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

Changeset 1:a9f14e2d1655 (2019-10-16)
Previous changeset 0:ed64c90a9b93 (2019-03-04) Next changeset 2:8e0f141c8c66 (2019-12-05)
Commit message:
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
modified:
README.md
macros.xml
normalize.xml
test-data/pl.clustermap.krumsiek11.png
test-data/pl.dotplot.krumsiek11.png
test-data/pl.pca_overview.pp.pca.krumsiek11.png
test-data/pl.scatter.krumsiek11.png
test-data/pp.filter_cells.krumsiek11-min_counts.h5ad
test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad
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.pca.krumsiek11.h5ad
test-data/pp.recipe_seurat.recipe_zheng17.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/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.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.rank_genes_groups.krumsiek11.h5ad
test-data/tl.score_genes.krumsiek11.h5ad
test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad
test-data/tl.tsne.krumsiek11.h5ad
test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
added:
test-data/blobs.h5ad
test-data/pl.draw_graph.png
test-data/pl.highly_variable_genes.seurat.blobs.png
test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png
test-data/pl.rank_genes_groups_violin.Ery.png
test-data/pl.rank_genes_groups_violin.Mk.png
test-data/pl.rank_genes_groups_violin.Mo.png
test-data/pl.rank_genes_groups_violin.Neu.png
test-data/pl.rank_genes_groups_violin.progenitor.png
test-data/pl.scatter.umap.pbmc68k_reduced.png
test-data/pl.stacked_violin.krumsiek11.png
test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad
test-data/pp.combat.blobs.h5ad
test-data/pp.downsample_counts.random-randint.h5ad
test-data/pp.filter_cells.krumsiek11-max_genes.h5ad
test-data/pp.filter_rank_genes_groups.h5ad
test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad
test-data/pp.highly_variable_genes.seurat.blobs.h5ad
test-data/pp.normalize_total.krumsiek11.h5ad
test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad
test-data/sparce_csr_matrix.h5ad
test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad
test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad
removed:
README.rst
test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular
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.per_gene.krumsiek11-cell_ranger.tabular
test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular
test-data/pp.normalize_per_cell.obs.krumsiek11.tabular
test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular
test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular
test-data/tl.score_genes.krumsiek11.obs.tabular
test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular
b
diff -r ed64c90a9b93 -r a9f14e2d1655 README.md
--- a/README.md Mon Mar 04 10:16:12 2019 -0500
+++ b/README.md Wed Oct 16 06:31:10 2019 -0400
[
b'@@ -1,138 +1,115 @@\n Scanpy\n ======\n \n-## Classification of methods into steps\n+1. Inspect & Manipulate (`inspect.xml`)\n \n-Steps:\n+    Methods | Description\n+    --- | ---\n+    `pp.calculate_qc_metrics` | Calculate quality control metrics\n+    `pp.neighbors` | Compute a neighborhood graph of observations\n+    `tl.score_genes` | Score a set of genes\n+    `tl.score_genes_cell_cycle` | Score cell cycle gene\n+    `tl.rank_genes_groups` | Rank genes for characterizing groups\n+    `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**)\n+    `pp.log1p` | Logarithmize the data matrix.\n+    `pp.scale` | Scale data to unit variance and zero mean\n+    `pp.sqrt` | Square root the data matrix\n \n-1. Filtering\n+2. Filter (`filter.xml`)\n \n     Methods | Description\n     --- | ---\n     `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed.\n     `pp.filter_genes` | Filter genes based on number of cells or counts.\n-    `pp.filter_genes_dispersion` | Extract highly variable genes\n+    `tl.filter_rank_genes_groups` | Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (**to fix**)\n     `pp.highly_variable_genes` | Extract highly variable genes\n     `pp.subsample` | Subsample to a fraction of the number of observations\n-    `queries.gene_coordinates` | (Could not find...)\n-    `queries.mitochondrial_genes` | Retrieves Mitochondrial gene symbols for specific organism through BioMart for filtering\n-\n-2. Quality Plots\n-\n-   These are in-between stages used to measure the effectiveness of a Filtering/Normalisation/Conf.Removal stage either after processing or prior to.\n+    `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts\n \n-    Methods | Description | Notes\n-    --- | --- | ---\n-    `pp.calculate_qc_metrics` | Calculate quality control metrics\n-    `pl.violin` | violin plot of features, lib. size, or subsets of. \n-    `pl.stacked_violin` | Same as above but for multiple series of features or cells\n-\n-3. Normalization\n+3. Normalize (`normalize.xml`)\n \n     Methods | Description\n     --- | ---\n-    `pp.normalize_per_cell` | Normalize total counts per cell\n+    `pp.normalize_total` | Normalize counts per cell\n     `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]\n     `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]\n     `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15]\n-    `pp.log1p` | Logarithmize the data matrix.\n-    `pp.scale` | Scale data to unit variance and zero mean\n-    `pp.sqrt` | \n-    `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts\n \n-4. Conf. removal\n+4. Remove confounders (`remove_confounder.xml`)\n \n     Methods | Description\n     --- | ---\n    `pp.regress_out` | Regress out unwanted sources of variation\n    `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors\n-   `pp.dca` | Deep count autoencoder to denoise the data\n-   `pp.magic` | Markov Affinity-based Graph Imputation of Cells (MAGIC) API to denoise\n-   `tl.sim` | Simulate dynamic gene expression data [Wittman09]\n-   `pp.calculate_qc_metrics` | Calculate quality control metrics\n-   `tl.score_genes` | Score a set of genes\n-   `tl.score_genes_cell_cycle` | Score cell cycle genes\n-   `tl.cyclone` | Assigns scores and predicted class to observations based on cell-cycle genes [Scialdone15]\n-   `tl.sandbag` | Calculates pairs of genes serving as markers for each cell-cycle phase [Scialdone15]\n+   `pp.combat` | ComBat function for batch effect correction\n \n-5. Clustering and Heatmaps\n+5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`)\n \n     Methods | Description\n     --- | ---\n-    `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15]\n-    `tl.louvain` | Cluster cells into subgroups [Blondel08] [Lev'..b"| Heatmap of the expression values of set of genes\n+        `pl.dotplot` | Makes a dot plot of the expression values\n+        `pl.violin` | Violin plot\n+        `pl.stacked_violin` | Stacked violin plots\n+        `pl.matrixplot` | Heatmap of the mean expression values per cluster\n+        `pl.clustermap` | Hierarchically-clustered heatmap\n     \n-6. Cluster Inspection and plotting\n+    2. Preprocessing\n \n-    Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related.\n+        Methods | Description\n+        --- | ---\n+        `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells\n+        `pl.highly_variable_genes` | Plot dispersions versus means for genes\n+\n+    3. PCA\n \n-    Methods | Description \n-    --- | --- \n-    `pl.clustermap` |\n-    `pl.phate` | \n-    `pl.dotplot` | \n-    `pl.draw_graph` | (really general purpose, would not implement directly)\n-    `pl.filter_genes_dispersion` | (depreciated for 'highly_variable_genes')\n-    `pl.matrix` | (could not find in API)\n-    `pl.pca` | \n-    `pl.pca_loadings` | \n-    `pl.pca_overview` | \n-    `pl.pca_variance_ratio` | \n-    `pl.ranking` | (not sure what this does...)\n-    `pl.scatter` | ([very general purpose](https://icb-scanpy.readthedocs-hosted.com/en/latest/api/scanpy.api.pl.scatter.html), would not implement directly)\n-    `pl.set_rcParams_defaults` | \n-    `pl.set_rcParams_scanpy` | \n-    `pl.sim` | \n-    `pl.tsne` | \n-    `pl.umap` | \n+        Methods | Description\n+        --- | ---\n+        `pl.pca` | Scatter plot in PCA coordinates\n+        `pl.pca_loadings` | Rank genes according to contributions to PCs\n+        `pl.pca_variance_ratio` | Scatter plot in PCA coordinates\n+        `pl.pca_overview` | Plot PCA results\n \n-7. Branch/Between-Cluster Inspection\n+    4. Embeddings\n \n-    Pseudotime analysis, relies on initial clustering.\n+        Methods | Description\n+        --- | ---\n+        `pl.tsne` | Scatter plot in tSNE basis\n+        `pl.umap` | Scatter plot in UMAP basis\n+        `pl.diffmap` | Scatter plot in Diffusion Map basis\n+        `pl.draw_graph` | Scatter plot in graph-drawing basis\n \n-    Methods | Description\n-    --- | ---\n-    `tl.dpt` | Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i]\n-    `pl.dpt_groups_pseudotime` | \n-    `pl.dpt_timeseries` | \n-    `tl.paga_compare_paths` | \n-    `tl.paga_degrees` | \n-    `tl.paga_expression_entropies` | \n-    `tl.paga` | Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i]\n-    `pl.paga` | \n-    `pl.paga_adjacency` | \n-    `pl.paga_compare` | \n-    `pl.paga_path` | \n-    `pl.timeseries` | \n-    `pl.timeseries_as_heatmap` | \n-    `pl.timeseries_subplot` | \n+    5. Branching trajectories and pseudotime, clustering\n \n+        Methods | Description\n+        --- | ---\n+        `pl.dpt_groups_pseudotime` | Plot groups and pseudotime\n+        `pl.dpt_timeseries` | Heatmap of pseudotime series\n+        `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges\n+        `pl.paga_compare` | Scatter and PAGA graph side-by-side\n+        `pl.paga_path` | Gene expression and annotation changes along paths\n \n-Methods to sort | Description\n---- | --- \n-`tl.ROC_AUC_analysis` | (could not find in API)\n-`tl.correlation_matrix` | (could not find in API)\n-`rtools.mnn_concatenate` | (could not find in API)\n-`utils.compute_association_matrix_of_groups` | (could not find in API) \n-`utils.cross_entropy_neighbors_in_rep` | (could not find in API)\n-`utils.merge_groups` | (could not find in API)\n-`utils.plot_category_association` | (could not find in API)\n-`utils.select_groups` | (could not find in API)\n\\ No newline at end of file\n+    6. Marker genes\n+\n+        Methods | Description\n+        --- | ---\n+        `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot\n+        `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons\n"
b
diff -r ed64c90a9b93 -r a9f14e2d1655 README.rst
--- a/README.rst Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
@@ -1,105 +0,0 @@
-The different methods from Scanpy have been grouped by themes:
-
-1. Filter in `filter.xml`
-  - Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`
-  - Filter genes based on number of cells or counts, using `pp.filter_genes`
-  - Extract highly variable genes, using `pp.filter_genes_dispersion`
-  - `tl.highly_variable_genes` (need to be added)
-  - Subsample to a fraction of the number of observations, using `pp.subsample`
-  - `queries.gene_coordinates` (need to be added)
-  - `queries.mitochondrial_genes` (need to be added)
-
-2. Normalize in `normalize.xml`
-  - Normalize total counts per cell, using `pp.normalize_per_cell`
-  - Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`
-  - Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`
-  - Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`
-  - Logarithmize the data matrix, using `pp.log1p`
-  - Scale data to unit variance and zero mean, using `pp.scale`
-  - Square root the data matrix, using `pp.sqrt`
-  - Downsample counts, using `pp.downsample_counts`
-
-3. Remove confounder in `remove_confounders.xml`
-  - Regress out unwanted sources of variation, using `pp.regress_out`
-  - `pp.mnn_correct` (need to be added)
-  - `pp.mnn_correct` (need to be added)
-  - `pp.magic` (need to be added)
-  - `tl.sim` (need to be added)
-  - `pp.calculate_qc_metrics` (need to be added)
-  - Score a set of genes, using `tl.score_genes`
-  - Score cell cycle genes, using `tl.score_genes_cell_cycle`
-  - `tl.cyclone` (need to be added)
-  - `tl.andbag` (need to be added)
-
-4. Cluster and reduce dimension in `cluster_reduce_dimension.xml`
-  - `tl.leiden` (need to be added)
-  - Cluster cells into subgroups, using `tl.louvain`
-  - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
-  - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
-  - Diffusion Maps, using `tl.diffmap`
-  - t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
-  - Embed the neighborhood graph using UMAP, using `tl.umap`
-  - `tl.phate` (need to be added)
-  - Compute a neighborhood graph of observations, using `pp.neighbors`
-  - Rank genes for characterizing groups, using `tl.rank_genes_groups`
-
-4. Inspect
-  - `tl.paga_compare_paths` (need to be added)
-  - `tl.paga_degrees` (need to be added)
-  - `tl.paga_expression_entropies` (need to be added)
-  - Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`
-  - Infer progression of cells through geodesic distance along the graph, using `tl.dpt`
-
-5. Plot
-  1. Generic
-    - Scatter plot along observations or variables axes, using `pl.scatter`
-    - Heatmap of the expression values of set of genes, using `pl.heatmap`
-    - Makes a dot plot of the expression values, using `pl.dotplot`
-    - Violin plot, using `pl.violin`
-    - `pl.stacked_violin` (need to be added)
-    - Heatmap of the mean expression values per cluster, using `pl.matrixplot`
-    - Hierarchically-clustered heatmap, using `pl.clustermap`
-    - `pl.ranking` 
-
-  2. Preprocessing
-    - Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes`
-    - Plot dispersions versus means for genes, using `pl.filter_genes_dispersion`
-    - `pl.highly_variable_genes` (need to be added)
-    - `pl.calculate_qc_metrics` (need to be added)
-  
-  3. PCA
-    - Scatter plot in PCA coordinates, using `pl.pca`
-    - Rank genes according to contributions to PCs, using `pl.pca_loadings`
-    - Scatter plot in PCA coordinates, using `pl.pca_variance_ratio`
-    - Plot PCA results, using `pl.pca_overview`
-  
-  4. Embeddings
-    - Scatter plot in tSNE basis, using `pl.tsne`
-    - Scatter plot in UMAP basis, using `pl.umap`
-    - Scatter plot in Diffusion Map basis, using `pl.diffmap`
-    - `pl.draw_graph` (need to be added)
-
-  5. Branching trajectories and pseudotime, clustering
-    - Plot groups and pseudotime, using `pl.dpt_groups_pseudotime`
-    - Heatmap of pseudotime series, using `pl.dpt_timeseries`
-    - Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga`
-    - `pl.paga_compare` (need to be added)
-    - `pl.paga_path` (need to be added)
-
-  6. Marker genes: 
-    - Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups`
-    - `pl.rank_genes_groups_dotplot` (need to be added)
-    - `pl.rank_genes_groups_heatmap` (need to be added)
-    - `pl.rank_genes_groups_matrixplot` (need to be added)
-    - `pl.rank_genes_groups_stacked_violin` (need to be added)
-    - `pl.rank_genes_groups_violin` (need to be added)
-
-  7. Misc
-    - `pl.phate` (need to be added)
-    - `pl.matrix` (need to be added)
-    - `pl.paga_adjacency` (need to be added)
-    - `pl.timeseries` (need to be added)
-    - `pl.timeseries_as_heatmap` (need to be added)
-    - `pl.timeseries_subplot` (need to be added)
-    
-  
\ No newline at end of file
b
diff -r ed64c90a9b93 -r a9f14e2d1655 macros.xml
--- a/macros.xml Mon Mar 04 10:16:12 2019 -0500
+++ b/macros.xml Wed Oct 16 06:31:10 2019 -0400
[
b'@@ -1,10 +1,12 @@\n <macros>\n-    <token name="@version@">1.4</token>\n+    <token name="@version@">1.4.4</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+            <requirement type="package" version="2.9.0">h5py</requirement>\n+            <requirement type="package" version="0.7.0">leidenalg</requirement>\n             <yield />\n         </requirements>\n     </xml>\n@@ -14,102 +16,33 @@\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+        <version_command><![CDATA[python -c "import scanpy as sc;print(\'scanpy version: %s\' % sc.__version__)"]]></version_command>\n     </xml>\n     <token name="@CMD@"><![CDATA[\n+cp \'$adata\' \'anndata.h5ad\' &&\n cat \'$script_file\' &&\n-python \'$script_file\'\n+python \'$script_file\' &&\n+ls .\n     ]]>\n     </token>\n     <token name="@CMD_imports@"><![CDATA[\n-import scanpy.api as sc\n+import scanpy 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+        <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/>\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+adata = sc.read(\'anndata.h5ad\')\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 '..b'tegorical for which `tl.paga` has been computed."/>\n-        <param argument="layout" type="select" value="" label="Plotting layout" help="">\n-            <option value="fa">fa: ForceAtlas2</option>\n-            <option value="fr">fr: Fruchterman-Reingold</option>\n-            <option value="fr">rt: stands for Reingold Tilford</option>\n-            <option value="fr">eq_tree: equally spaced tree</option>\n-        </param>\n+        <expand macro="param_layout"/>\n         <param argument="init_pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for initializing the layout" help=""/>\n-        <param argument="random_state" type="integer" value="0" label="Random state" help="For layouts with random initialization like \'fr\', change this to use different intial states for the optimization. If `None`, the initial state is not reproducible."/>\n-        <param argument="root" type="text" value="" label="Comma-separated roots" help="If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is `None` or an empty list, the root vertices are automatically calculated based on topological sorting."/>\n+        <expand macro="param_random_state"/>\n+        <expand macro="param_root"/>\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="solid_edges" type="text" value="connectivities" label="Key corresponding to the matrix storing the edges to be drawn solid black" help="Key for uns/paga"/>\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@@ -1031,10 +972,11 @@\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 str($method.color) != \'\'\n+    #set $color=([x.strip() for x in str($method.color).split(\',\')])\n+    color=$color,\n+#end if\n #if $method.pos\n     pos=np.fromfile($method.pos, dtype=dt),\n #end if\n@@ -1081,4 +1023,10 @@\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+    <xml name="gene_symbols">\n+        <param argument="gene_symbols" type="text" value="" optional="true" label="Key for field in `.var` that stores gene symbols"/>\n+    </xml>\n+    <xml name="n_genes">\n+        <param argument="n_genes" type="integer" min="0" value="20" label="Number of genes to show" help=""/>\n+    </xml>               \n </macros>\n'
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diff -r ed64c90a9b93 -r a9f14e2d1655 normalize.xml
--- a/normalize.xml Mon Mar 04 10:16:12 2019 -0500
+++ b/normalize.xml Wed Oct 16 06:31:10 2019 -0400
[
b'@@ -1,5 +1,5 @@\n-<tool id="scanpy_normalize" name="Normalize with scanpy" version="@galaxy_version@">\n-    <description></description>\n+<tool id="scanpy_normalize" name="Normalize" version="@galaxy_version@">\n+    <description>with scanpy</description>\n     <macros>\n         <import>macros.xml</import>\n     </macros>\n@@ -13,26 +13,36 @@\n @CMD_imports@\n @CMD_read_inputs@\n \n-#if $method.method == "pp.normalize_per_cell"\n-sc.pp.normalize_per_cell(\n-    data=adata,\n-    #if $method.counts_per_cell_after\n-    counts_per_cell_after=$method.counts_per_cell_after,\n+#if $method.method == "pp.normalize_total"\n+sc.pp.normalize_total(\n+    adata,\n+    #if str($method.target_sum)!= \'\'\n+    target_sum=$method.target_sum,\n+    #end if\n+    exclude_highly_expressed=$method.exclude_highly_expressed.exclude_highly_expressed,\n+    #if $method.exclude_highly_expressed.exclude_highly_expressed == "True"\n+    max_fraction=$method.exclude_highly_expressed.max_fraction,\n     #end if\n-    #if $method.counts_per_cell\n-    counts_per_cell=np.loadtxt(\'$method.counts_per_cell\'),\n+    key_added=\'$method.key_added\',\n+    #if str($method.layers) != \'all\'\n+    layers[str(x.strip()) for x in str($method.layers).split(\',\')],\n+    #else\n+    layers=\'$method.layers\',\n     #end if\n-    key_n_counts=\'$method.key_n_counts\',\n-    copy=False)\n-adata.obs.to_csv(\'$anndata_obs\', sep=\'\\t\')\n-#elif $method.method == "pp.recipe_zheng17"\n+    #if str($method.layer_norm) != "None"\n+        layer_norm=\'$method.layer_norm\',\n+    #end if\n+    inplace=True)\n+\n+#else if $method.method == "pp.recipe_zheng17"\n sc.pp.recipe_zheng17(\n     adata=adata,\n     n_top_genes=$method.n_top_genes,\n     log=$method.log,\n     plot=False,\n     copy=False)\n-#elif $method.method == "pp.recipe_weinreb17"\n+\n+#else if $method.method == "pp.recipe_weinreb17"\n sc.pp.recipe_weinreb17(\n     adata=adata,\n     log=$method.log,\n@@ -42,34 +52,14 @@\n     svd_solver=\'$method.svd_solver\',\n     random_state=$method.random_state,\n     copy=False)\n-#elif $method.method == "pp.recipe_seurat"\n+\n+#else if $method.method == "pp.recipe_seurat"\n sc.pp.recipe_seurat(\n     adata=adata,\n     log=$method.log,\n     plot=False,\n     copy=False)\n-#elif $method.method == "pp.log1p"\n-sc.pp.log1p(\n-    data=adata,\n-    copy=False)\n-#elif $method.method == "pp.scale"\n-sc.pp.scale(\n-    data=adata,\n-    zero_center=$method.zero_center,\n-    #if $method.max_value\n-    max_value=$method.max_value,\n-    #end if\n-    copy=False)\n-#elif $method.method == "pp.sqrt"\n-sc.pp.sqrt(\n-    data=adata,\n-    copy=False)\n-#elif $method.method == "pp.downsample_counts"\n-sc.pp.downsample_counts(\n-    adata=adata,\n-    target_counts=$method.target_counts,\n-    random_state=$method.random_state,\n-    copy=False)\n+\n #end if\n \n @CMD_anndata_write_outputs@\n@@ -79,20 +69,31 @@\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.normalize_per_cell">Normalize total counts per cell, using `pp.normalize_per_cell`</option>\n+            <param argument="method" type="select" label="Method used for normalization">\n+                <option value="pp.normalize_total">Normalize counts per cell, using `pp.normalize_total`</option>\n                 <option value="pp.recipe_zheng17">Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`</option>\n                 <option value="pp.recipe_weinreb17">Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`</option>\n                 <option value="pp.recipe_seurat">Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`</option>\n-                <option value="pp.log1p">Logarithmize the data matrix, using `pp.log1p`</option>\n-                <option value="pp.scale">Scale data to unit variance and zero mean, using `pp.scale`</option>\n-                <option value="pp.sqrt">Square root th'..b'   </conditional>\n-            <param name="anndata_output_format" value="h5ad" />\n-            <assert_stdout>\n-                <has_text_matching expression="sc.pp.sqrt"/>\n-            </assert_stdout>\n-            <output name="anndata_out_h5ad" file="pp.sqrt.krumsiek11.h5ad" ftype="h5" compare="sim_size"/>\n-        </test>\n-        <test>\n-            <conditional name="input">\n-                <param name="format" value="h5ad" />\n-                <param name="adata" value="random-randint.h5ad" />\n-            </conditional>\n-            <conditional name="method">\n-                <param name="method" value="pp.downsample_counts"/>\n-                <param name="target_counts" value="20000"/>\n-                <param name="random_state" value="0"/>\n-            </conditional>\n-            <param name="anndata_output_format" value="h5ad" />\n-            <assert_stdout>\n-                <has_text_matching expression="sc.pp.downsample_counts"/>\n-                <has_text_matching expression="target_counts=20000"/>\n-                <has_text_matching expression="random_state=0"/>\n-            </assert_stdout>\n-            <output name="anndata_out_h5ad" ftype="h5">\n-                <assert_contents>\n-                    <has_h5_keys keys="X, obs, var" />\n-                </assert_contents>\n-            </output>\n-        </test>\n+        \n     </tests>\n     <help><![CDATA[\n Normalize total counts per cell (`pp.normalize_per_cell`)\n@@ -308,7 +199,7 @@\n Similar functions are used, for example, by Seurat, Cell Ranger or SPRING.\n \n More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.normalize_per_cell.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.normalize_per_cell.html>`__\n \n \n Normalization and filtering as of Zheng et al. (2017), the Cell Ranger R Kit of 10x Genomics (`pp.recipe_zheng17`)\n@@ -327,7 +218,7 @@\n - scale to unit variance and shift to zero mean\n \n More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_zheng17.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_zheng17.html>`__\n \n \n Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`)\n@@ -336,7 +227,7 @@\n Expects non-logarithmized data. If using logarithmized data, pass `log=False`.\n \n More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_weinreb17.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_weinreb17.html>`__\n \n \n Normalization and filtering as of Seurat et al (2015) (`pp.recipe_seurat`)\n@@ -347,33 +238,7 @@\n Expects non-logarithmized data. If using logarithmized data, pass `log=False`.\n \n More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_seurat.html>`__\n-\n-Logarithmize the data matrix (`pp.log1p`)\n-=========================================\n-\n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.log1p.html>`__\n-\n-Scale data to unit variance and zero mean (`pp.scale`)\n-======================================================\n-\n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.scale.html>`__\n-\n-Computes the square root the data matrix (`pp.sqrt`)\n-====================================================\n-\n-`X = sqrt(X)`\n-\n-Downsample counts (`pp.downsample_counts`)\n-==========================================\n-\n-Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This\n-has been implemented by M. D. Luecken.\n-\n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.downsample_counts.html>`__\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_seurat.html>`__\n \n     ]]></help>\n     <expand macro="citations"/>\n'
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.stacked_violin.krumsiek11.png
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_cells.krumsiek11-max_genes.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_cells.krumsiek11-min_counts.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular
--- a/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
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b
diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
b
Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular
--- a/test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
@@ -1,12 +0,0 @@
-index n_counts
-Gata2 163.95355
-Gata1 203.95117
-Fog1 83.94181
-EKLF 70.69286
-Fli1 57.56072
-SCL 202.67444
-Cebpa 469.87094
-Pu.1 250.78569
-cJun 188.10158
-EgrNab 164.99693
-Gfi1 159.99155
b
diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular
--- a/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
@@ -1,222 +0,0 @@
- gene_subset number_per_gene
-0 True 34
-1 True 123
-2 True 281
-3 True 54
-4 True 253
-5 True 63
-6 True 9
-7 True 266
-8 True 101
-9 True 233
-10 True 267
-11 True 285
-12 True 332
-13 True 197
-14 True 158
-15 True 64
-16 True 285
-17 True 229
-18 True 43
-19 True 199
-20 True 271
-21 True 318
-22 True 132
-23 True 83
-24 True 88
-25 True 87
-26 True 71
-27 True 258
-28 True 58
-29 True 348
-30 True 280
-31 True 150
-32 True 121
-33 True 237
-34 True 29
-35 True 220
-36 True 103
-37 True 87
-38 True 115
-39 True 100
-40 True 139
-41 True 23
-42 True 162
-43 True 76
-44 True 180
-45 True 51
-46 True 244
-47 True 132
-48 True 244
-49 True 82
-50 True 172
-51 True 27
-52 True 100
-53 True 327
-54 True 277
-55 True 282
-56 True 245
-57 True 21
-58 True 52
-59 True 19
-60 True 227
-61 True 288
-62 True 274
-63 True 301
-64 True 316
-65 True 314
-66 True 271
-67 True 270
-68 True 283
-69 True 245
-70 True 263
-71 True 312
-72 True 285
-73 True 228
-74 True 170
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular Mon Mar 04 10:16:12 2019 -0500
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@@ -1,12 +0,0 @@
- gene_subset means dispersions dispersions_norm
-0 False 0.22807331 -1.513815
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-2 False 0.12324284 -1.1931922
-3 True 0.10477218 -0.8270577 0.67448974
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-7 False 0.3306357 -0.91260546
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-9 False 0.22937028 -0.7354343
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular Mon Mar 04 10:16:12 2019 -0500
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@@ -1,9 +0,0 @@
-index means dispersions dispersions_norm
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-EKLF 0.10477218 -0.8270577 0.70710677
-SCL 0.2751125 -0.6042374 0.707108
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-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 ed64c90a9b93 -r a9f14e2d1655 test-data/pp.normalize_per_cell.obs.krumsiek11.tabular
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:16:12 2019 -0500
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@@ -1,641 +0,0 @@
-index cell_type
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-0 progenitor
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-87 Ery
-88 Ery
-89 Ery
-90 Ery
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-0 progenitor
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-87 Mk
-88 Mk
-89 Mk
-90 Mk
-91 Mk
-92 Mk
-93 Mk
-94 Mk
-95 Mk
-96 Mk
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-147 Mk
-148 Mk
-149 Mk
-150 Mk
-151 Mk
-152 Mk
-153 Mk
-154 Mk
-155 Mk
-156 Mk
-157 Mk
-158 Mk
-159 Mk
-0 progenitor
-1 progenitor
-2 progenitor
-3 progenitor
-4 progenitor
-5 progenitor
-6 progenitor
-7 progenitor
-8 progenitor
-9 progenitor
-10 progenitor
-11 progenitor
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-80 Neu
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--- a/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular Mon Mar 04 10:16:12 2019 -0500
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular
--- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:16:12 2019 -0500
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@@ -1,101 +0,0 @@
-index paul15_clusters dpt_groups dpt_order dpt_order_indices
-578 13Baso 2 53 27
-2242 3Ery 1 30 46
-2690 10GMP 2 66 45
-70 5Ery 1 32 65
-758 15Mo 2 67 8
-465 16Neu 2 68 80
-245 16Neu 2 69 87
-2172 10GMP 2 70 90
-2680 10GMP 0 4 36
-1790 7MEP 2 71 59
-855 11DC 2 72 82
-2721 10GMP 2 73 30
-104 2Ery 1 38 62
-1106 2Ery 1 40 32
-2367 15Mo 3 93 35
-124 2Ery 1 41 37
-2477 8Mk 2 74 31
-1968 2Ery 1 42 78
-563 1Ery 1 43 28
-276 2Ery 1 44 56
-192 16Neu 2 75 42
-2409 2Ery 1 45 44
-2054 15Mo 3 95 75
-720 8Mk 2 76 48
-2225 14Mo 3 97 98
-878 6Ery 1 29 54
-156 7MEP 2 77 79
-1244 8Mk 0 0 40
-10 2Ery 1 18 83
-1108 6Ery 2 65 25
-353 5Ery 1 11 1
-182 5Ery 1 16 97
-2053 3Ery 1 13 3
-2291 16Neu 3 92 96
-2056 10GMP 2 79 95
-1047 2Ery 1 14 94
-1947 14Mo 0 8 92
-1390 3Ery 1 15 60
-2317 14Mo 2 90 12
-2348 11DC 2 82 69
-953 5Ery 1 27 13
-628 9GMP 2 83 15
-2691 5Ery 1 20 17
-1499 16Neu 3 96 18
-1083 2Ery 1 21 19
-831 14Mo 0 2 21
-15 7MEP 0 1 86
-2005 7MEP 2 87 66
-1662 3Ery 1 23 84
-2457 7MEP 2 64 89
-757 7MEP 2 81 70
-1642 14Mo 2 91 68
-2520 10GMP 2 89 67
-1393 7MEP 2 88 0
-2170 6Ery 1 25 73
-988 14Mo 2 86 76
-1338 2Ery 1 19 77
-2189 16Neu 2 85 81
-446 13Baso 2 84 85
-2276 14Mo 0 9 88
-317 2Ery 1 37 91
-1540 16Neu 3 99 93
-2164 4Ery 1 12 72
-227 15Mo 2 78 64
-906 12Baso 2 63 49
-716 15Mo 0 3 29
-912 14Mo 1 47 2
-2688 11DC 2 52 4
-1678 7MEP 2 51 5
-1063 6Ery 1 39 6
-1041 5Ery 1 50 7
-2279 15Mo 3 98 9
-558 13Baso 2 62 10
-2196 14Mo 2 54 11
-1270 13Baso 3 94 16
-2259 3Ery 1 22 20
-2410 13Baso 2 55 23
-886 7MEP 2 56 26
-2072 13Baso 1 17 63
-443 5Ery 1 26 34
-910 13Baso 0 5 99
-2608 15Mo 2 57 50
-2645 1Ery 1 10 39
-616 6Ery 1 28 41
-1866 2Ery 1 48 58
-923 7MEP 2 58 57
-1716 4Ery 1 46 55
-2476 11DC 0 6 47
-1872 10GMP 2 59 53
-1009 4Ery 1 49 52
-1680 6Ery 0 7 38
-1490 14Mo 2 60 51
-1454 2Ery 1 36 33
-2580 9GMP 2 61 14
-958 1Ery 1 35 74
-2626 2Ery 1 34 22
-1677 3Ery 1 33 43
-982 4Ery 1 31 24
-202 2Ery 1 24 71
-891 10GMP 2 80 61
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.pca.krumsiek11.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
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b'@@ -1,641 +0,0 @@\n-index\tcell_type\tscore\n-0\tprogenitor\t\n-1\tprogenitor\t\n-2\tprogenitor\t\n-3\tprogenitor\t\n-4\tprogenitor\t\n-5\tprogenitor\t\n-6\tprogenitor\t\n-7\tprogenitor\t\n-8\tprogenitor\t\n-9\tprogenitor\t\n-10\tprogenitor\t\n-11\tprogenitor\t\n-12\tprogenitor\t\n-13\tprogenitor\t\n-14\tprogenitor\t\n-15\tprogenitor\t\n-16\tprogenitor\t\n-17\tprogenitor\t\n-18\tprogenitor\t\n-19\tprogenitor\t\n-20\tprogenitor\t\n-21\tprogenitor\t\n-22\tprogenitor\t\n-23\tprogenitor\t\n-24\tprogenitor\t\n-25\tprogenitor\t\n-26\tprogenitor\t\n-27\tprogenitor\t\n-28\tprogenitor\t\n-29\tprogenitor\t\n-30\tprogenitor\t\n-31\tprogenitor\t\n-32\tprogenitor\t\n-33\tprogenitor\t\n-34\tprogenitor\t\n-35\tprogenitor\t\n-36\tprogenitor\t\n-37\tprogenitor\t\n-38\tprogenitor\t\n-39\tprogenitor\t\n-40\tprogenitor\t\n-41\tprogenitor\t\n-42\tprogenitor\t\n-43\tprogenitor\t\n-44\tprogenitor\t\n-45\tprogenitor\t\n-46\tprogenitor\t\n-47\tprogenitor\t\n-48\tprogenitor\t\n-49\tprogenitor\t\n-50\tprogenitor\t\n-51\tprogenitor\t\n-52\tprogenitor\t\n-53\tprogenitor\t\n-54\tprogenitor\t\n-55\tprogenitor\t\n-56\tprogenitor\t\n-57\tprogenitor\t\n-58\tprogenitor\t\n-59\tprogenitor\t\n-60\tprogenitor\t\n-61\tprogenitor\t\n-62\tprogenitor\t\n-63\tprogenitor\t\n-64\tprogenitor\t\n-65\tprogenitor\t\n-66\tprogenitor\t\n-67\tprogenitor\t\n-68\tprogenitor\t\n-69\tprogenitor\t\n-70\tprogenitor\t\n-71\tprogenitor\t\n-72\tprogenitor\t\n-73\tprogenitor\t\n-74\tprogenitor\t\n-75\tprogenitor\t\n-76\tprogenitor\t\n-77\tprogenitor\t\n-78\tprogenitor\t\n-79\tprogenitor\t\n-80\tMo\t\n-81\tMo\t\n-82\tMo\t\n-83\tMo\t\n-84\tMo\t\n-85\tMo\t\n-86\tMo\t\n-87\tMo\t\n-88\tMo\t\n-89\tMo\t\n-90\tMo\t\n-91\tMo\t\n-92\tMo\t\n-93\tMo\t\n-94\tMo\t\n-95\tMo\t\n-96\tMo\t\n-97\tMo\t\n-98\tMo\t\n-99\tMo\t\n-100\tMo\t\n-101\tMo\t\n-102\tMo\t\n-103\tMo\t\n-104\tMo\t\n-105\tMo\t\n-106\tMo\t\n-107\tMo\t\n-108\tMo\t\n-109\tMo\t\n-110\tMo\t\n-111\tMo\t\n-112\tMo\t\n-113\tMo\t\n-114\tMo\t\n-115\tMo\t\n-116\tMo\t\n-117\tMo\t\n-118\tMo\t\n-119\tMo\t\n-120\tMo\t\n-121\tMo\t\n-122\tMo\t\n-123\tMo\t\n-124\tMo\t\n-125\tMo\t\n-126\tMo\t\n-127\tMo\t\n-128\tMo\t\n-129\tMo\t\n-130\tMo\t\n-131\tMo\t\n-132\tMo\t\n-133\tMo\t\n-134\tMo\t\n-135\tMo\t\n-136\tMo\t\n-137\tMo\t\n-138\tMo\t\n-139\tMo\t\n-140\tMo\t\n-141\tMo\t\n-142\tMo\t\n-143\tMo\t\n-144\tMo\t\n-145\tMo\t\n-146\tMo\t\n-147\tMo\t\n-148\tMo\t\n-149\tMo\t\n-150\tMo\t\n-151\tMo\t\n-152\tMo\t\n-153\tMo\t\n-154\tMo\t\n-155\tMo\t\n-156\tMo\t\n-157\tMo\t\n-158\tMo\t\n-159\tMo\t\n-0\tprogenitor\t\n-1\tprogenitor\t\n-2\tprogenitor\t\n-3\tprogenitor\t\n-4\tprogenitor\t\n-5\tprogenitor\t\n-6\tprogenitor\t\n-7\tprogenitor\t\n-8\tprogenitor\t\n-9\tprogenitor\t\n-10\tprogenitor\t\n-11\tprogenitor\t\n-12\tprogenitor\t\n-13\tprogenitor\t\n-14\tprogenitor\t\n-15\tprogenitor\t\n-16\tprogenitor\t\n-17\tprogenitor\t\n-18\tprogenitor\t\n-19\tprogenitor\t\n-20\tprogenitor\t\n-21\tprogenitor\t\n-22\tprogenitor\t\n-23\tprogenitor\t\n-24\tprogenitor\t\n-25\tprogenitor\t\n-26\tprogenitor\t\n-27\tprogenitor\t\n-28\tprogenitor\t\n-29\tprogenitor\t\n-30\tprogenitor\t\n-31\tprogenitor\t\n-32\tprogenitor\t\n-33\tprogenitor\t\n-34\tprogenitor\t\n-35\tprogenitor\t\n-36\tprogenitor\t\n-37\tprogenitor\t\n-38\tprogenitor\t\n-39\tprogenitor\t\n-40\tprogenitor\t\n-41\tprogenitor\t\n-42\tprogenitor\t\n-43\tprogenitor\t\n-44\tprogenitor\t\n-45\tprogenitor\t\n-46\tprogenitor\t\n-47\tprogenitor\t\n-48\tprogenitor\t\n-49\tprogenitor\t\n-50\tprogenitor\t\n-51\tprogenitor\t\n-52\tprogenitor\t\n-53\tprogenitor\t\n-54\tprogenitor\t\n-55\tprogenitor\t\n-56\tprogenitor\t\n-57\tprogenitor\t\n-58\tprogenitor\t\n-59\tprogenitor\t\n-60\tprogenitor\t\n-61\tprogenitor\t\n-62\tprogenitor\t\n-63\tprogenitor\t\n-64\tprogenitor\t\n-65\tprogenitor\t\n-66\tprogenitor\t\n-67\tprogenitor\t\n-68\tprogenitor\t\n-69\tprogenitor\t\n-70\tprogenitor\t\n-71\tprogenitor\t\n-72\tprogenitor\t\n-73\tprogenitor\t\n-74\tprogenitor\t\n-75\tprogenitor\t\n-76\tprogenitor\t\n-77\tprogenitor\t\n-78\tprogenitor\t\n-79\tprogenitor\t\n-80\tEry\t\n-81\tEry\t\n-82\tEry\t\n-83\tEry\t\n-84\tEry\t\n-85\tEry\t\n-86\tEry\t\n-87\tEry\t\n-88\tEry\t\n-89\tEry\t\n-90\tEry\t\n-91\tEry\t\n-92\tEry\t\n-93\tEry\t\n-94\tEry\t\n-95\tEry\t\n-96\tEry\t\n-97\tEry\t\n-98\tEry\t\n-99\tEry\t\n-100\tEry\t\n-101\tEry\t\n-102\tEry\t\n-103\tEry\t\n-104\tEry\t\n-105\tEry\t\n-106\tEry\t\n-107\tEry\t\n-108\tEry\t\n-109\tEry\t\n-110\tEry\t\n-111\tEry\t\n-112\tEry\t\n-113\tEry\t\n-114\tEry\t\n-115\tEry\t\n-116\tEry\t\n-117\tEry\t\n-118\tEry\t\n-119\tEry\t\n-120\tEry\t\n-121\tEry\t\n-122\tEry\t\n-123\tEry\t\n-124\tEry\t\n-125\tEry\t\n-126\tEry\t\n-127\tEry\t\n-128\tEry\t\n-129\tEry\t\n-130\tEry\t\n-131\tEry\t\n-132\tEry\t\n-133\tEry\t\n-134\tEry\t\n-135\tEry\t\n-136\tEry\t\n-137\tEry\t\n-138\tEry\t\n-139\tEry\t\n-140\tEry\t\n-141\tEry\t\n-142\tEry\t\n-143\tEry\t\n-144\tEry\t\n-145\tEry\t\n-146\tEry\t\n-147\tEry\t\n-148\tEry\t\n-149\tEry\t\n-150\tEry\t\n-151\tEry\t\n-152\tEry\t\n-153\tEry'..b'genitor\t\n-2\tprogenitor\t\n-3\tprogenitor\t\n-4\tprogenitor\t\n-5\tprogenitor\t\n-6\tprogenitor\t\n-7\tprogenitor\t\n-8\tprogenitor\t\n-9\tprogenitor\t\n-10\tprogenitor\t\n-11\tprogenitor\t\n-12\tprogenitor\t\n-13\tprogenitor\t\n-14\tprogenitor\t\n-15\tprogenitor\t\n-16\tprogenitor\t\n-17\tprogenitor\t\n-18\tprogenitor\t\n-19\tprogenitor\t\n-20\tprogenitor\t\n-21\tprogenitor\t\n-22\tprogenitor\t\n-23\tprogenitor\t\n-24\tprogenitor\t\n-25\tprogenitor\t\n-26\tprogenitor\t\n-27\tprogenitor\t\n-28\tprogenitor\t\n-29\tprogenitor\t\n-30\tprogenitor\t\n-31\tprogenitor\t\n-32\tprogenitor\t\n-33\tprogenitor\t\n-34\tprogenitor\t\n-35\tprogenitor\t\n-36\tprogenitor\t\n-37\tprogenitor\t\n-38\tprogenitor\t\n-39\tprogenitor\t\n-40\tprogenitor\t\n-41\tprogenitor\t\n-42\tprogenitor\t\n-43\tprogenitor\t\n-44\tprogenitor\t\n-45\tprogenitor\t\n-46\tprogenitor\t\n-47\tprogenitor\t\n-48\tprogenitor\t\n-49\tprogenitor\t\n-50\tprogenitor\t\n-51\tprogenitor\t\n-52\tprogenitor\t\n-53\tprogenitor\t\n-54\tprogenitor\t\n-55\tprogenitor\t\n-56\tprogenitor\t\n-57\tprogenitor\t\n-58\tprogenitor\t\n-59\tprogenitor\t\n-60\tprogenitor\t\n-61\tprogenitor\t\n-62\tprogenitor\t\n-63\tprogenitor\t\n-64\tprogenitor\t\n-65\tprogenitor\t\n-66\tprogenitor\t\n-67\tprogenitor\t\n-68\tprogenitor\t\n-69\tprogenitor\t\n-70\tprogenitor\t\n-71\tprogenitor\t\n-72\tprogenitor\t\n-73\tprogenitor\t\n-74\tprogenitor\t\n-75\tprogenitor\t\n-76\tprogenitor\t\n-77\tprogenitor\t\n-78\tprogenitor\t\n-79\tprogenitor\t\n-80\tMk\t\n-81\tMk\t\n-82\tMk\t\n-83\tMk\t\n-84\tMk\t\n-85\tMk\t\n-86\tMk\t\n-87\tMk\t\n-88\tMk\t\n-89\tMk\t\n-90\tMk\t\n-91\tMk\t\n-92\tMk\t\n-93\tMk\t\n-94\tMk\t\n-95\tMk\t\n-96\tMk\t\n-97\tMk\t\n-98\tMk\t\n-99\tMk\t\n-100\tMk\t\n-101\tMk\t\n-102\tMk\t\n-103\tMk\t\n-104\tMk\t\n-105\tMk\t\n-106\tMk\t\n-107\tMk\t\n-108\tMk\t\n-109\tMk\t\n-110\tMk\t\n-111\tMk\t\n-112\tMk\t\n-113\tMk\t\n-114\tMk\t\n-115\tMk\t\n-116\tMk\t\n-117\tMk\t\n-118\tMk\t\n-119\tMk\t\n-120\tMk\t\n-121\tMk\t\n-122\tMk\t\n-123\tMk\t\n-124\tMk\t\n-125\tMk\t\n-126\tMk\t\n-127\tMk\t\n-128\tMk\t\n-129\tMk\t\n-130\tMk\t\n-131\tMk\t\n-132\tMk\t\n-133\tMk\t\n-134\tMk\t\n-135\tMk\t\n-136\tMk\t\n-137\tMk\t\n-138\tMk\t\n-139\tMk\t\n-140\tMk\t\n-141\tMk\t\n-142\tMk\t\n-143\tMk\t\n-144\tMk\t\n-145\tMk\t\n-146\tMk\t\n-147\tMk\t\n-148\tMk\t\n-149\tMk\t\n-150\tMk\t\n-151\tMk\t\n-152\tMk\t\n-153\tMk\t\n-154\tMk\t\n-155\tMk\t\n-156\tMk\t\n-157\tMk\t\n-158\tMk\t\n-159\tMk\t\n-0\tprogenitor\t\n-1\tprogenitor\t\n-2\tprogenitor\t\n-3\tprogenitor\t\n-4\tprogenitor\t\n-5\tprogenitor\t\n-6\tprogenitor\t\n-7\tprogenitor\t\n-8\tprogenitor\t\n-9\tprogenitor\t\n-10\tprogenitor\t\n-11\tprogenitor\t\n-12\tprogenitor\t\n-13\tprogenitor\t\n-14\tprogenitor\t\n-15\tprogenitor\t\n-16\tprogenitor\t\n-17\tprogenitor\t\n-18\tprogenitor\t\n-19\tprogenitor\t\n-20\tprogenitor\t\n-21\tprogenitor\t\n-22\tprogenitor\t\n-23\tprogenitor\t\n-24\tprogenitor\t\n-25\tprogenitor\t\n-26\tprogenitor\t\n-27\tprogenitor\t\n-28\tprogenitor\t\n-29\tprogenitor\t\n-30\tprogenitor\t\n-31\tprogenitor\t\n-32\tprogenitor\t\n-33\tprogenitor\t\n-34\tprogenitor\t\n-35\tprogenitor\t\n-36\tprogenitor\t\n-37\tprogenitor\t\n-38\tprogenitor\t\n-39\tprogenitor\t\n-40\tprogenitor\t\n-41\tprogenitor\t\n-42\tprogenitor\t\n-43\tprogenitor\t\n-44\tprogenitor\t\n-45\tprogenitor\t\n-46\tprogenitor\t\n-47\tprogenitor\t\n-48\tprogenitor\t\n-49\tprogenitor\t\n-50\tprogenitor\t\n-51\tprogenitor\t\n-52\tprogenitor\t\n-53\tprogenitor\t\n-54\tprogenitor\t\n-55\tprogenitor\t\n-56\tprogenitor\t\n-57\tprogenitor\t\n-58\tprogenitor\t\n-59\tprogenitor\t\n-60\tprogenitor\t\n-61\tprogenitor\t\n-62\tprogenitor\t\n-63\tprogenitor\t\n-64\tprogenitor\t\n-65\tprogenitor\t\n-66\tprogenitor\t\n-67\tprogenitor\t\n-68\tprogenitor\t\n-69\tprogenitor\t\n-70\tprogenitor\t\n-71\tprogenitor\t\n-72\tprogenitor\t\n-73\tprogenitor\t\n-74\tprogenitor\t\n-75\tprogenitor\t\n-76\tprogenitor\t\n-77\tprogenitor\t\n-78\tprogenitor\t\n-79\tprogenitor\t\n-80\tNeu\t\n-81\tNeu\t\n-82\tNeu\t\n-83\tNeu\t\n-84\tNeu\t\n-85\tNeu\t\n-86\tNeu\t\n-87\tNeu\t\n-88\tNeu\t\n-89\tNeu\t\n-90\tNeu\t\n-91\tNeu\t\n-92\tNeu\t\n-93\tNeu\t\n-94\tNeu\t\n-95\tNeu\t\n-96\tNeu\t\n-97\tNeu\t\n-98\tNeu\t\n-99\tNeu\t\n-100\tNeu\t\n-101\tNeu\t\n-102\tNeu\t\n-103\tNeu\t\n-104\tNeu\t\n-105\tNeu\t\n-106\tNeu\t\n-107\tNeu\t\n-108\tNeu\t\n-109\tNeu\t\n-110\tNeu\t\n-111\tNeu\t\n-112\tNeu\t\n-113\tNeu\t\n-114\tNeu\t\n-115\tNeu\t\n-116\tNeu\t\n-117\tNeu\t\n-118\tNeu\t\n-119\tNeu\t\n-120\tNeu\t\n-121\tNeu\t\n-122\tNeu\t\n-123\tNeu\t\n-124\tNeu\t\n-125\tNeu\t\n-126\tNeu\t\n-127\tNeu\t\n-128\tNeu\t\n-129\tNeu\t\n-130\tNeu\t\n-131\tNeu\t\n-132\tNeu\t\n-133\tNeu\t\n-134\tNeu\t\n-135\tNeu\t\n-136\tNeu\t\n-137\tNeu\t\n-138\tNeu\t\n-139\tNeu\t\n-140\tNeu\t\n-141\tNeu\t\n-142\tNeu\t\n-143\tNeu\t\n-144\tNeu\t\n-145\tNeu\t\n-146\tNeu\t\n-147\tNeu\t\n-148\tNeu\t\n-149\tNeu\t\n-150\tNeu\t\n-151\tNeu\t\n-152\tNeu\t\n-153\tNeu\t\n-154\tNeu\t\n-155\tNeu\t\n-156\tNeu\t\n-157\tNeu\t\n-158\tNeu\t\n-159\tNeu\t\n'
b
diff -r ed64c90a9b93 -r a9f14e2d1655 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 ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:16:12 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
b'@@ -1,641 +0,0 @@\n-index\tcell_type\tS_score\tG2M_score\tphase\n-0\tprogenitor\t0.2681\t0.20055\tS\n-1\tprogenitor\t0.24346666\t0.15855001\tS\n-2\tprogenitor\t0.2276\t0.13482499\tS\n-3\tprogenitor\t0.21043333\t0.12637499\tS\n-4\tprogenitor\t0.19113334\t0.1272\tS\n-5\tprogenitor\t0.17531666\t0.13072497\tS\n-6\tprogenitor\t0.16073334\t0.13242501\tS\n-7\tprogenitor\t0.15353334\t0.13672501\tS\n-8\tprogenitor\t0.14314999\t0.1399\tS\n-9\tprogenitor\t0.1337\t0.14515\tG2M\n-10\tprogenitor\t0.12695001\t0.15165001\tG2M\n-11\tprogenitor\t0.11726667\t0.16077498\tG2M\n-12\tprogenitor\t0.11081667\t0.16735\tG2M\n-13\tprogenitor\t0.104849994\t0.17429999\tG2M\n-14\tprogenitor\t0.09816667\t0.18152499\tG2M\n-15\tprogenitor\t0.095350005\t0.186625\tG2M\n-16\tprogenitor\t0.09528333\t0.19447501\tG2M\n-17\tprogenitor\t0.09463333\t0.199675\tG2M\n-18\tprogenitor\t0.0947\t0.205275\tG2M\n-19\tprogenitor\t0.0947\t0.20802501\tG2M\n-20\tprogenitor\t0.097733326\t0.21100001\tG2M\n-21\tprogenitor\t0.09881667\t0.21964999\tG2M\n-22\tprogenitor\t0.10131666\t0.22662501\tG2M\n-23\tprogenitor\t0.104849994\t0.23022501\tG2M\n-24\tprogenitor\t0.112266675\t0.23387499\tG2M\n-25\tprogenitor\t0.120283335\t0.2393\tG2M\n-26\tprogenitor\t0.12826668\t0.24174997\tG2M\n-27\tprogenitor\t0.13323334\t0.24710001\tG2M\n-28\tprogenitor\t0.13971666\t0.25280002\tG2M\n-29\tprogenitor\t0.14393334\t0.256775\tG2M\n-30\tprogenitor\t0.15066667\t0.259775\tG2M\n-31\tprogenitor\t0.15316668\t0.26244998\tG2M\n-32\tprogenitor\t0.15993333\t0.26487502\tG2M\n-33\tprogenitor\t0.16430001\t0.266275\tG2M\n-34\tprogenitor\t0.16598332\t0.270625\tG2M\n-35\tprogenitor\t0.17068332\t0.2715\tG2M\n-36\tprogenitor\t0.17713334\t0.276475\tG2M\n-37\tprogenitor\t0.17893334\t0.27514997\tG2M\n-38\tprogenitor\t0.18013333\t0.278025\tG2M\n-39\tprogenitor\t0.18251666\t0.279675\tG2M\n-40\tprogenitor\t0.18876666\t0.27925\tG2M\n-41\tprogenitor\t0.19041668\t0.281775\tG2M\n-42\tprogenitor\t0.19083333\t0.2824\tG2M\n-43\tprogenitor\t0.19411668\t0.281725\tG2M\n-44\tprogenitor\t0.19639999\t0.2844\tG2M\n-45\tprogenitor\t0.19843334\t0.285375\tG2M\n-46\tprogenitor\t0.20406666\t0.284075\tG2M\n-47\tprogenitor\t0.20673332\t0.28625\tG2M\n-48\tprogenitor\t0.20769998\t0.2885\tG2M\n-49\tprogenitor\t0.21186668\t0.28935\tG2M\n-50\tprogenitor\t0.21285\t0.28867498\tG2M\n-51\tprogenitor\t0.21443334\t0.28855002\tG2M\n-52\tprogenitor\t0.21568334\t0.28705\tG2M\n-53\tprogenitor\t0.21788335\t0.29035\tG2M\n-54\tprogenitor\t0.22551665\t0.28815\tG2M\n-55\tprogenitor\t0.22586668\t0.28689998\tG2M\n-56\tprogenitor\t0.23069999\t0.2816\tG2M\n-57\tprogenitor\t0.23118332\t0.282375\tG2M\n-58\tprogenitor\t0.23160002\t0.28230003\tG2M\n-59\tprogenitor\t0.23546667\t0.28329998\tG2M\n-60\tprogenitor\t0.23661667\t0.28195\tG2M\n-61\tprogenitor\t0.24134998\t0.27899998\tG2M\n-62\tprogenitor\t0.24546666\t0.27855\tG2M\n-63\tprogenitor\t0.24836665\t0.27609998\tG2M\n-64\tprogenitor\t0.25375\t0.27562502\tG2M\n-65\tprogenitor\t0.25834998\t0.273525\tG2M\n-66\tprogenitor\t0.26393333\t0.27015\tG2M\n-67\tprogenitor\t0.26746666\t0.26622498\tS\n-68\tprogenitor\t0.2706333\t0.267025\tS\n-69\tprogenitor\t0.27618334\t0.2651\tS\n-70\tprogenitor\t0.28033334\t0.263975\tS\n-71\tprogenitor\t0.2868167\t0.2622\tS\n-72\tprogenitor\t0.29141667\t0.26174998\tS\n-73\tprogenitor\t0.29198334\t0.26385\tS\n-74\tprogenitor\t0.29348332\t0.26275003\tS\n-75\tprogenitor\t0.29788333\t0.263575\tS\n-76\tprogenitor\t0.30125\t0.26232502\tS\n-77\tprogenitor\t0.29955\t0.261825\tS\n-78\tprogenitor\t0.30065\t0.2623\tS\n-79\tprogenitor\t0.30573332\t0.2588\tS\n-80\tMo\t0.30818334\t0.25555003\tS\n-81\tMo\t0.31073332\t0.25422502\tS\n-82\tMo\t0.31378332\t0.25410002\tS\n-83\tMo\t0.31268334\t0.25304997\tS\n-84\tMo\t0.31355\t0.25059998\tS\n-85\tMo\t0.3157\t0.251275\tS\n-86\tMo\t0.3139333\t0.25072497\tS\n-87\tMo\t0.3151833\t0.25165\tS\n-88\tMo\t0.3149333\t0.25079998\tS\n-89\tMo\t0.31440002\t0.25172502\tS\n-90\tMo\t0.31251666\t0.254725\tS\n-91\tMo\t0.31613332\t0.25347498\tS\n-92\tMo\t0.31813332\t0.25372502\tS\n-93\tMo\t0.31543335\t0.25340003\tS\n-94\tMo\t0.31663334\t0.257025\tS\n-95\tMo\t0.31793332\t0.25435\tS\n-96\tMo\t0.3184333\t0.2527\tS\n-97\tMo\t0.31743336\t0.25052497\tS\n-98\tMo\t0.3164667\t0.24747501\tS\n-99\tMo\t0.31841668\t0.2466\tS\n-100\tMo\t0.31648335\t0.24679999\tS\n-101\tMo\t0.31504998\t0.2501\tS\n-102\tMo\t0.31489998\t0.250375\tS\n-103\tMo\t0.31256667\t0.25195\tS\n-104\tMo\t0.31425\t0.250675\tS\n-105\tMo\t0.31441668\t0.248675\tS\n-106\tMo\t0.31828332\t0.24724999\tS\n-107\tMo\t0.32236665\t0.25105\tS\n-108\tMo\t0.32341668\t0.2527\tS\n-109\tMo\t0.32334998\t'..b'25013\tG2M\n-51\tprogenitor\t9.999983e-05\t-0.0017999709\tS\n-52\tprogenitor\t0.0018333336\t-0.01987499\tS\n-53\tprogenitor\t0.00090000033\t-0.032900006\tS\n-54\tprogenitor\t0.0029999996\t-0.05064997\tS\n-55\tprogenitor\t0.003983334\t-0.068574995\tS\n-56\tprogenitor\t-0.0008500004\t-0.08140004\tG1\n-57\tprogenitor\t-0.0029833335\t-0.09470001\tG1\n-58\tprogenitor\t-0.0021333336\t-0.106824994\tG1\n-59\tprogenitor\t-0.0015000002\t-0.115550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diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.tsne.krumsiek11.h5ad
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diff -r ed64c90a9b93 -r a9f14e2d1655 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