot() of Seurat. + +More details on the `scanpy documentation +`__ PCA: Scatter plot in PCA coordinates (`pl.pca`) =============================================== More details on the `scanpy documentation -`__ +`__ PCA: Rank genes according to contributions to PCs (`pl.pca_loadings`) ===================================================================== More details on the `scanpy documentation -`__ +`__ PCA: Plot the variance ratio (`pl.pca_variance_ratio`) ====================================================== More details on the `scanpy documentation -`__ +`__ PCA: Plot PCA results (`pl.pca_overview`) ========================================= @@ -1412,37 +1749,37 @@ if you want to change the default settings. More details on the `scanpy documentation -`__ +`__ Embedding: Scatter plot in tSNE basis (`pl.tsne`) ================================================= More details on the `scanpy documentation -`__ +`__ Embeddings: Scatter plot in UMAP basis (`pl.umap`) ================================================== More details on the `scanpy documentation -`__ +`__ Embeddings: Scatter plot in Diffusion Map basis (`pl.diffmap`) ============================================================== More details on the `scanpy documentation -`__ +`__ Branching trajectories and pseudotime, clustering: Plot groups and pseudotime (`pl.dpt_groups_pseudotime`) =========================================================================================================== More details on the `scanpy documentation -`__ +`__ Branching trajectories and pseudotime, clustering: Heatmap of pseudotime series (`pl.dpt_timeseries`) ===================================================================================================== More details on the `scanpy documentation -`__ +`__ Branching trajectories and pseudotime, clustering: Plot the abstracted graph through thresholding low-connectivity edges (`pl.paga`) @@ -1455,9 +1792,32 @@ `maxiter` parameter by 1 if the layout is flipped. More details on the `scanpy documentation -`__ +`__ +Branching trajectories and pseudotime, clustering: Scatter and PAGA graph side-by-side (`pl.paga_compare`) +========================================================================================================== + +More details on the `scanpy documentation +`__ + +Branching trajectories and pseudotime, clustering: Gene expression and annotation changes along paths (`pl.paga_path`) +====================================================================================================================== + +More details on the `scanpy documentation +`__ + +Marker genes: Plot ranking of genes using dotplot plot (`pl.rank_genes_groups`) +=============================================================================== + +More details on the `scanpy documentation +`__ + +Marker genes: Plot ranking of genes for all tested comparisons (`pl.rank_genes_groups_violin`) +============================================================================================== + +More details on the `scanpy documentation +`__ ]]> diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/blobs.h5ad Binary file test-data/blobs.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pl.clustermap.krumsiek11.png Binary file test-data/pl.clustermap.krumsiek11.png has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pl.dotplot.krumsiek11.png Binary file test-data/pl.dotplot.krumsiek11.png has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pl.draw_graph.png Binary file test-data/pl.draw_graph.png has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pl.highly_variable_genes.seurat.blobs.png Binary file test-data/pl.highly_variable_genes.seurat.blobs.png has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pl.pca_overview.pp.pca.krumsiek11.png Binary file test-data/pl.pca_overview.pp.pca.krumsiek11.png has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 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397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_cells.krumsiek11-min_counts.h5ad Binary file test-data/pp.filter_cells.krumsiek11-min_counts.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular --- a/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular Mon Mar 04 10:14:25 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 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-445 True 11 -446 True 10 -447 True 10 -448 True 10 -449 True 10 -450 True 10 -451 True 11 -452 True 11 -453 True 11 -454 True 10 -455 True 10 -456 True 11 -457 True 10 -458 True 10 -459 True 11 -460 True 11 -461 True 10 -462 True 9 -463 True 10 -464 True 10 -465 True 10 -466 True 10 -467 True 9 -468 True 10 -469 True 10 -470 True 11 -471 True 11 -472 True 9 -473 True 9 -474 True 9 -475 True 9 -476 True 9 -477 True 10 -478 True 10 -479 True 9 -480 True 8 -481 True 10 -482 True 10 -483 True 10 -484 True 8 -485 True 8 -486 True 9 -487 True 8 -488 True 9 -489 True 10 -490 True 11 -491 True 11 -492 True 11 -493 True 9 -494 True 10 -495 True 10 -496 True 10 -497 True 10 -498 True 11 -499 True 11 -500 True 11 -501 True 11 -502 True 11 -503 True 11 -504 True 11 -505 True 11 -506 True 11 -507 True 11 -508 True 11 -509 True 11 -510 True 11 -511 True 11 -512 True 11 -513 True 11 -514 True 11 -515 True 11 -516 True 11 -517 True 11 -518 True 11 -519 True 11 -520 True 11 -521 True 11 -522 True 11 -523 True 11 -524 True 11 -525 True 11 -526 True 11 -527 True 11 -528 True 11 -529 True 11 -530 True 11 -531 True 11 -532 True 11 -533 True 11 -534 True 11 -535 True 11 -536 True 10 -537 True 10 -538 True 10 -539 True 10 -540 True 10 -541 True 10 -542 True 11 -543 True 11 -544 True 11 -545 True 11 -546 True 11 -547 True 10 -548 True 9 -549 True 9 -550 True 10 -551 True 11 -552 True 10 -553 True 9 -554 True 9 -555 True 9 -556 True 8 -557 True 9 -558 True 7 -559 True 8 -560 True 8 -561 True 10 -562 True 9 -563 True 8 -564 True 8 -565 True 8 -566 True 8 -567 True 8 -568 True 6 -569 True 6 -570 True 6 -571 True 6 -572 True 8 -573 True 8 -574 True 7 -575 True 9 -576 True 7 -577 True 7 -578 True 8 -579 True 8 -580 True 6 -581 True 7 -582 True 7 -583 True 8 -584 True 6 -585 True 5 -586 True 5 -587 True 5 -588 True 6 -589 True 7 -590 True 6 -591 True 8 -592 True 7 -593 True 7 -594 True 8 -595 True 7 -596 True 7 -597 True 8 -598 True 5 -599 True 4 -600 True 5 -601 True 6 -602 True 5 -603 True 6 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469.87094 -Pu.1 250.78569 -cJun 188.10158 -EgrNab 164.99693 -Gfi1 159.99155 diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular --- a/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular Mon Mar 04 10:14:25 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 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-9.449574351310729980e-02 -1.709264367818832397e-01 4.125457257032394409e-03 -6.873404979705810547e-02 4.397033154964447021e-02 1.867942214012145996e-01 -1.137310042977333069e-01 6.536182016134262085e-02 -8.733030408620834351e-02 4.486828297376632690e-02 6.879048049449920654e-02 3.395766764879226685e-02 -1.334057748317718506e-01 -3.880335018038749695e-02 5.785062164068222046e-02 -9.526924788951873779e-02 -1.083553209900856018e-01 1.990574225783348083e-02 1.783852465450763702e-02 -5.172537639737129211e-02 -1.314793080091476440e-01 -3.332496806979179382e-02 -1.304124593734741211e-01 7.114066183567047119e-02 4.942291602492332458e-02 1.143850944936275482e-02 3.503248840570449829e-02 8.114560507237911224e-03 2.840096876025199890e-02 6.378748267889022827e-02 -1.035511195659637451e-01 7.710250467061996460e-02 5.883126333355903625e-02 -1.181510612368583679e-01 -1.791930794715881348e-01 -2.050231397151947021e-02 -4.374536871910095215e-02 2.246104925870895386e-01 1.195801421999931335e-02 -1.478822380304336548e-01 -9.886784851551055908e-02 -9.984883666038513184e-02 -6.099858880043029785e-02 8.420736342668533325e-02 -9.065923094749450684e-02 diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular --- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:14:25 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 diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.pca.krumsiek11.h5ad Binary file test-data/tl.pca.krumsiek11.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.rank_genes_groups.krumsiek11.h5ad Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad Binary file test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad Binary file test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes.krumsiek11.h5ad Binary file test-data/tl.score_genes.krumsiek11.h5ad has changed diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes.krumsiek11.obs.tabular --- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:14:25 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 -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor 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