# HG changeset patch
# User iuc
# Date 1551712465 18000
# Node ID 397d2c97af057eab99f17a86ff9feea3a06aaa74
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
diff -r 000000000000 -r 397d2c97af05 README.md
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md Mon Mar 04 10:14:25 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
diff -r 000000000000 -r 397d2c97af05 README.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst Mon Mar 04 10:14:25 2019 -0500
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+The different methods from Scanpy have been grouped by themes:
+
+1. Filter in `filter.xml`
+ - Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`
+ - Filter genes based on number of cells or counts, using `pp.filter_genes`
+ - Extract highly variable genes, using `pp.filter_genes_dispersion`
+ - `tl.highly_variable_genes` (need to be added)
+ - Subsample to a fraction of the number of observations, using `pp.subsample`
+ - `queries.gene_coordinates` (need to be added)
+ - `queries.mitochondrial_genes` (need to be added)
+
+2. Normalize in `normalize.xml`
+ - Normalize total counts per cell, using `pp.normalize_per_cell`
+ - Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`
+ - Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`
+ - Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`
+ - Logarithmize the data matrix, using `pp.log1p`
+ - Scale data to unit variance and zero mean, using `pp.scale`
+ - Square root the data matrix, using `pp.sqrt`
+ - Downsample counts, using `pp.downsample_counts`
+
+3. Remove confounder in `remove_confounders.xml`
+ - Regress out unwanted sources of variation, using `pp.regress_out`
+ - `pp.mnn_correct` (need to be added)
+ - `pp.mnn_correct` (need to be added)
+ - `pp.magic` (need to be added)
+ - `tl.sim` (need to be added)
+ - `pp.calculate_qc_metrics` (need to be added)
+ - Score a set of genes, using `tl.score_genes`
+ - Score cell cycle genes, using `tl.score_genes_cell_cycle`
+ - `tl.cyclone` (need to be added)
+ - `tl.andbag` (need to be added)
+
+4. Cluster and reduce dimension in `cluster_reduce_dimension.xml`
+ - `tl.leiden` (need to be added)
+ - Cluster cells into subgroups, using `tl.louvain`
+ - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
+ - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
+ - Diffusion Maps, using `tl.diffmap`
+ - t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
+ - Embed the neighborhood graph using UMAP, using `tl.umap`
+ - `tl.phate` (need to be added)
+ - Compute a neighborhood graph of observations, using `pp.neighbors`
+ - Rank genes for characterizing groups, using `tl.rank_genes_groups`
+
+4. Inspect
+ - `tl.paga_compare_paths` (need to be added)
+ - `tl.paga_degrees` (need to be added)
+ - `tl.paga_expression_entropies` (need to be added)
+ - Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`
+ - Infer progression of cells through geodesic distance along the graph, using `tl.dpt`
+
+5. Plot
+ 1. Generic
+ - Scatter plot along observations or variables axes, using `pl.scatter`
+ - Heatmap of the expression values of set of genes, using `pl.heatmap`
+ - Makes a dot plot of the expression values, using `pl.dotplot`
+ - Violin plot, using `pl.violin`
+ - `pl.stacked_violin` (need to be added)
+ - Heatmap of the mean expression values per cluster, using `pl.matrixplot`
+ - Hierarchically-clustered heatmap, using `pl.clustermap`
+ - `pl.ranking`
+
+ 2. Preprocessing
+ - Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes`
+ - Plot dispersions versus means for genes, using `pl.filter_genes_dispersion`
+ - `pl.highly_variable_genes` (need to be added)
+ - `pl.calculate_qc_metrics` (need to be added)
+
+ 3. PCA
+ - Scatter plot in PCA coordinates, using `pl.pca`
+ - Rank genes according to contributions to PCs, using `pl.pca_loadings`
+ - Scatter plot in PCA coordinates, using `pl.pca_variance_ratio`
+ - Plot PCA results, using `pl.pca_overview`
+
+ 4. Embeddings
+ - Scatter plot in tSNE basis, using `pl.tsne`
+ - Scatter plot in UMAP basis, using `pl.umap`
+ - Scatter plot in Diffusion Map basis, using `pl.diffmap`
+ - `pl.draw_graph` (need to be added)
+
+ 5. Branching trajectories and pseudotime, clustering
+ - Plot groups and pseudotime, using `pl.dpt_groups_pseudotime`
+ - Heatmap of pseudotime series, using `pl.dpt_timeseries`
+ - Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga`
+ - `pl.paga_compare` (need to be added)
+ - `pl.paga_path` (need to be added)
+
+ 6. Marker genes:
+ - Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups`
+ - `pl.rank_genes_groups_dotplot` (need to be added)
+ - `pl.rank_genes_groups_heatmap` (need to be added)
+ - `pl.rank_genes_groups_matrixplot` (need to be added)
+ - `pl.rank_genes_groups_stacked_violin` (need to be added)
+ - `pl.rank_genes_groups_violin` (need to be added)
+
+ 7. Misc
+ - `pl.phate` (need to be added)
+ - `pl.matrix` (need to be added)
+ - `pl.paga_adjacency` (need to be added)
+ - `pl.timeseries` (need to be added)
+ - `pl.timeseries_as_heatmap` (need to be added)
+ - `pl.timeseries_subplot` (need to be added)
+
+
\ No newline at end of file
diff -r 000000000000 -r 397d2c97af05 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Mon Mar 04 10:14:25 2019 -0500
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+
+ 1.4
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+
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+ scanpy
+ loompy
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+ 10.1186/s13059-017-1382-0
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+ anndata_output_format == 'h5ad'
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+ anndata_output_format == 'loom'
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+ modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad'
+
+
+ modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom'
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+ 0:
+ #set $var_group_positions=[]
+ #set $var_group_labels=[]
+ #for $i, $s in enumerate($method.var_group_positions)
+ #silent $var_group_positions.append((int($s.start), int($s.end)))
+ #silent $var_group_labels.append(str($s.label))
+ #end for
+ var_group_positions=$var_group_positions,
+ var_group_labels=$var_group_labels,
+ #else
+ var_group_positions=None,
+ var_group_labels=None,
+ #end if
+#if $method.var_group_rotation
+ var_group_rotation=$method.var_group_rotation,
+#end if
+#if $method.figsize.test == 'yes'
+ figsize=($method.figsize.width, $method.figsize.height),
+#end if
+#if $method.layer != ''
+ layer='$method.layer',
+#end if
+ ]]>
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+ 0
+ #set $components=[]
+ #for $i, $s in enumerate($method.plot.components)
+ #silent $components.append(str($s.axis1) + ',' + str($s.axis2))
+ #end for
+ components=$components,
+#else
+ components=None,
+#end if
+ ]]>
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diff -r 000000000000 -r 397d2c97af05 plot.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/plot.xml Mon Mar 04 10:14:25 2019 -0500
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+ macros.xml
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+ format == 'png'
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+ format == 'pdf'
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+ format == 'svg'
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+
+ `__
+
+Generic: Heatmap of the expression values of set of genes (`pl.heatmap`)
+========================================================================
+
+If `groupby` is given, the heatmap is ordered by the respective group. For
+example, a list of marker genes can be plotted, ordered by clustering. If
+the `groupby` observation annotation is not categorical the observation
+annotation is turned into a categorical by binning the data into the number
+specified in `num_categories`.
+
+More details on the `scanpy documentation
+`__
+
+Generic: Makes a dot plot of the expression values (`pl.dotplot`)
+=================================================================
+
+For each var_name and each `groupby` category a dot is plotted. Each dot
+represents two values: mean expression within each category (visualized by
+color) and fraction of cells expressing the var_name in the
+category. (visualized by the size of the dot). If groupby is not given, the
+dotplot assumes that all data belongs to a single category. A gene is not
+considered expressed if the expression value in the adata (or adata.raw) is
+equal to zero.
+
+For instance, for each marker gene, the mean value and the percentage of cells
+expressing the gene can be visualized for each cluster.
+
+More details on the `scanpy documentation
+`__
+
+Generic: Violin plot (`pl.violin`)
+==================================
+
+Wraps `seaborn.violinplot` for `anndata.AnnData`.
+
+More details on the `scanpy documentation
+`__
+
+Generic: Heatmap of the mean expression values per cluster (`pl.matrixplot`)
+============================================================================
+
+Creates a heatmap of the mean expression values per cluster of each var_names
+
+If groupby is not given, the matrixplot assumes that all data belongs to a single
+category.
+
+More details on the `scanpy documentation
+`__
+
+Generic: Hierarchically-clustered heatmap (`pl.clustermap`)
+===========================================================
+
+Wraps `seaborn.clustermap
+`__ for
+`anndata.AnnData`.
+
+The returned object has a savefig() method that should be used if you want
+to save the figure object without clipping the dendrograms.
+
+To access the reordered row indices, use:
+clustergrid.dendrogram_row.reordered_ind
+
+Column indices, use: clustergrid.dendrogram_col.reordered_ind
+
+More details on the `scanpy documentation
+`__
+
+Preprocessing: Plot the fraction of counts assigned to each gene over all cells (`pl.highest_expr_genes`)
+=========================================================================================================
+
+Computes, for each gene, the fraction of counts assigned to that gene within
+a cell. The `n_top` genes with the highest mean fraction over all cells are
+plotted as boxplots.
+
+This plot is similar to the `scater` package function `plotHighestExprs(type= "highest-expression")`, see `here
+`__.
+-- Davis McCarthy and Aaron Lun
+
+More details on the `scanpy documentation
+`__
+
+PCA: Scatter plot in PCA coordinates (`pl.pca`)
+===============================================
+
+More details on the `scanpy documentation
+`__
+
+PCA: Rank genes according to contributions to PCs (`pl.pca_loadings`)
+=====================================================================
+
+More details on the `scanpy documentation
+`__
+
+PCA: Plot the variance ratio (`pl.pca_variance_ratio`)
+======================================================
+
+More details on the `scanpy documentation
+`__
+
+PCA: Plot PCA results (`pl.pca_overview`)
+=========================================
+
+The parameters are the ones of the scatter plot. Call pca_ranking separately
+if you want to change the default settings.
+
+More details on the `scanpy documentation
+`__
+
+Embedding: Scatter plot in tSNE basis (`pl.tsne`)
+=================================================
+
+More details on the `scanpy documentation
+`__
+
+Embeddings: Scatter plot in UMAP basis (`pl.umap`)
+==================================================
+
+More details on the `scanpy documentation
+`__
+
+Embeddings: Scatter plot in Diffusion Map basis (`pl.diffmap`)
+==============================================================
+
+More details on the `scanpy documentation
+`__
+
+Branching trajectories and pseudotime, clustering: Plot groups and pseudotime (`pl.dpt_groups_pseudotime`)
+===========================================================================================================
+
+More details on the `scanpy documentation
+`__
+
+Branching trajectories and pseudotime, clustering: Heatmap of pseudotime series (`pl.dpt_timeseries`)
+=====================================================================================================
+
+More details on the `scanpy documentation
+`__
+
+
+Branching trajectories and pseudotime, clustering: Plot the abstracted graph through thresholding low-connectivity edges (`pl.paga`)
+====================================================================================================================================
+
+This uses ForceAtlas2 or igraph's layout algorithms for most layouts.
+
+When initializing the positions, note that - for some reason - igraph
+mirrors coordinates along the x axis... that is, you should increase the
+`maxiter` parameter by 1 if the layout is flipped.
+
+More details on the `scanpy documentation
+`__
+
+
+ ]]>
+
+
diff -r 000000000000 -r 397d2c97af05 test-data/krumsiek11.h5ad
Binary file test-data/krumsiek11.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/krumsiek11.loom
Binary file test-data/krumsiek11.loom has changed
diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
@@ -0,0 +1,640 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
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diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
@@ -0,0 +1,12 @@
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diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
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diff -r 000000000000 -r 397d2c97af05 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad
Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular Mon Mar 04 10:14:25 2019 -0500
@@ -0,0 +1,12 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular Mon Mar 04 10:14:25 2019 -0500
@@ -0,0 +1,9 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/pp.log1p.krumsiek11.h5ad
Binary file test-data/pp.log1p.krumsiek11.h5ad has changed
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:14:25 2019 -0500
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
@@ -0,0 +1,101 @@
+index paul15_clusters dpt_groups dpt_order dpt_order_indices
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+192 16Neu 2 75 42
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+182 5Ery 1 16 97
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+2291 16Neu 3 92 96
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diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
@@ -0,0 +1,2731 @@
+index paul15_clusters dpt_pseudotime dpt_groups dpt_order dpt_order_indices
+0 7MEP 0.077775456 1 172 404
+1 15Mo 0.70791066 2 2592 1876
+2 3Ery 0.9299172 1 1309 2408
+3 15Mo 0.6765072 2 2406 1880
+4 3Ery 0.86704516 1 1154 2098
+5 15Mo 0.58440965 2 1808 823
+6 4Ery 0.60054153 1 688 1149
+7 2Ery 0.43734095 1 539 2385
+8 3Ery 0.2407884 1 367 2384
+9 2Ery 0.8557937 1 1110 155
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+11 17Neu 0.30127475 2 1456 2365
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+1575 16Neu 0.6871232 2 2480 2632
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+1947 14Mo 0.6595199 2 2269 2495
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+1951 9GMP 0.3965054 2 1564 1742
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+1953 12Baso 0.28381324 2 1442 1495
+1954 10GMP 0.15789407 2 1350 1605
+1955 14Mo 0.6156825 2 1913 255
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+1957 14Mo 0.6667399 2 2327 62
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+1959 15Mo 0.65986997 2 2276 595
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+1963 14Mo 0.56948024 2 1782 280
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+1967 2Ery 0.8543089 1 1100 414
+1968 2Ery 0.6544664 1 750 2454
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+1970 2Ery 0.7846794 1 903 604
+1971 2Ery 0.86650366 1 1153 895
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+1973 2Ery 0.87086326 1 1171 2422
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+1977 6Ery 0.18856433 1 324 277
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+1979 6Ery 0.09344822 1 218 821
+1980 13Baso 0.6463502 2 2143 1243
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+1984 10GMP 0.3602726 2 1514 142
+1985 2Ery 0.68055326 1 779 1330
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+1988 14Mo 0.39688507 2 1565 1881
+1989 2Ery 0.80890965 1 964 2461
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+1991 2Ery 0.80403626 1 951 692
+1992 5Ery 0.57673305 1 666 1231
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+1995 5Ery 0.80122435 1 943 1099
+1996 4Ery 0.68955934 1 788 1756
+1997 2Ery 0.7694164 1 879 232
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+1999 2Ery 0.6264478 1 724 1163
+2000 2Ery 0.46427318 1 567 427
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+2009 14Mo 0.6415271 2 2101 1020
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+2016 7MEP 0.0895867 1 204 1639
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+2018 14Mo 0.6592349 2 2267 579
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+2020 6Ery 0.11544419 1 267 2705
+2021 2Ery 0.8626003 1 1137 1242
+2022 4Ery 0.8143835 1 981 521
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.pca.krumsiek11.h5ad
Binary file test-data/tl.pca.krumsiek11.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.pca.variance_ratio.krumsiek11.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/tl.pca.variance_ratio.krumsiek11.tabular Mon Mar 04 10:14:25 2019 -0500
@@ -0,0 +1,10 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.rank_genes_groups.krumsiek11.h5ad
Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.score_genes.krumsiek11.h5ad
Binary file test-data/tl.score_genes.krumsiek11.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 test-data/tl.score_genes.krumsiek11.obs.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:14:25 2019 -0500
@@ -0,0 +1,641 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad
Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed
diff -r 000000000000 -r 397d2c97af05 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 10:14:25 2019 -0500
@@ -0,0 +1,641 @@
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.tsne.krumsiek11_X_tsne.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 000000000000 -r 397d2c97af05 test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed