| Previous changeset 0:0e212e42ef88 (2019-03-04) Next changeset 2:dc2de14881ba (2019-12-05) |
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Commit message:
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5" |
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README.md cluster_reduce_dimension.xml macros.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 |
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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 |
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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 |
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| diff -r 0e212e42ef88 -r 20cfb9f3dded README.md --- a/README.md Mon Mar 04 10:13:44 2019 -0500 +++ b/README.md Wed Oct 16 06:29:43 2019 -0400 |
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| 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" |
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| diff -r 0e212e42ef88 -r 20cfb9f3dded README.rst --- a/README.rst Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -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 |
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| diff -r 0e212e42ef88 -r 20cfb9f3dded cluster_reduce_dimension.xml --- a/cluster_reduce_dimension.xml Mon Mar 04 10:13:44 2019 -0500 +++ b/cluster_reduce_dimension.xml Wed Oct 16 06:29:43 2019 -0400 |
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| b'@@ -1,32 +1,46 @@\n-<tool id="scanpy_cluster_reduce_dimension" name="Cluster and reduce dimension with scanpy" version="@galaxy_version@">\n- <description></description>\n+<tool id="scanpy_cluster_reduce_dimension" name="Cluster," version="@galaxy_version@">\n+ <description>infer trajectories and embed with scanpy</description>\n <macros>\n <import>macros.xml</import>\n <xml name="pca_inputs">\n- <param name="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/>\n- <param name="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>\n+ <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/>\n+ <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>\n <conditional name="pca">\n- <param name="chunked" type="select" label="Type of PCA?">\n+ <param argument="chunked" type="select" label="Type of PCA?">\n <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of `random_seed` and `svd_solver`)</option>\n <option value="False" selected="true">Full PCA</option>\n </param>\n <when value="True">\n- <param name="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/>\n+ <param argument="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/>\n </when>\n <when value="False">\n- <param name="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true"\n+ <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true"\n label="Compute standard PCA from covariance matrix?"\n help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/>\n <expand macro="svd_solver"/> \n- <param name="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/>\n+ <param argument="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/>\n </when>\n </conditional>\n+ <param argument="use_highly_variable" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use highly variable genes only?" help="They should be use if they have been determined beforehand."/>\n </xml>\n- <token name="@CMD_pca_outputs@"><![CDATA[\n-np.savetxt(\'$X_pca\', adata.obsm[\'X_pca\'], delimiter=\'\\t\')\n-np.savetxt(\'$PCs\', adata.varm[\'PCs\'], delimiter=\'\\t\')\n-np.savetxt(\'$variance\', adata.uns[\'pca\'][\'variance\'], delimiter=\'\\t\')\n-np.savetxt(\'$variance_ratio\', adata.uns[\'pca\'][\'variance_ratio\'], delimiter=\'\\t\')\n+ <xml name="param_random_state">\n+ <param argument="random_state" type="integer" value="0" label="Random state" help="Change the initialization of the optimization."/>\n+ </xml>\n+ <xml name="param_use_weights">\n+ <param argument="use_weights" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use weights from knn graph?"/>\n+ </xml>\n+ <token name="@CMD_pca_help@"><![CDATA[\n+The PCA is computed using the implementation of *scikit-learn*.\n+\n+The returned AnnData object contains:\n+\n+- PCA coordinates in the multi-dimensional observation annotation (obsm)\n+- Principal components containing the loadings in the multi-dimensional variable annotation (varm)\n+- The variance decomposition '..b'ed within Scanpy (Wolf et al, 2017). Here, we use a further developed\n+version, which is able to deal with disconnected graphs (Wolf et al, 2017) and can\n+be run in a `hierarchical` mode by setting the parameter\n+`n_branchings>1`. We recommend, however, to only use\n+`tl.dpt` for computing pseudotime (`n_branchings=0`) and\n+to detect branchings via `paga`. For pseudotime, you need\n+to annotate your data with a root cell. \n \n-The neighbor search efficiency of this heavily relies on UMAP (McInnes et al, 2018),\n-which also provides a method for estimating connectivities of data points -\n-the connectivity of the manifold (`method==\'umap\'`). If `method==\'diffmap\'`,\n-connectivities are computed according to Coifman et al (2005), in the adaption of\n-Haghverdi et al (2016).\n+This requires to run `pp.neighbors`, first. In order to\n+reproduce the original implementation of DPT, use `method==\'gauss\'` in\n+this. Using the default `method==\'umap\'` only leads to minor quantitative\n+differences, though.\n+\n+\n+If `n_branchings==0`, no field `dpt_groups` will be written.\n+\n+- dpt_pseudotime : Array of dim (number of samples) that stores the pseudotime of each cell, that is, the DPT distance with respect to the root cell.\n+- dpt_groups : Array of dim (number of samples) that stores the subgroup id (\'0\',\'1\', ...) for each cell. The groups typically correspond to \'progenitor cells\', \'undecided cells\' or \'branches\' of a process.\n+\n+The tool is similar to the R package `destiny` of Angerer et al (2016).\n+\n+More details on the `tl.dpt scanpy documentation\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_\n \n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.neighbors.html#scanpy.api.pp.neighbors>`__\n+\n+Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)\n+=======================================================================================\n \n-Rank genes for characterizing groups, using `tl.rank_genes_groups`\n-==================================================================\n+By quantifying the connectivity of partitions (groups, clusters) of the\n+single-cell graph, partition-based graph abstraction (PAGA) generates a much\n+simpler abstracted graph (*PAGA graph*) of partitions, in which edge weights\n+represent confidence in the presence of connections. By tresholding this\n+confidence in `paga`, a much simpler representation of data\n+can be obtained.\n \n-It returns:\n+The confidence can be interpreted as the ratio of the actual versus the\n+expected value of connetions under the null model of randomly connecting\n+partitions. We do not provide a p-value as this null model does not\n+precisely capture what one would consider "connected" in real data, hence it\n+strongly overestimates the expected value. See an extensive discussion of\n+this in Wolf et al (2017).\n+\n+Together with a random walk-based distance measure, this generates a partial\n+coordinatization of data useful for exploring and explaining its variation.\n \n-- `Gene names`: Gene names ordered in column by group id and in rows according to scores\n-- `Scores`: Score for each gene (rows) for each group (columns), same order as for the names\n+The returned AnnData object contains:\n+\n+- Full adjacency matrix of the abstracted graph, weights correspond to confidence in the connectivities of partition (connectivities)\n+- Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree)\n \n-More details on the `scanpy documentation\n-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.rank_genes_groups.html#scanpy.api.tl.rank_genes_groups>`__\n+These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects\n \n+More details on the `tl.paga scanpy documentation\n+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_\n ]]></help>\n <expand macro="citations"/>\n </tool>\n' |
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| diff -r 0e212e42ef88 -r 20cfb9f3dded macros.xml --- a/macros.xml Mon Mar 04 10:13:44 2019 -0500 +++ b/macros.xml Wed Oct 16 06:29:43 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 0e212e42ef88 -r 20cfb9f3dded 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 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,641 +0,0 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tTrue\t8\n-327\tTrue\t8\n-328\tTrue\t7\n-329\tTrue\t9\n-330\tTrue\t8\n-331\tTrue\t9\n-332\tTrue\t8\n-333\tTrue\t8\n-334\tTrue\t8\n-335\tTrue\t10\n-336\tTrue\t10\n-337\tTrue\t9\n-338\tTrue\t10\n-339\tTrue\t10\n-340\tTrue\t10\n-341\tTrue\t10\n-342\tTrue\t10\n-343\tTrue\t10\n-344\tTrue\t10\n-345\tTrue\t11\n-346\tTrue\t11\n-347\tTrue\t11\n-348\tTrue\t11\n-349\tTrue\t11\n-350\tTrue\t11\n-351\tTrue\t11\n-352\tTrue\t11\n-353\tTrue\t11\n-354\tTrue\t11\n-355\tTrue\t11\n-356\tTrue\t11\n-357\tTrue\t11\n-358\tTrue\t11\n-359\tTrue\t11\n-360\tTrue\t11\n-361\tTrue\t11\n-362\tTrue\t11\n-363\tTrue\t11\n-364\tTrue\t11\n-365\tTrue\t11\n-366\tTrue\t11\n-367\tTrue\t11\n-368\tTrue\t11\n-369\tTrue\t11\n-370\tTrue\t11\n-371\tTrue\t11\n-372\tTrue\t11\n-373\tTrue\t11\n-374\tTrue\t11\n-375\tTrue\t11\n-376\tTrue\t11\n-377\tTrue\t11\n-378\tTrue\t11\n-379\tTrue\t11\n-380\tTrue\t11\n-381\tTrue\t11\n-382\tTrue\t11\n-383\tTrue\t11\n-384\tTrue\t11\n-385\tTrue\t11\n-386\tTrue\t11\n-387\tTrue\t11\n-388\tTrue\t11\n-389\tTrue\t11\n-390\tTrue\t11\n-391\tTrue\t11\n-392\tTrue\t11\n-393\tTrue\t11\n-394\tTrue\t11\n-395\tTrue\t11\n-396\tTrue\t11\n-397\tTrue\t11\n-398\tTrue\t11\n-399\tTrue\t11\n-400\tTrue\t11\n-401\tTrue\t11\n-402\tTrue\t11\n-403\tTrue\t11\n-404\tTrue\t11\n-405\tTrue\t11\n-406\tTrue\t11\n-407\tTrue\t11\n-408\tTrue\t11\n-409\tTrue\t11\n-410\tTrue\t11\n-411\tTrue\t11\n-412\tTrue\t11\n-413\tTrue\t11\n-414\tTrue\t11\n-415\tTrue\t11\n-416\tTrue\t11\n-417\tTrue\t11\n-418\tTrue\t11\n-419\tTrue\t11\n-420\tTrue\t11\n-421\tTrue\t11\n-422\tTrue\t11\n-423\tTrue\t11\n-424\tTrue\t11\n-425\tTrue\t11\n-426\tTrue\t11\n-427\tTrue\t11\n-428\tTrue\t11\n-429\tTrue\t11\n-430\tTrue\t11\n-431\tTrue\t11\n-432\tTrue\t11\n-433\tTrue\t11\n-434\tTrue\t11\n-435\tTrue\t11\n-436\tTrue\t11\n-437\tTrue\t11\n-438\tTrue\t11\n-439\tTrue\t11\n-440\tTrue\t11\n-441\tTrue\t11\n-442\tTrue\t11\n-443\tTrue\t11\n-444\tTrue\t11\n-445\tTrue\t11\n-446\tTrue\t10\n-447\tTrue\t10\n-448\tTrue\t10\n-449\tTrue\t10\n-450\tTrue\t10\n-451\tTrue\t11\n-452\tTrue\t11\n-453\tTrue\t11\n-454\tTrue\t10\n-455\tTrue\t10\n-456\tTrue\t11\n-457\tTrue\t10\n-458\tTrue\t10\n-459\tTrue\t11\n-460\tTrue\t11\n-461\tTrue\t10\n-462\tTrue\t9\n-463\tTrue\t10\n-464\tTrue\t10\n-465\tTrue\t10\n-466\tTrue\t10\n-467\tTrue\t9\n-468\tTrue\t10\n-469\tTrue\t10\n-470\tTrue\t11\n-471\tTrue\t11\n-472\tTrue\t9\n-473\tTrue\t9\n-474\tTrue\t9\n-475\tTrue\t9\n-476\tTrue\t9\n-477\tTrue\t10\n-478\tTrue\t10\n-479\tTrue\t9\n-480\tTrue\t8\n-481\tTrue\t10\n-482\tTrue\t10\n-483\tTrue\t10\n-484\tTrue\t8\n-485\tTrue\t8\n-486\tTrue\t9\n-487\tTrue\t8\n-488\tTrue\t9\n-489\tTrue\t10\n-490\tTrue\t11\n-491\tTrue\t11\n-492\tTrue\t11\n-493\tTrue\t9\n-494\tTrue\t10\n-495\tTrue\t10\n-496\tTrue\t10\n-497\tTrue\t10\n-498\tTrue\t11\n-499\tTrue\t11\n-500\tTrue\t11\n-501\tTrue\t11\n-502\tTrue\t11\n-503\tTrue\t11\n-504\tTrue\t11\n-505\tTrue\t11\n-506\tTrue\t11\n-507\tTrue\t11\n-508\tTrue\t11\n-509\tTrue\t11\n-510\tTrue\t11\n-511\tTrue\t11\n-512\tTrue\t11\n-513\tTrue\t11\n-514\tTrue\t11\n-515\tTrue\t11\n-516\tTrue\t11\n-517\tTrue\t11\n-518\tTrue\t11\n-519\tTrue\t11\n-520\tTrue\t11\n-521\tTrue\t11\n-522\tTrue\t11\n-523\tTrue\t11\n-524\tTrue\t11\n-525\tTrue\t11\n-526\tTrue\t11\n-527\tTrue\t11\n-528\tTrue\t11\n-529\tTrue\t11\n-530\tTrue\t11\n-531\tTrue\t11\n-532\tTrue\t11\n-533\tTrue\t11\n-534\tTrue\t11\n-535\tTrue\t11\n-536\tTrue\t10\n-537\tTrue\t10\n-538\tTrue\t10\n-539\tTrue\t10\n-540\tTrue\t10\n-541\tTrue\t10\n-542\tTrue\t11\n-543\tTrue\t11\n-544\tTrue\t11\n-545\tTrue\t11\n-546\tTrue\t11\n-547\tTrue\t10\n-548\tTrue\t9\n-549\tTrue\t9\n-550\tTrue\t10\n-551\tTrue\t11\n-552\tTrue\t10\n-553\tTrue\t9\n-554\tTrue\t9\n-555\tTrue\t9\n-556\tTrue\t8\n-557\tTrue\t9\n-558\tTrue\t7\n-559\tTrue\t8\n-560\tTrue\t8\n-561\tTrue\t10\n-562\tTrue\t9\n-563\tTrue\t8\n-564\tTrue\t8\n-565\tTrue\t8\n-566\tTrue\t8\n-567\tTrue\t8\n-568\tTrue\t6\n-569\tTrue\t6\n-570\tTrue\t6\n-571\tTrue\t6\n-572\tTrue\t8\n-573\tTrue\t8\n-574\tTrue\t7\n-575\tTrue\t9\n-576\tTrue\t7\n-577\tTrue\t7\n-578\tTrue\t8\n-579\tTrue\t8\n-580\tTrue\t6\n-581\tTrue\t7\n-582\tTrue\t7\n-583\tTrue\t8\n-584\tTrue\t6\n-585\tTrue\t5\n-586\tTrue\t5\n-587\tTrue\t5\n-588\tTrue\t6\n-589\tTrue\t7\n-590\tTrue\t6\n-591\tTrue\t8\n-592\tTrue\t7\n-593\tTrue\t7\n-594\tTrue\t8\n-595\tTrue\t7\n-596\tTrue\t7\n-597\tTrue\t8\n-598\tTrue\t5\n-599\tTrue\t4\n-600\tTrue\t5\n-601\tTrue\t6\n-602\tTrue\t5\n-603\tTrue\t6\n-604\tTrue\t7\n-605\tTrue\t7\n-606\tTrue\t9\n-607\tTrue\t10\n-608\tTrue\t8\n-609\tTrue\t8\n-610\tTrue\t10\n-611\tTrue\t10\n-612\tTrue\t9\n-613\tTrue\t8\n-614\tTrue\t8\n-615\tTrue\t8\n-616\tTrue\t7\n-617\tTrue\t8\n-618\tTrue\t7\n-619\tTrue\t6\n-620\tTrue\t6\n-621\tTrue\t7\n-622\tTrue\t7\n-623\tTrue\t7\n-624\tTrue\t8\n-625\tTrue\t6\n-626\tTrue\t7\n-627\tTrue\t7\n-628\tTrue\t7\n-629\tTrue\t6\n-630\tTrue\t5\n-631\tTrue\t7\n-632\tTrue\t6\n-633\tTrue\t6\n-634\tTrue\t7\n-635\tTrue\t6\n-636\tTrue\t8\n-637\tTrue\t8\n-638\tTrue\t6\n-639\tTrue\t8\n' |
| b |
| diff -r 0e212e42ef88 -r 20cfb9f3dded test-data/pp.filter_genes.krumsiek11-min_counts.h5ad |
| b |
| Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed |
| b |
| diff -r 0e212e42ef88 -r 20cfb9f3dded 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 10:13:44 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 0e212e42ef88 -r 20cfb9f3dded 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 10:13:44 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 -75 True 11 -76 True 228 -77 True 192 -78 True 140 -79 True 15 -80 True 22 -81 True 10 -82 True 233 -83 True 129 -84 True 12 -85 True 297 -86 True 295 -87 True 127 -88 True 208 -89 True 281 -90 True 265 -91 True 254 -92 True 122 -93 True 76 -94 True 237 -95 True 74 -96 True 65 -97 True 45 -98 True 90 -99 True 147 -100 True 189 -101 True 170 -102 True 207 -103 True 14 -104 True 307 -105 True 267 -106 True 111 -107 True 94 -108 True 306 -109 True 126 -110 True 269 -111 True 116 -112 True 140 -113 True 260 -114 True 201 -115 True 198 -116 True 155 -117 True 256 -118 True 214 -119 True 70 -120 True 304 -121 True 336 -122 True 201 -123 True 305 -124 True 301 -125 True 301 -126 True 338 -127 True 81 -128 True 256 -129 True 277 -130 True 237 -131 True 173 -132 True 228 -133 True 64 -134 True 52 -135 True 34 -136 True 333 -137 True 285 -138 True 132 -139 True 32 -140 True 275 -141 True 31 -142 True 244 -143 True 15 -144 True 54 -145 True 289 -146 True 186 -147 True 283 -148 True 333 -149 True 53 -150 True 26 -151 True 173 -152 True 19 -153 True 109 -154 True 138 -155 True 264 -156 True 293 -157 True 225 -158 True 150 -159 True 62 -160 True 350 -161 True 13 -162 True 341 -163 True 223 -164 True 177 -165 True 15 -166 True 202 -167 True 101 -168 True 203 -169 True 271 -170 True 305 -171 True 45 -172 True 322 -173 True 164 -174 True 213 -175 True 55 -176 True 143 -177 True 112 -178 True 266 -179 True 168 -180 True 9 -181 True 300 -182 True 249 -183 True 101 -184 True 55 -185 True 312 -186 True 181 -187 True 256 -188 True 27 -189 True 242 -190 True 210 -191 True 12 -192 True 203 -193 True 41 -194 True 205 -195 True 315 -196 True 94 -197 True 262 -198 True 316 -199 True 13 -200 True 94 -201 True 204 -202 True 245 -203 True 11 -204 True 238 -205 True 301 -206 True 219 -207 True 106 -208 True 253 -209 True 134 -210 True 262 -211 True 222 -212 True 82 -213 True 153 -214 True 122 -215 True 211 -216 True 49 -217 True 211 -218 True 176 -219 True 329 -220 True 8 |
| b |
| diff -r 0e212e42ef88 -r 20cfb9f3dded test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad |
| b |
| Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed |
| b |
| diff -r 0e212e42ef88 -r 20cfb9f3dded 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 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,12 +0,0 @@ - gene_subset means dispersions dispersions_norm -0 False 0.22807331 -1.513815 -1 False 0.27662647 -0.6374868 -2 False 0.12324284 -1.1931922 -3 True 0.10477218 -0.8270577 0.67448974 -4 True 0.08612139 -0.880823 0.67448974 -5 False 0.2751125 -0.6042374 -6 False 0.55053085 -1.5924454 -7 False 0.3306357 -0.91260546 -8 False 0.25766766 -0.86990273 -9 False 0.22937028 -0.7354343 -10 False 0.223133 -0.96748924 |
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| diff -r 0e212e42ef88 -r 20cfb9f3dded 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 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,9 +0,0 @@ -index means dispersions dispersions_norm -Fog1 0.12324284 -1.1931922 1.0 -EKLF 0.10477218 -0.8270577 0.70710677 -SCL 0.2751125 -0.6042374 0.707108 -Cebpa 0.55053085 -1.5924454 1.0 -Pu.1 0.3306357 -0.91260546 1.0 -cJun 0.25766766 -0.86990273 1.0 -EgrNab 0.22937028 -0.7354343 0.7071069 -Gfi1 0.223133 -0.96748924 1.0 |
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progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mo -81 Mo -82 Mo -83 Mo -84 Mo -85 Mo -86 Mo -87 Mo -88 Mo -89 Mo -90 Mo -91 Mo -92 Mo -93 Mo -94 Mo -95 Mo -96 Mo -97 Mo -98 Mo -99 Mo -100 Mo -101 Mo -102 Mo -103 Mo -104 Mo -105 Mo -106 Mo -107 Mo -108 Mo -109 Mo -110 Mo -111 Mo -112 Mo -113 Mo -114 Mo -115 Mo -116 Mo -117 Mo -118 Mo -119 Mo -120 Mo -121 Mo -122 Mo -123 Mo -124 Mo -125 Mo -126 Mo -127 Mo -128 Mo -129 Mo -130 Mo -131 Mo -132 Mo -133 Mo -134 Mo -135 Mo -136 Mo -137 Mo -138 Mo -139 Mo -140 Mo -141 Mo -142 Mo -143 Mo -144 Mo -145 Mo -146 Mo -147 Mo -148 Mo -149 Mo -150 Mo -151 Mo -152 Mo -153 Mo -154 Mo -155 Mo -156 Mo -157 Mo -158 Mo -159 Mo -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 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progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mk -81 Mk -82 Mk -83 Mk -84 Mk -85 Mk -86 Mk -87 Mk -88 Mk -89 Mk -90 Mk -91 Mk -92 Mk -93 Mk -94 Mk -95 Mk -96 Mk -97 Mk -98 Mk -99 Mk -100 Mk -101 Mk -102 Mk -103 Mk -104 Mk -105 Mk -106 Mk -107 Mk -108 Mk -109 Mk -110 Mk -111 Mk -112 Mk -113 Mk -114 Mk -115 Mk -116 Mk -117 Mk -118 Mk -119 Mk -120 Mk -121 Mk -122 Mk -123 Mk -124 Mk -125 Mk -126 Mk -127 Mk -128 Mk -129 Mk -130 Mk -131 Mk -132 Mk -133 Mk -134 Mk -135 Mk -136 Mk -137 Mk -138 Mk -139 Mk -140 Mk -141 Mk -142 Mk -143 Mk -144 Mk -145 Mk -146 Mk -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 -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor 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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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| b |
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\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 0e212e42ef88 -r 20cfb9f3dded 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 0e212e42ef88 -r 20cfb9f3dded test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular --- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,641 +0,0 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