# HG changeset patch
# User iuc
# Date 1551712572 18000
# Node ID ed64c90a9b930a7536fcfc004e378135ee29cc79
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
diff -r 000000000000 -r ed64c90a9b93 README.md
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md Mon Mar 04 10:16:12 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 ed64c90a9b93 README.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst Mon Mar 04 10:16:12 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 ed64c90a9b93 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Mon Mar 04 10:16:12 2019 -0500
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+
+ 1.4
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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'
+
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+ 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 ed64c90a9b93 normalize.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/normalize.xml Mon Mar 04 10:16:12 2019 -0500
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+ macros.xml
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+ method['method'] == 'pp.normalize_per_cell'
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+ `__
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+
+Normalization and filtering as of Zheng et al. (2017), the Cell Ranger R Kit of 10x Genomics (`pp.recipe_zheng17`)
+==================================================================================================================
+
+Expects non-logarithmized data. If using logarithmized data, pass `log=False`.
+
+The recipe runs the following steps:
+
+- only consider genes with more than 1 count
+- normalize with total UMI count per cell
+- select highly-variable genes
+- subset the genes
+- renormalize after filtering
+- log transform (if needed)
+- scale to unit variance and shift to zero mean
+
+More details on the `scanpy documentation
+`__
+
+
+Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`)
+==============================================================================
+
+Expects non-logarithmized data. If using logarithmized data, pass `log=False`.
+
+More details on the `scanpy documentation
+`__
+
+
+Normalization and filtering as of Seurat et al (2015) (`pp.recipe_seurat`)
+==========================================================================
+
+This uses a particular preprocessing.
+
+Expects non-logarithmized data. If using logarithmized data, pass `log=False`.
+
+More details on the `scanpy documentation
+`__
+
+Logarithmize the data matrix (`pp.log1p`)
+=========================================
+
+More details on the `scanpy documentation
+`__
+
+Scale data to unit variance and zero mean (`pp.scale`)
+======================================================
+
+More details on the `scanpy documentation
+`__
+
+Computes the square root the data matrix (`pp.sqrt`)
+====================================================
+
+`X = sqrt(X)`
+
+Downsample counts (`pp.downsample_counts`)
+==========================================
+
+Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This
+has been implemented by M. D. Luecken.
+
+More details on the `scanpy documentation
+`__
+
+ ]]>
+
+
diff -r 000000000000 -r ed64c90a9b93 test-data/krumsiek11.h5ad
Binary file test-data/krumsiek11.h5ad has changed
diff -r 000000000000 -r ed64c90a9b93 test-data/krumsiek11.loom
Binary file test-data/krumsiek11.loom has changed
diff -r 000000000000 -r ed64c90a9b93 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 10:16:12 2019 -0500
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diff -r 000000000000 -r ed64c90a9b93 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad
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diff -r 000000000000 -r ed64c90a9b93 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 ed64c90a9b93 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 ed64c90a9b93 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 10:16:12 2019 -0500
@@ -0,0 +1,101 @@
+index paul15_clusters dpt_groups dpt_order dpt_order_indices
+578 13Baso 2 53 27
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+2690 10GMP 2 66 45
+70 5Ery 1 32 65
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+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
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+2317 14Mo 2 90 12
+2348 11DC 2 82 69
+953 5Ery 1 27 13
+628 9GMP 2 83 15
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+1499 16Neu 3 96 18
+1083 2Ery 1 21 19
+831 14Mo 0 2 21
+15 7MEP 0 1 86
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+2457 7MEP 2 64 89
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+1642 14Mo 2 91 68
+2520 10GMP 2 89 67
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+988 14Mo 2 86 76
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+2189 16Neu 2 85 81
+446 13Baso 2 84 85
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+1540 16Neu 3 99 93
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+2279 15Mo 3 98 9
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+1680 6Ery 0 7 38
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+1677 3Ery 1 33 43
+982 4Ery 1 31 24
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+891 10GMP 2 80 61
diff -r 000000000000 -r ed64c90a9b93 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 10:16:12 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
+10 2Ery 0.8691475 1 1164 2372
+11 17Neu 0.30127475 2 1456 2365
+12 14Mo 0.63893527 2 2068 164
+13 13Baso 0.5479835 2 1748 607
+14 3Ery 0.8603202 1 1134 167
+15 7MEP 0.117413916 1 270 1172
+16 3Ery 0.6879372 1 786 1908
+17 3Ery 0.84132445 1 1055 805
+18 2Ery 0.8558495 1 1111 2413
+19 8Mk 0.1685136 0 98 2417
+20 3Ery 0.27082652 1 399 1126
+21 9GMP 0.35508415 2 1510 2436
+22 8Mk 0.12326076 0 100 1815
+23 10GMP 0.36161897 2 1515 1616
+24 15Mo 0.65855885 2 2259 2511
+25 16Neu 0.6648798 2 2312 438
+26 4Ery 0.7767835 1 889 437
+27 13Baso 0.411375 2 1588 1607
+28 17Neu 0.2787515 2 1438 1089
+29 3Ery 0.7026065 1 798 2475
+30 2Ery 0.78192663 1 899 614
+31 3Ery 0.4758986 1 578 112
+32 5Ery 0.52930063 1 627 851
+33 3Ery 0.5440802 1 638 115
+34 13Baso 0.5579254 2 1766 2457
+35 1Ery 0.9610762 1 1324 423
+36 3Ery 0.94298 1 1317 1109
+37 6Ery 0.35788366 1 471 2452
+38 15Mo 0.73092824 2 2657 1858
+39 3Ery 0.47530073 1 577 1117
+40 3Ery 0.8309953 1 1021 1843
+41 14Mo 0.64247084 2 2108 384
+42 19Lymph 0.4615774 0 113 2323
+43 12Baso 0.3523068 2 1509 185
+44 3Ery 0.8736262 1 1184 1299
+45 14Mo 0.64636815 2 2144 2032
+46 6Ery 0.41744366 1 524 1306
+47 4Ery 0.7252952 1 817 261
+48 7MEP 0.2547727 2 1422 2154
+49 2Ery 0.8350906 1 1032 723
+50 13Baso 0.36504018 2 1520 2051
+51 14Mo 0.66908467 2 2351 720
+52 6Ery 0.26223817 1 393 737
+53 18Eos 0.3815021 2 1544 1393
+54 4Ery 0.8454671 1 1065 718
+55 3Ery 0.7204466 1 813 715
+56 3Ery 0.624237 1 720 308
+57 14Mo 0.63932437 2 2076 1384
+58 2Ery 0.84006816 1 1050 1378
+59 13Baso 0.6566314 2 2235 1348
+60 2Ery 0.85520875 1 1106 1370
+61 13Baso 0.6836325 2 2458 1360
+62 13Baso 0.6240188 2 1957 2136
+63 14Mo 0.6315316 2 2004 2522
+64 7MEP 0.12562352 0 94 2172
+65 7MEP 0.20317915 1 333 2025
+66 7MEP 0.14576882 2 1346 793
+67 10GMP 0.4374045 2 1628 1505
+68 12Baso 0.35526624 2 1511 2293
+69 6Ery 0.15430509 1 295 1484
+70 5Ery 0.29059684 1 421 636
+71 5Ery 0.41076174 1 516 1222
+72 14Mo 0.63960767 2 2078 2269
+73 4Ery 0.820214 1 994 1238
+74 16Neu 0.6670669 2 2332 248
+75 16Neu 0.6509542 2 2187 1244
+76 3Ery 0.66913307 1 766 1457
+77 2Ery 0.89367026 1 1250 1455
+78 6Ery 0.76215255 1 863 1265
+79 13Baso 0.59909 2 1840 229
+80 13Baso 0.45156288 2 1649 2221
+81 10GMP 0.49630007 2 1711 2005
+82 14Mo 0.6566849 2 2236 1280
+83 5Ery 0.45687908 1 559 743
+84 7MEP 0.14249937 0 127 350
+85 7MEP 0.0928669 1 216 2540
+86 5Ery 0.6481682 1 746 2097
+87 6Ery 0.26217386 1 392 1654
+88 2Ery 0.8884944 1 1232 499
+89 10GMP 0.07816703 1 173 2564
+90 10GMP 0.36928764 2 1526 2687
+91 2Ery 0.85090935 1 1086 1651
+92 7MEP 0.2221476 2 1399 890
+93 6Ery 0.22915362 1 356 1726
+94 7MEP 0.08779579 1 199 64
+95 14Mo 0.6131882 2 1902 961
+96 16Neu 0.6578041 2 2246 1790
+97 10GMP 0.43475908 2 1624 1787
+98 3Ery 0.79592675 1 927 19
+99 3Ery 0.8079216 1 959 2592
+100 13Baso 0.6605006 2 2280 22
+101 1Ery 1.0 1 1331 523
+102 14Mo 0.6370637 2 2050 541
+103 15Mo 0.7214093 2 2628 1738
+104 2Ery 0.73762625 1 832 540
+105 5Ery 0.21692096 1 344 474
+106 2Ery 0.8745584 1 1190 2611
+107 5Ery 0.30303305 1 430 1774
+108 3Ery 0.87296826 1 1179 2661
+109 14Mo 0.58798885 2 1815 2659
+110 10GMP 0.44662797 2 1644 922
+111 2Ery 0.8643726 1 1145 525
+112 7MEP 0.14947182 0 31 974
+113 4Ery 0.7285211 1 823 42
+114 9GMP 0.40913528 2 1585 993
+115 7MEP 0.13643783 0 33 1630
+116 3Ery 0.85794955 1 1122 2631
+117 16Neu 0.7009699 2 2561 1034
+118 11DC 0.5372636 3 2701 1647
+119 10GMP 0.37033108 2 1531 1681
+120 6Ery 0.32231367 1 446 2713
+121 3Ery 0.2562019 1 387 452
+122 2Ery 0.8032558 1 947 2718
+123 5Ery 0.79876626 1 935 2707
+124 2Ery 0.8192419 1 992 2708
+125 2Ery 0.80044454 1 940 1055
+126 5Ery 0.43929636 1 542 513
+127 14Mo 0.64730495 2 2154 84
+128 5Ery 0.5184172 1 619 2551
+129 14Mo 0.7214278 2 2629 2549
+130 3Ery 0.47004408 1 573 1808
+131 2Ery 0.8697594 1 1168 2544
+132 16Neu 0.63243085 2 2010 506
+133 2Ery 0.8984441 1 1267 840
+134 13Baso 0.4428648 2 1634 2657
+135 16Neu 0.6703501 2 2360 1130
+136 2Ery 0.8305287 1 1019 479
+137 3Ery 0.87886757 1 1206 2232
+138 3Ery 0.7002867 1 797 2091
+139 14Mo 0.6564193 2 2233 867
+140 5Ery 0.6211381 1 714 870
+141 16Neu 0.72532946 2 2645 2617
+142 14Mo 0.6286634 2 1984 999
+143 14Mo 0.51324356 2 1722 1095
+144 13Baso 0.64119786 2 2095 1875
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+1911 13Baso 0.6237064 2 1956 535
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+1916 3Ery 0.89456785 1 1256 226
+1917 16Neu 0.67951095 2 2429 664
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+1919 14Mo 0.60336655 2 1850 671
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+1953 12Baso 0.28381324 2 1442 1495
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+1959 15Mo 0.65986997 2 2276 595
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+1963 14Mo 0.56948024 2 1782 280
+1964 4Ery 0.8325112 1 1024 1670
+1965 6Ery 0.25336298 1 382 1644
+1966 15Mo 0.68847847 2 2492 782
+1967 2Ery 0.8543089 1 1100 414
+1968 2Ery 0.6544664 1 750 2454
+1969 5Ery 0.20722313 1 336 1011
+1970 2Ery 0.7846794 1 903 604
+1971 2Ery 0.86650366 1 1153 895
+1972 14Mo 0.65335953 2 2205 1173
+1973 2Ery 0.87086326 1 1171 2422
+1974 14Mo 0.64784974 2 2160 2678
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+1976 5Ery 0.68117297 1 780 412
+1977 6Ery 0.18856433 1 324 277
+1978 13Baso 0.6024012 2 1844 1017
+1979 6Ery 0.09344822 1 218 821
+1980 13Baso 0.6463502 2 2143 1243
+1981 5Ery 0.2487112 1 376 698
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+1983 3Ery 0.75009274 1 853 1144
+1984 10GMP 0.3602726 2 1514 142
+1985 2Ery 0.68055326 1 779 1330
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+1987 2Ery 0.49259976 1 594 649
+1988 14Mo 0.39688507 2 1565 1881
+1989 2Ery 0.80890965 1 964 2461
+1990 2Ery 0.86828595 1 1160 2599
+1991 2Ery 0.80403626 1 951 692
+1992 5Ery 0.57673305 1 666 1231
+1993 14Mo 0.67117536 2 2365 1903
+1994 1Ery 0.8286186 1 1017 936
+1995 5Ery 0.80122435 1 943 1099
+1996 4Ery 0.68955934 1 788 1756
+1997 2Ery 0.7694164 1 879 232
+1998 10GMP 0.4229138 2 1608 1893
+1999 2Ery 0.6264478 1 724 1163
+2000 2Ery 0.46427318 1 567 427
+2001 7MEP 0.09880815 1 235 678
+2002 2Ery 0.8563361 1 1115 1114
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+2007 2Ery 0.79102075 1 915 1235
+2008 17Neu 0.2165167 2 1395 1159
+2009 14Mo 0.6415271 2 2101 1020
+2010 14Mo 0.6582938 2 2257 132
+2011 13Baso 0.6124933 2 1899 1019
+2012 7MEP 0.22798298 2 1401 843
+2013 16Neu 0.6818719 2 2447 605
+2014 2Ery 0.8146313 1 983 1103
+2015 2Ery 0.777303 1 890 803
+2016 7MEP 0.0895867 1 204 1639
+2017 6Ery 0.17116767 1 311 2370
+2018 14Mo 0.6592349 2 2267 579
+2019 4Ery 0.8235734 1 1002 2534
+2020 6Ery 0.11544419 1 267 2705
+2021 2Ery 0.8626003 1 1137 1242
+2022 4Ery 0.8143835 1 981 521
+2023 1Ery 0.89807314 1 1264 887
+2024 5Ery 0.44429657 1 547 2133
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+2029 13Baso 0.6387254 2 2066 380
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+2031 13Baso 0.68463624 2 2467 1282
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+2034 3Ery 0.9651047 1 1326 846
+2035 13Baso 0.6402312 2 2086 1104
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+2043 14Mo 0.67074907 2 2363 1928
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+2047 10GMP 0.57624406 2 1793 374
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+2071 16Neu 0.74487156 2 2668 2512
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diff -r 000000000000 -r ed64c90a9b93 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 ed64c90a9b93 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 ed64c90a9b93 test-data/tl.pca.krumsiek11.h5ad
Binary file test-data/tl.pca.krumsiek11.h5ad has changed
diff -r 000000000000 -r ed64c90a9b93 test-data/tl.pca.variance_ratio.krumsiek11.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/tl.pca.variance_ratio.krumsiek11.tabular Mon Mar 04 10:16:12 2019 -0500
@@ -0,0 +1,10 @@
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diff -r 000000000000 -r ed64c90a9b93 test-data/tl.rank_genes_groups.krumsiek11.h5ad
Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed
diff -r 000000000000 -r ed64c90a9b93 test-data/tl.score_genes.krumsiek11.h5ad
Binary file test-data/tl.score_genes.krumsiek11.h5ad has changed
diff -r 000000000000 -r ed64c90a9b93 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 10:16:12 2019 -0500
@@ -0,0 +1,641 @@
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diff -r 000000000000 -r ed64c90a9b93 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 ed64c90a9b93 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 10:16:12 2019 -0500
@@ -0,0 +1,641 @@
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diff -r 000000000000 -r ed64c90a9b93 test-data/tl.tsne.krumsiek11.h5ad
Binary file test-data/tl.tsne.krumsiek11.h5ad has changed
diff -r 000000000000 -r ed64c90a9b93 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 ed64c90a9b93 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