Repository 'scanpy_remove_confounders'
hg clone https://toolshed.g2.bx.psu.edu/repos/iuc/scanpy_remove_confounders

Changeset 0:9ca360dde8e3 (2019-03-04)
Next changeset 1:a89ee42625ad (2019-10-16)
Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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
b
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
[
@@ -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
b
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
b
@@ -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
b
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
[
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=$method.frameon,\n+    ]]>\n+    </token>\n+    <xml name="param_swap_axes">\n+        <param argument="swap_axes" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Swap axes?" help="By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby` categories (if any). By setting `swap_axes` then x are the `groupby` categories and y the `var_names`."/>\n+    </xml>\n+</macros>\n'
b
diff -r 000000000000 -r 9ca360dde8e3 remove_confounders.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/remove_confounders.xml Mon Mar 04 10:16:47 2019 -0500
[
b'@@ -0,0 +1,204 @@\n+<tool id="scanpy_remove_confounders" name="Remove confounders with scanpy" version="@version@">\n+    <description></description>\n+    <macros>\n+        <import>macros.xml</import>\n+        <xml name="score_genes_params">\n+            <param argument="n_bins" type="integer" value="25" label="Number of expression level bins for sampling" help=""/>\n+            <param argument="random_state" type="integer" value="0" label="Random seed for sampling" help=""/>\n+            <expand macro="param_use_raw"/>\n+        </xml>\n+        <token name="@CMD_score_genes_inputs@"><![CDATA[\n+    n_bins=$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'
b
diff -r 000000000000 -r 9ca360dde8e3 test-data/krumsiek11.h5ad
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diff -r 000000000000 -r 9ca360dde8e3 test-data/krumsiek11.loom
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diff -r 000000000000 -r 9ca360dde8e3 test-data/krumsiek11_counts_per_cell
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/krumsiek11_counts_per_cell Mon Mar 04 10:16:47 2019 -0500
b
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular Mon Mar 04 10:16:47 2019 -0500
b
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5\tTrue\t7\n+326\tTrue\t8\n+327\tTrue\t8\n+328\tTrue\t7\n+329\tTrue\t9\n+330\tTrue\t8\n+331\tTrue\t9\n+332\tTrue\t8\n+333\tTrue\t8\n+334\tTrue\t8\n+335\tTrue\t10\n+336\tTrue\t10\n+337\tTrue\t9\n+338\tTrue\t10\n+339\tTrue\t10\n+340\tTrue\t10\n+341\tTrue\t10\n+342\tTrue\t10\n+343\tTrue\t10\n+344\tTrue\t10\n+345\tTrue\t11\n+346\tTrue\t11\n+347\tTrue\t11\n+348\tTrue\t11\n+349\tTrue\t11\n+350\tTrue\t11\n+351\tTrue\t11\n+352\tTrue\t11\n+353\tTrue\t11\n+354\tTrue\t11\n+355\tTrue\t11\n+356\tTrue\t11\n+357\tTrue\t11\n+358\tTrue\t11\n+359\tTrue\t11\n+360\tTrue\t11\n+361\tTrue\t11\n+362\tTrue\t11\n+363\tTrue\t11\n+364\tTrue\t11\n+365\tTrue\t11\n+366\tTrue\t11\n+367\tTrue\t11\n+368\tTrue\t11\n+369\tTrue\t11\n+370\tTrue\t11\n+371\tTrue\t11\n+372\tTrue\t11\n+373\tTrue\t11\n+374\tTrue\t11\n+375\tTrue\t11\n+376\tTrue\t11\n+377\tTrue\t11\n+378\tTrue\t11\n+379\tTrue\t11\n+380\tTrue\t11\n+381\tTrue\t11\n+382\tTrue\t11\n+383\tTrue\t11\n+384\tTrue\t11\n+385\tTrue\t11\n+386\tTrue\t11\n+387\tTrue\t11\n+388\tTrue\t11\n+389\tTrue\t11\n+390\tTrue\t11\n+391\tTrue\t11\n+392\tTrue\t11\n+393\tTrue\t11\n+394\tTrue\t11\n+395\tTrue\t11\n+396\tTrue\t11\n+397\tTrue\t11\n+398\tTrue\t11\n+399\tTrue\t11\n+400\tTrue\t11\n+401\tTrue\t11\n+402\tTrue\t11\n+403\tTrue\t11\n+404\tTrue\t11\n+405\tTrue\t11\n+406\tTrue\t11\n+407\tTrue\t11\n+408\tTrue\t11\n+409\tTrue\t11\n+410\tTrue\t11\n+411\tTrue\t11\n+412\tTrue\t11\n+413\tTrue\t11\n+414\tTrue\t11\n+415\tTrue\t11\n+416\tTrue\t11\n+417\tTrue\t11\n+418\tTrue\t11\n+419\tTrue\t11\n+420\tTrue\t11\n+421\tTrue\t11\n+422\tTrue\t11\n+423\tTrue\t11\n+424\tTrue\t11\n+425\tTrue\t11\n+426\tTrue\t11\n+427\tTrue\t11\n+428\tTrue\t11\n+429\tTrue\t11\n+430\tTrue\t11\n+431\tTrue\t11\n+432\tTrue\t11\n+433\tTrue\t11\n+434\tTrue\t11\n+435\tTrue\t11\n+436\tTrue\t11\n+437\tTrue\t11\n+438\tTrue\t11\n+439\tTrue\t11\n+440\tTrue\t11\n+441\tTrue\t11\n+442\tTrue\t11\n+443\tTrue\t11\n+444\tTrue\t11\n+445\tTrue\t11\n+446\tTrue\t10\n+447\tTrue\t10\n+448\tTrue\t10\n+449\tTrue\t10\n+450\tTrue\t10\n+451\tTrue\t11\n+452\tTrue\t11\n+453\tTrue\t11\n+454\tTrue\t10\n+455\tTrue\t10\n+456\tTrue\t11\n+457\tTrue\t10\n+458\tTrue\t10\n+459\tTrue\t11\n+460\tTrue\t11\n+461\tTrue\t10\n+462\tTrue\t9\n+463\tTrue\t10\n+464\tTrue\t10\n+465\tTrue\t10\n+466\tTrue\t10\n+467\tTrue\t9\n+468\tTrue\t10\n+469\tTrue\t10\n+470\tTrue\t11\n+471\tTrue\t11\n+472\tTrue\t9\n+473\tTrue\t9\n+474\tTrue\t9\n+475\tTrue\t9\n+476\tTrue\t9\n+477\tTrue\t10\n+478\tTrue\t10\n+479\tTrue\t9\n+480\tTrue\t8\n+481\tTrue\t10\n+482\tTrue\t10\n+483\tTrue\t10\n+484\tTrue\t8\n+485\tTrue\t8\n+486\tTrue\t9\n+487\tTrue\t8\n+488\tTrue\t9\n+489\tTrue\t10\n+490\tTrue\t11\n+491\tTrue\t11\n+492\tTrue\t11\n+493\tTrue\t9\n+494\tTrue\t10\n+495\tTrue\t10\n+496\tTrue\t10\n+497\tTrue\t10\n+498\tTrue\t11\n+499\tTrue\t11\n+500\tTrue\t11\n+501\tTrue\t11\n+502\tTrue\t11\n+503\tTrue\t11\n+504\tTrue\t11\n+505\tTrue\t11\n+506\tTrue\t11\n+507\tTrue\t11\n+508\tTrue\t11\n+509\tTrue\t11\n+510\tTrue\t11\n+511\tTrue\t11\n+512\tTrue\t11\n+513\tTrue\t11\n+514\tTrue\t11\n+515\tTrue\t11\n+516\tTrue\t11\n+517\tTrue\t11\n+518\tTrue\t11\n+519\tTrue\t11\n+520\tTrue\t11\n+521\tTrue\t11\n+522\tTrue\t11\n+523\tTrue\t11\n+524\tTrue\t11\n+525\tTrue\t11\n+526\tTrue\t11\n+527\tTrue\t11\n+528\tTrue\t11\n+529\tTrue\t11\n+530\tTrue\t11\n+531\tTrue\t11\n+532\tTrue\t11\n+533\tTrue\t11\n+534\tTrue\t11\n+535\tTrue\t11\n+536\tTrue\t10\n+537\tTrue\t10\n+538\tTrue\t10\n+539\tTrue\t10\n+540\tTrue\t10\n+541\tTrue\t10\n+542\tTrue\t11\n+543\tTrue\t11\n+544\tTrue\t11\n+545\tTrue\t11\n+546\tTrue\t11\n+547\tTrue\t10\n+548\tTrue\t9\n+549\tTrue\t9\n+550\tTrue\t10\n+551\tTrue\t11\n+552\tTrue\t10\n+553\tTrue\t9\n+554\tTrue\t9\n+555\tTrue\t9\n+556\tTrue\t8\n+557\tTrue\t9\n+558\tTrue\t7\n+559\tTrue\t8\n+560\tTrue\t8\n+561\tTrue\t10\n+562\tTrue\t9\n+563\tTrue\t8\n+564\tTrue\t8\n+565\tTrue\t8\n+566\tTrue\t8\n+567\tTrue\t8\n+568\tTrue\t6\n+569\tTrue\t6\n+570\tTrue\t6\n+571\tTrue\t6\n+572\tTrue\t8\n+573\tTrue\t8\n+574\tTrue\t7\n+575\tTrue\t9\n+576\tTrue\t7\n+577\tTrue\t7\n+578\tTrue\t8\n+579\tTrue\t8\n+580\tTrue\t6\n+581\tTrue\t7\n+582\tTrue\t7\n+583\tTrue\t8\n+584\tTrue\t6\n+585\tTrue\t5\n+586\tTrue\t5\n+587\tTrue\t5\n+588\tTrue\t6\n+589\tTrue\t7\n+590\tTrue\t6\n+591\tTrue\t8\n+592\tTrue\t7\n+593\tTrue\t7\n+594\tTrue\t8\n+595\tTrue\t7\n+596\tTrue\t7\n+597\tTrue\t8\n+598\tTrue\t5\n+599\tTrue\t4\n+600\tTrue\t5\n+601\tTrue\t6\n+602\tTrue\t5\n+603\tTrue\t6\n+604\tTrue\t7\n+605\tTrue\t7\n+606\tTrue\t9\n+607\tTrue\t10\n+608\tTrue\t8\n+609\tTrue\t8\n+610\tTrue\t10\n+611\tTrue\t10\n+612\tTrue\t9\n+613\tTrue\t8\n+614\tTrue\t8\n+615\tTrue\t8\n+616\tTrue\t7\n+617\tTrue\t8\n+618\tTrue\t7\n+619\tTrue\t6\n+620\tTrue\t6\n+621\tTrue\t7\n+622\tTrue\t7\n+623\tTrue\t7\n+624\tTrue\t8\n+625\tTrue\t6\n+626\tTrue\t7\n+627\tTrue\t7\n+628\tTrue\t7\n+629\tTrue\t6\n+630\tTrue\t5\n+631\tTrue\t7\n+632\tTrue\t6\n+633\tTrue\t6\n+634\tTrue\t7\n+635\tTrue\t6\n+636\tTrue\t8\n+637\tTrue\t8\n+638\tTrue\t6\n+639\tTrue\t8\n'
b
diff -r 000000000000 -r 9ca360dde8e3 test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
b
Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed
b
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
b
@@ -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
b
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
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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
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b'@@ -0,0 +1,100 @@\n+1.006254479289054871e-01\t7.390013337135314941e-02\t5.162549763917922974e-02\t-6.088243797421455383e-02\t1.660361140966415405e-02\t2.669865079224109650e-02\t-1.013666391372680664e-01\t5.378784239292144775e-02\t2.008118629455566406e-01\t1.484276503324508667e-01\t1.083310469985008240e-01\t1.318635195493698120e-01\t1.997928470373153687e-01\t-7.899370044469833374e-02\t-2.425468564033508301e-01\n+9.916571527719497681e-02\t-6.192789599299430847e-02\t3.743748366832733154e-02\t5.766532197594642639e-02\t2.186784986406564713e-03\t-1.058542281389236450e-01\t5.377947166562080383e-02\t1.402157917618751526e-02\t1.486204266548156738e-01\t8.553525805473327637e-02\t2.134956121444702148e-01\t-9.449188411235809326e-02\t-9.736447781324386597e-02\t-6.411797553300857544e-02\t-3.109178543090820312e-01\n+1.063194051384925842e-01\t8.235514909029006958e-02\t9.153002500534057617e-02\t-1.288201361894607544e-01\t2.123118191957473755e-02\t-5.571385100483894348e-02\t4.388944245874881744e-03\t-1.907968819141387939e-01\t5.077145248651504517e-02\t-5.320586264133453369e-02\t-2.017502486705780029e-02\t2.888677455484867096e-02\t-3.507991880178451538e-02\t5.667190253734588623e-02\t7.913256995379924774e-03\n+9.840648621320724487e-02\t-4.607489332556724548e-02\t3.584916703402996063e-03\t1.064319070428609848e-02\t-1.083880066871643066e-01\t1.248296573758125305e-01\t6.351136416196823120e-02\t1.885401159524917603e-01\t-2.020059973001480103e-01\t3.631909489631652832e-01\t1.699842363595962524e-01\t1.017943695187568665e-01\t-1.385858207941055298e-01\t3.727469593286514282e-02\t8.702995628118515015e-02\n+1.002686843276023865e-01\t8.042127639055252075e-02\t-1.485065966844558716e-01\t-1.834145635366439819e-01\t1.185779422521591187e-01\t-4.452232271432876587e-03\t2.401492185890674591e-02\t1.709450007183477283e-04\t-1.044583246111869812e-01\t1.082587465643882751e-01\t8.498968929052352905e-02\t1.078867260366678238e-02\t1.678988523781299591e-02\t-4.863050952553749084e-02\t-8.437795564532279968e-03\n+1.039626300334930420e-01\t6.898555904626846313e-02\t-5.245065316557884216e-02\t-1.401370912790298462e-01\t1.724163144826889038e-01\t-9.417925775051116943e-02\t5.670287460088729858e-02\t-2.294020541012287140e-02\t-1.041278913617134094e-01\t-4.751206189393997192e-02\t-2.486055344343185425e-02\t1.906321793794631958e-01\t-1.133706346154212952e-01\t1.018952578306198120e-01\t-1.696813404560089111e-01\n+1.031595841050148010e-01\t6.644981354475021362e-02\t-3.434765338897705078e-02\t-8.121536672115325928e-02\t-2.303110063076019287e-01\t6.845026742666959763e-03\t-3.113305754959583282e-02\t3.572509065270423889e-02\t-2.340330332517623901e-01\t1.681309342384338379e-01\t2.042572945356369019e-01\t-2.152139544486999512e-01\t3.323959186673164368e-02\t-1.992796063423156738e-01\t1.612142026424407959e-01\n+1.063378602266311646e-01\t9.444754570722579956e-02\t1.147621273994445801e-01\t-1.788049340248107910e-01\t1.663699001073837280e-01\t-1.299377232789993286e-01\t9.099455177783966064e-02\t8.901253342628479004e-02\t-3.782828152179718018e-02\t1.340985000133514404e-01\t-1.110952645540237427e-01\t-1.353431493043899536e-01\t9.196916222572326660e-02\t-5.004190653562545776e-02\t1.283620446920394897e-01\n+1.019066423177719116e-01\t3.868932276964187622e-02\t-5.681320559233427048e-03\t2.885684370994567871e-02\t-1.618600785732269287e-01\t-7.793435454368591309e-02\t-6.321301311254501343e-02\t-7.030926644802093506e-02\t-8.451142162084579468e-02\t1.784774512052536011e-01\t-5.302700772881507874e-02\t2.233742922544479370e-02\t-8.568169549107551575e-03\t-3.328817337751388550e-02\t-2.190946601331233978e-02\n+1.055724918842315674e-01\t6.597422808408737183e-02\t1.207857206463813782e-01\t-1.769655644893646240e-01\t-1.941676251590251923e-02\t-2.537060342729091644e-02\t2.017348445951938629e-02\t2.019873559474945068e-01\t2.603957951068878174e-01\t-8.517616987228393555e-02\t1.920531690120697021e-01\t3.013696968555450439e-01\t1.285752207040786743e-01\t-6.937998533248901367e-02\t2.583270706236362457e-02\n+1.013405695557594299e-01\t7.315808534622192383e-02\t-4.366664588451385498e-02\t2.623320743441581726e-02\t-1.268581300973892212e-01\t-9.996589273214340210e-02\t3.80'..b'926277161e-02\t6.935269385576248169e-02\t5.418083444237709045e-02\t-2.927818335592746735e-02\t2.737504616379737854e-02\t6.675768643617630005e-03\t-4.358346015214920044e-02\n+1.045850887894630432e-01\t3.966004028916358948e-02\t7.888220995664596558e-02\t1.084793508052825928e-01\t-3.825098276138305664e-02\t5.556414648890495300e-02\t-2.386018633842468262e-03\t-1.735868491232395172e-02\t1.626232713460922241e-01\t-3.189559327438473701e-03\t-3.677500039339065552e-02\t-1.077032759785652161e-01\t-6.530553102493286133e-02\t-1.181415840983390808e-01\t2.054665982723236084e-01\n+1.013580858707427979e-01\t6.717060506343841553e-02\t-7.871925085783004761e-02\t-4.536754917353391647e-03\t-8.628232032060623169e-02\t-4.364237934350967407e-02\t5.463447421789169312e-02\t7.559294998645782471e-02\t1.829659007489681244e-02\t-3.810231760144233704e-02\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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
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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
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@@ -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
b
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.pca.krumsiek11.h5ad
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.pca.variance_ratio.krumsiek11.tabular
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.score_genes.krumsiek11.h5ad
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diff -r 000000000000 -r 9ca360dde8e3 test-data/tl.score_genes.krumsiek11.obs.tabular
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+++ b/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:16:47 2019 -0500
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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
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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
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Binary file test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed