| Next changeset 1:a89ee42625ad (2019-10-16) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6 |
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added:
README.md README.rst macros.xml remove_confounders.xml test-data/krumsiek11.h5ad test-data/krumsiek11.loom test-data/krumsiek11_counts_per_cell test-data/paul15_subsample.h5ad test-data/pbmc68k_reduced.h5ad test-data/pl.clustermap.krumsiek11.png test-data/pl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsampl.png test-data/pl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.dotplot.krumsiek11.png test-data/pl.dpt_timeseries.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.heatmap.krumsiek11.png test-data/pl.highest_expr_genes.filter_genes_dispersion.krumsiek11-seurat.png test-data/pl.matrixplot.krumsiek11.png test-data/pl.paga.paul15_gauss_braycurtis.png test-data/pl.pca.pbmc68k_reduced.CD3D_CD79A_components_2d.pdf test-data/pl.pca_loadings.pp.pca.krumsiek11.png test-data/pl.pca_overview.pp.pca.krumsiek11.png test-data/pl.pca_variance_ratio.pp.pca.krumsiek11.png test-data/pl.scatter.krumsiek11.png test-data/pl.scatter.pca.krumsiek11.png test-data/pl.tsne.krumsiek11.png test-data/pl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.png test-data/pl.violin.pbmc68k_reduced_custom.png test-data/pp.filter_cells.krumsiek11-min_counts.h5ad test-data/pp.filter_cells.krumsiek11-min_counts.loom test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular test-data/pp.filter_genes.krumsiek11-min_counts.h5ad test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular test-data/pp.log1p.krumsiek11.h5ad test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.normalize_per_cell.krumsiek11.h5ad test-data/pp.normalize_per_cell.obs.krumsiek11.tabular test-data/pp.pca.krumsiek11.h5ad test-data/pp.pca.krumsiek11_chunk.h5ad test-data/pp.pca.variance.krumsiek11.tabular test-data/pp.recipe_seurat.recipe_zheng17.h5ad test-data/pp.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.recipe_zheng17.random-randint.h5ad test-data/pp.regress_out.krumsiek11.h5ad test-data/pp.scale.krumsiek11.h5ad test-data/pp.scale_max_value.krumsiek11.h5ad test-data/pp.sqrt.krumsiek11.h5ad test-data/pp.subsample.krumsiek11_fraction.h5ad test-data/pp.subsample.krumsiek11_n_obs.h5ad test-data/random-randint.h5ad test-data/random.h5ad test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular test-data/tl.dpt.neighbors.paul15_gauss_braycurtis.obs.tabular test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.pca.krumsiek11.h5ad test-data/tl.pca.variance_ratio.krumsiek11.tabular test-data/tl.rank_genes_groups.krumsiek11.h5ad test-data/tl.score_genes.krumsiek11.h5ad test-data/tl.score_genes.krumsiek11.obs.tabular test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular test-data/tl.tsne.krumsiek11.h5ad test-data/tl.tsne.krumsiek11_X_tsne.tabular test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Mon Mar 04 10:16:47 2019 -0500 |
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| @@ -0,0 +1,138 @@ +Scanpy +====== + +## Classification of methods into steps + +Steps: + +1. Filtering + + 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 + `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. + + 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 + + Methods | Description + --- | --- + `pp.normalize_per_cell` | Normalize total 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 + + 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] + +5. Clustering and Heatmaps + + Methods | Description + --- | --- + `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15] + `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17] + `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` | + +6. Cluster Inspection and plotting + + Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related. + + 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` | + +7. Branch/Between-Cluster Inspection + + Pseudotime analysis, relies on initial clustering. + + 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` | + + +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 |
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| diff -r 000000000000 -r 9ca360dde8e3 README.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Mon Mar 04 10:16:47 2019 -0500 |
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| @@ -0,0 +1,105 @@ +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 |
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| diff -r 000000000000 -r 9ca360dde8e3 macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Mon Mar 04 10:16:47 2019 -0500 |
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| b'@@ -0,0 +1,1084 @@\n+<macros>\n+ <token name="@version@">1.4</token>\n+ <token name="@galaxy_version@"><![CDATA[@version@+galaxy0]]></token>\n+ <xml name="requirements">\n+ <requirements>\n+ <requirement type="package" version="@version@">scanpy</requirement>\n+ <requirement type="package" version="2.0.17">loompy</requirement>\n+ <yield />\n+ </requirements>\n+ </xml>\n+ <xml name="citations">\n+ <citations>\n+ <citation type="doi">10.1186/s13059-017-1382-0</citation>\n+ </citations>\n+ </xml>\n+ <xml name="version_command">\n+ <version_command><![CDATA[python -c "import scanpy.api as sc;print(\'scanpy version: %s\' % sc.__version__)"]]></version_command>\n+ </xml>\n+ <token name="@CMD@"><![CDATA[\n+cat \'$script_file\' &&\n+python \'$script_file\'\n+ ]]>\n+ </token>\n+ <token name="@CMD_imports@"><![CDATA[\n+import scanpy.api as sc\n+import pandas as pd\n+import numpy as np\n+ ]]>\n+ </token>\n+ <xml name="inputs_anndata">\n+ <conditional name="input">\n+ <param name="format" type="select" label="Format for the annotated data matrix">\n+ <option value="loom">loom</option>\n+ <option value="h5ad">h5ad-formatted hdf5 (anndata)</option>\n+ </param>\n+ <when value="loom">\n+ <param name="adata" type="data" format="loom" label="Annotated data matrix"/>\n+ <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/>\n+ <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/>\n+ <param name="x_name" type="text" value="spliced" label="X_name"/>\n+ <param name="obs_names" type="text" value="CellID" label="obs_names"/>\n+ <param name="var_names" type="text" value="Gene" label="var_names"/>\n+ </when>\n+ <when value="h5ad">\n+ <param name="adata" type="data" format="h5" label="Annotated data matrix"/>\n+ </when>\n+ </conditional>\n+ </xml>\n+ <token name="@CMD_read_inputs@"><![CDATA[\n+#if $input.format == \'loom\'\n+adata = sc.read_loom(\n+ \'$input.adata\',\n+ sparse=$input.sparse,\n+ cleanup=$input.cleanup,\n+ X_name=\'$input.x_name\',\n+ obs_names=\'$input.obs_names\',\n+ var_names=\'$input.var_names\')\n+#else if $input.format == \'h5ad\'\n+adata = sc.read_h5ad(\'$input.adata\')\n+#end if\n+]]>\n+ </token>\n+ <xml name="anndata_output_format">\n+ <param name="anndata_output_format" type="select" label="Format to write the annotated data matrix">\n+ <option value="loom">loom</option>\n+ <option value="h5ad">h5ad-formatted hdf5 (anndata)</option>\n+ </param>\n+ </xml>\n+ <xml name="anndata_modify_output_input">\n+ <conditional name="modify_anndata">\n+ <param name="modify_anndata" type="select" label="Return modify annotate data matrix?">\n+ <option value="true">Yes</option>\n+ <option value="false">No</option>\n+ </param>\n+ <when value="true">\n+ <expand macro="anndata_output_format"/>\n+ </when>\n+ <when value="false"/>\n+ </conditional>\n+ </xml>\n+ <xml name="anndata_outputs">\n+ <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix">\n+ <filter>anndata_output_format == \'h5ad\'</filter>\n+ </data>\n+ <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix">\n+ <filter>anndata_output_format == \'loom\'</filter>\n+ </data>\n+ </xml>\n+ <xml name="anndata_modify_outputs">\n+ <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated'..b'ical sorting."/>\n+ <param argument="transitions" type="text" value="" label="Key corresponding to the matrix storing the arrows" help="Key for `.uns[\'paga\']`, e.g. \'transistions_confidence\'"/>\n+ <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\']`"/>\n+ <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."/>\n+ <param argument="single_component" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Restrict to largest connected component?" help=""/>\n+ <param argument="fontsize" type="integer" min="0" value="1" label="Font size for node labels" help=""/>\n+ <param argument="node_size_scale" type="float" min="0" value="1.0" label="Size of the nodes" help=""/>\n+ <param argument="node_size_power" type="float" min="0" value="0.5" label="Power with which groups sizes influence the radius of the nodes" help=""/>\n+ <param argument="edge_width_scale" type="float" min="0" value="5" label="Scale for edge width" help=""/>\n+ <param argument="min_edge_width" type="float" min="0" value="" optional="true" label="Min width of solid edges" help=""/>\n+ <param argument="max_edge_width" type="float" min="0" value="" optional="true" label="Max width of solid and dashed edges" help=""/>\n+ <param argument="arrowsize" type="integer" min="0" value="30" label="Arrow size" help="For directed graphs, choose the size of the arrow head head\'s length and width."/>\n+ <param argument="normalize_to_color" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Normalize categorical plots to color or the underlying grouping?" help=""/>\n+ <expand macro="param_cmap"/>\n+ <expand macro="param_title"/>\n+ <expand macro="param_frameon"/>\n+ </xml>\n+ <token name="@CMD_params_pl_paga@"><![CDATA[\n+ threshold=$method.threshold,\n+#if str($method.groups) != \'\'\n+ #set $groups=([x.strip() for x in str($method.groups).split(\',\')])\n+ groups=$groups,\n+#else\n+ groups=None,\n+#end if\n+ color=\'$method.color\',\n+#if $method.pos\n+ pos=np.fromfile($method.pos, dtype=dt),\n+#end if\n+#if str($method.labels) != \'\'\n+ #set $labels=([x.strip() for x in str($method.labels).split(\',\')])\n+ labels=$labels,\n+#end if\n+ layout=\'$method.layout\',\n+#if $method.init_pos\n+ init_pos=np.fromfile($method.init_pos, dtype=dt),\n+#end if\n+ random_state=$method.random_state,\n+#if str($method.root) != \'\'\n+ #set $root=([int(x.strip()) for x in str($method.root).split(\',\')])\n+ root=$root,\n+#end if\n+#if $method.transitions\n+ transitions=\'$method.transitions\',\n+#end if\n+ solid_edges=\'$method.solid_edges\',\n+#if $method.dashed_edges\n+ dashed_edges=\'$method.dashed_edges\',\n+#end if\n+ single_component=$method.single_component,\n+ fontsize=$method.fontsize,\n+ node_size_scale=$method.node_size_scale,\n+ node_size_power=$method.node_size_power,\n+ edge_width_scale=$method.edge_width_scale,\n+#if $method.min_edge_width\n+ min_edge_width=$method.min_edge_width,\n+#end if\n+#if $method.max_edge_width\n+ max_edge_width=$method.max_edge_width,\n+#end if\n+ arrowsize=$method.arrowsize,\n+ normalize_to_color=$method.normalize_to_color,\n+ cmap=\'$method.cmap\',\n+#if $method.title\n+ title=\'$method.title\',\n+#end if\n+ frameon=$method.frameon,\n+ ]]>\n+ </token>\n+ <xml name="param_swap_axes">\n+ <param argument="swap_axes" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Swap axes?" help="By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby` categories (if any). By setting `swap_axes` then x are the `groupby` categories and y the `var_names`."/>\n+ </xml>\n+</macros>\n' |
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| diff -r 000000000000 -r 9ca360dde8e3 remove_confounders.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/remove_confounders.xml Mon Mar 04 10:16:47 2019 -0500 |
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| b'@@ -0,0 +1,204 @@\n+<tool id="scanpy_remove_confounders" name="Remove confounders with scanpy" version="@version@">\n+ <description></description>\n+ <macros>\n+ <import>macros.xml</import>\n+ <xml name="score_genes_params">\n+ <param argument="n_bins" type="integer" value="25" label="Number of expression level bins for sampling" help=""/>\n+ <param argument="random_state" type="integer" value="0" label="Random seed for sampling" help=""/>\n+ <expand macro="param_use_raw"/>\n+ </xml>\n+ <token name="@CMD_score_genes_inputs@"><![CDATA[\n+ n_bins=$method.n_bins,\n+ random_state=$method.random_state,\n+ use_raw=$method.use_raw,\n+ copy=False\n+ ]]></token>\n+ </macros>\n+ <expand macro="requirements"/>\n+ <command detect_errors="exit_code"><![CDATA[\n+@CMD@\n+ ]]></command>\n+ <configfiles>\n+ <configfile name="script_file"><![CDATA[\n+@CMD_imports@\n+@CMD_read_inputs@\n+\n+#if $method.method == "pp.regress_out"\n+sc.pp.regress_out(\n+ adata=adata,\n+ keys=\'$method.reg_keys\',\n+ copy=False)\n+#elif $method.method == "tl.score_genes"\n+sc.tl.score_genes(\n+ adata=adata,\n+ #set $gene_list = [str(x.strip()) for x in str($method.gene_list).split(\',\')]\n+ gene_list=$gene_list,\n+ ctrl_size=$method.ctrl_size,\n+ score_name=\'$method.score_name\',\n+ #if $method.gene_pool\n+ #set $gene_pool = [str(x.strip()) for x in $method.gene_pool.split(\',\')]\n+ gene_pool=$gene_pool,\n+ #end if\n+ @CMD_score_genes_inputs@)\n+adata.obs.to_csv(\'$obs\', sep=\'\\t\')\n+#elif $method.method == "tl.score_genes_cell_cycle"\n+sc.tl.score_genes_cell_cycle(\n+ adata=adata,\n+ #set $s_genes = [str(x.strip()) for x in $method.s_genes.split(\',\')]\n+ s_genes=$s_genes,\n+ #set $g2m_genes = [str(x.strip()) for x in $method.g2m_genes.split(\',\')]\n+ g2m_genes=$g2m_genes,\n+ @CMD_score_genes_inputs@)\n+adata.obs.to_csv(\'$obs\', sep=\'\\t\')\n+#end if\n+\n+@CMD_anndata_write_outputs@\n+]]></configfile>\n+ </configfiles>\n+ <inputs>\n+ <expand macro="inputs_anndata"/>\n+ <conditional name="method">\n+ <param argument="method" type="select" label="Method used for plotting">\n+ <option value="pp.regress_out">Regress out unwanted sources of variation, using `pp.regress_out`</option>\n+ <!--<option value="pp.mnn_correct">, using `pp.mnn_correct`</option>!-->\n+ <!--<option value="pp.dca">, using `pp.mnn_correct`</option>!-->\n+ <!--<option value="pp.magic">, using `pp.magic`</option>!-->\n+ <!--<option value="tl.sim">, using `tl.sim`</option>!-->\n+ <!--<option value="pp.calculate_qc_metrics">, using `pp.calculate_qc_metrics`</option>!-->\n+ <option value="tl.score_genes">Score a set of genes, using `tl.score_genes`</option>\n+ <option value="tl.score_genes_cell_cycle">Score cell cycle genes, using `tl.score_genes_cell_cycle`</option>\n+ <!--<option value="tl.cyclone">, using `tl.cyclone`</option>!-->\n+ <!--<option value="tl.andbag">, using `tl.andbag`</option>!-->\n+ </param>\n+ <when value="pp.regress_out">\n+ <param argument="reg_keys" type="text" value="" label="Keys for observation annotation on which to regress on" help=""/>\n+ </when>\n+ <when value="tl.score_genes">\n+ <param argument="gene_list" type="text" value="" label="The list of gene names used for score calculation" help="Genes separated by a comma"/>\n+ <param argument="ctrl_size" type="integer" value="50" label="Number of reference genes to be sampled"\n+ help="If `len(gene_list)` is not too low, you can set `ctrl_size=len(gene_list)`."/>\n+ <param argument="gene_pool" type="text" value="" optional="true" label="Genes for sampling the reference set"\n+ help="Default is all genes. Genes separated by a comma"/>\n+'..b'/>\n+ </conditional>\n+ <param name="anndata_output_format" value="h5ad"/>\n+ <assert_stdout>\n+ <has_text_matching expression="sc.tl.score_genes" />\n+ <has_text_matching expression="gene_list=\\[\'Gata2\', \'Fog1\'\\]" />\n+ <has_text_matching expression="ctrl_size=2" />\n+ <has_text_matching expression="score_name=\'score\'" />\n+ <has_text_matching expression="n_bins=2" />\n+ <has_text_matching expression="random_state=2" />\n+ <has_text_matching expression="use_raw=False" />\n+ <has_text_matching expression="copy=False" />\n+ </assert_stdout>\n+ <output name="anndata_out_h5ad" file="tl.score_genes.krumsiek11.h5ad" ftype="h5" compare="sim_size"/>\n+ <output name="obs" file="tl.score_genes.krumsiek11.obs.tabular" ftype="tabular" compare="sim_size"/>\n+ </test>\n+ <test>\n+ <conditional name="input">\n+ <param name="format" value="h5ad" />\n+ <param name="adata" value="krumsiek11.h5ad" />\n+ </conditional>\n+ <conditional name="method">\n+ <param name="method" value="tl.score_genes_cell_cycle"/>\n+ <param name="s_genes" value="Gata2, Fog1, EgrNab"/>\n+ <param name="g2m_genes" value="Gata2, Fog1, EgrNab"/>\n+ <param name="n_bins" value="2"/>\n+ <param name="random_state" value="1"/>\n+ <param name="use_raw" value="False"/>\n+ </conditional>\n+ <param name="anndata_output_format" value="h5ad"/>\n+ <assert_stdout>\n+ <has_text_matching expression="sc.tl.score_genes_cell_cycle"/>\n+ <has_text_matching expression="s_genes=\\[\'Gata2\', \'Fog1\', \'EgrNab\'\\]"/>\n+ <has_text_matching expression="g2m_genes=\\[\'Gata2\', \'Fog1\', \'EgrNab\'\\]"/>\n+ <has_text_matching expression="n_bins=2"/>\n+ <has_text_matching expression="random_state=1"/>\n+ <has_text_matching expression="use_raw=False"/>\n+ </assert_stdout>\n+ <output name="anndata_out_h5ad" file="tl.score_genes_cell_cycle.krumsiek11.h5ad" ftype="h5" compare="sim_size"/>\n+ <output name="obs" file="tl.score_genes_cell_cycle.krumsiek11.obs.tabular" ftype="tabular" compare="sim_size"/>\n+ </test>\n+ </tests>\n+ <help><![CDATA[\n+Regress out unwanted sources of variation, using `pp.regress_out`\n+=================================================================\n+\n+Regress out unwanted sources of variation, using simple linear regression. This is \n+inspired by Seurat\'s `regressOut` function in R.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.regress_out.html#scanpy.api.pp.regress_out>`__\n+\n+Score a set of genes, using `tl.score_genes`\n+============================================\n+\n+The score is the average expression of a set of genes subtracted with the\n+average expression of a reference set of genes. The reference set is\n+randomly sampled from the `gene_pool` for each binned expression value.\n+\n+This reproduces the approach in Seurat (Satija et al, 2015) and has been implemented\n+for Scanpy by Davide Cittaro.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.score_genes.html#scanpy.api.tl.score_genes>`__\n+\n+Score cell cycle genes, using `tl.score_genes_cell_cycle`\n+=========================================================\n+\n+Given two lists of genes associated to S phase and G2M phase, calculates\n+scores and assigns a cell cycle phase (G1, S or G2M). See\n+`score_genes` for more explanation.\n+\n+More details on the `scanpy documentation\n+<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.score_genes_cell_cycle.html#scanpy.api.tl.score_genes_cell_cycle>`__\n+ ]]></help>\n+ <expand macro="citations"/>\n+</tool>\n' |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular Mon Mar 04 10:16:47 2019 -0500 |
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| @@ -0,0 +1,12 @@ +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 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
| @@ -0,0 +1,222 @@ + 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 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
| @@ -0,0 +1,12 @@ + 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 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
| @@ -0,0 +1,9 @@ +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 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.log1p.krumsiek11.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.normalize_per_cell.krumsiek11.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.normalize_per_cell.obs.krumsiek11.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:16:47 2019 -0500 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.pca.krumsiek11.h5ad |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.pca.variance.krumsiek11.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/pp.pca.variance.krumsiek11.tabular Mon Mar 04 10:16:47 2019 -0500 |
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| @@ -0,0 +1,10 @@ +7.540945410728454590e-01 +2.517578899860382080e-01 +8.900784701108932495e-02 +4.575614631175994873e-02 +2.166538313031196594e-02 +5.896944552659988403e-03 +2.081273589283227921e-03 +1.173791475594043732e-03 +1.542696409160271287e-04 +3.281867248006165028e-05 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular Mon Mar 04 10:16:47 2019 -0500 |
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|
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad |
| b |
| Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed |
| b |
| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad |
| b |
| Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed |
| b |
| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
| @@ -0,0 +1,101 @@ +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 |
| b |
| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.dpt.neighbors.paul15_gauss_braycurtis.obs.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.dpt.neighbors.paul15_gauss_braycurtis.obs.tabular Mon Mar 04 10:16:47 2019 -0500 |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.pca.variance_ratio.krumsiek11.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.pca.variance_ratio.krumsiek11.tabular Mon Mar 04 10:16:47 2019 -0500 |
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| @@ -0,0 +1,10 @@ +6.436238884925842285e-01 +2.148767709732055664e-01 +7.596869766712188721e-02 +3.905308619141578674e-02 +1.849153265357017517e-02 +5.033073481172323227e-03 +1.776377670466899872e-03 +1.001837081275880337e-03 +1.316703710472211242e-04 +2.801087975967675447e-05 |
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| b |
| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad |
| b |
| Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed |
| b |
| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
| b'@@ -0,0 +1,641 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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.tsne.krumsiek11.h5ad |
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| Binary file test-data/tl.tsne.krumsiek11.h5ad has changed |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.tsne.krumsiek11_X_tsne.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/tl.tsne.krumsiek11_X_tsne.tabular Mon Mar 04 10:16:47 2019 -0500 |
| b |
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| diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad |
| b |
| Binary file test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed |
