Mercurial > repos > iuc > scanpy_cluster_reduce_dimension
changeset 1:20cfb9f3dded draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
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--- a/README.md Mon Mar 04 10:13:44 2019 -0500 +++ b/README.md Wed Oct 16 06:29:43 2019 -0400 @@ -1,138 +1,115 @@ Scanpy ====== -## Classification of methods into steps +1. Inspect & Manipulate (`inspect.xml`) -Steps: + Methods | Description + --- | --- + `pp.calculate_qc_metrics` | Calculate quality control metrics + `pp.neighbors` | Compute a neighborhood graph of observations + `tl.score_genes` | Score a set of genes + `tl.score_genes_cell_cycle` | Score cell cycle gene + `tl.rank_genes_groups` | Rank genes for characterizing groups + `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**) + `pp.log1p` | Logarithmize the data matrix. + `pp.scale` | Scale data to unit variance and zero mean + `pp.sqrt` | Square root the data matrix -1. Filtering +2. Filter (`filter.xml`) 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 + `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**) `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. + `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts - 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 +3. Normalize (`normalize.xml`) Methods | Description --- | --- - `pp.normalize_per_cell` | Normalize total counts per cell + `pp.normalize_total` | Normalize 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 +4. Remove confounders (`remove_confounder.xml`) 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] + `pp.combat` | ComBat function for batch effect correction -5. Clustering and Heatmaps +5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`) Methods | Description --- | --- - `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15] - `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17] + `tl.louvain` | Cluster cells into subgroups + `tl.leiden` | Cluster cells into subgroups `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` | + `tl.draw_graph` | Force-directed graph drawing + `tl.dpt` | Infer progression of cells through geodesic distance along the graph + `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds + +6. Plot (`plot.xml`) + + 1. Generic + + Methods | Description + --- | --- + `pl.scatter` | Scatter plot along observations or variables axes + `pl.heatmap` | Heatmap of the expression values of set of genes + `pl.dotplot` | Makes a dot plot of the expression values + `pl.violin` | Violin plot + `pl.stacked_violin` | Stacked violin plots + `pl.matrixplot` | Heatmap of the mean expression values per cluster + `pl.clustermap` | Hierarchically-clustered heatmap -6. Cluster Inspection and plotting + 2. Preprocessing - Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related. + Methods | Description + --- | --- + `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells + `pl.highly_variable_genes` | Plot dispersions versus means for genes + + 3. PCA - 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` | + Methods | Description + --- | --- + `pl.pca` | Scatter plot in PCA coordinates + `pl.pca_loadings` | Rank genes according to contributions to PCs + `pl.pca_variance_ratio` | Scatter plot in PCA coordinates + `pl.pca_overview` | Plot PCA results -7. Branch/Between-Cluster Inspection + 4. Embeddings - Pseudotime analysis, relies on initial clustering. + Methods | Description + --- | --- + `pl.tsne` | Scatter plot in tSNE basis + `pl.umap` | Scatter plot in UMAP basis + `pl.diffmap` | Scatter plot in Diffusion Map basis + `pl.draw_graph` | Scatter plot in graph-drawing basis - 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` | + 5. Branching trajectories and pseudotime, clustering + Methods | Description + --- | --- + `pl.dpt_groups_pseudotime` | Plot groups and pseudotime + `pl.dpt_timeseries` | Heatmap of pseudotime series + `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges + `pl.paga_compare` | Scatter and PAGA graph side-by-side + `pl.paga_path` | Gene expression and annotation changes along paths -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 + 6. Marker genes + + Methods | Description + --- | --- + `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot + `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons
--- a/README.rst Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -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
--- 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 @@ -1,32 +1,46 @@ -<tool id="scanpy_cluster_reduce_dimension" name="Cluster and reduce dimension with scanpy" version="@galaxy_version@"> - <description></description> +<tool id="scanpy_cluster_reduce_dimension" name="Cluster," version="@galaxy_version@"> + <description>infer trajectories and embed with scanpy</description> <macros> <import>macros.xml</import> <xml name="pca_inputs"> - <param name="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/> - <param name="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/> + <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/> + <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/> <conditional name="pca"> - <param name="chunked" type="select" label="Type of PCA?"> + <param argument="chunked" type="select" label="Type of PCA?"> <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of `random_seed` and `svd_solver`)</option> <option value="False" selected="true">Full PCA</option> </param> <when value="True"> - <param name="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/> + <param argument="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/> </when> <when value="False"> - <param name="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" + <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Compute standard PCA from covariance matrix?" help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/> <expand macro="svd_solver"/> - <param name="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/> + <param argument="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/> </when> </conditional> + <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."/> </xml> - <token name="@CMD_pca_outputs@"><![CDATA[ -np.savetxt('$X_pca', adata.obsm['X_pca'], delimiter='\t') -np.savetxt('$PCs', adata.varm['PCs'], delimiter='\t') -np.savetxt('$variance', adata.uns['pca']['variance'], delimiter='\t') -np.savetxt('$variance_ratio', adata.uns['pca']['variance_ratio'], delimiter='\t') + <xml name="param_random_state"> + <param argument="random_state" type="integer" value="0" label="Random state" help="Change the initialization of the optimization."/> + </xml> + <xml name="param_use_weights"> + <param argument="use_weights" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use weights from knn graph?"/> + </xml> + <token name="@CMD_pca_help@"><![CDATA[ +The PCA is computed using the implementation of *scikit-learn*. + +The returned AnnData object contains: + +- PCA coordinates in the multi-dimensional observation annotation (obsm) +- Principal components containing the loadings in the multi-dimensional variable annotation (varm) +- The variance decomposition in the unstructured annotation (uns) + - Ratio of explained variance for PCA (variance) + - Explained variance, equivalent to the eigenvalues of the covariance matrix + +This data is accessible using the inspect tool for AnnData ]]></token> <token name="@CMD_pca_params@"><![CDATA[ data=adata, @@ -35,41 +49,14 @@ copy=False, chunked=$method.pca.chunked, #if $method.pca.chunked == 'True' - chunk_size=$method.pca.chunk_size + chunk_size=$method.pca.chunk_size, #else - zero_center='$method.pca.zero_center', + zero_center=$method.pca.zero_center, svd_solver='$method.pca.svd_solver', - random_state=$method.pca.random_state + random_state=$method.pca.random_state, #end if + use_highly_variable=$method.use_highly_variable ]]></token> - <xml name="penalty"> - <param argument="penalty" type="select" label="Norm used in the penalization" help=""> - <option value="l1">l1</option> - <option value="l2">l2</option> - <option value="customized">customized</option> - </param> - </xml> - <xml name="custom_penalty"> - <param argument="pen" type="text" value="" label="Norm used in the penalization" help=""/> - </xml> - <xml name="fit_intercept"> - <param argument="fit_intercept" type="boolean" truevalue="True" falsevalue="False" checked="true" - label="Should a constant (a.k.a. bias or intercept) be added to the decision function?" help=""/> - </xml> - <xml name="random_state"> - <param argument="random_state" type="integer" value="" optional="true" - label="The seed of the pseudo random number generator to use when shuffling the data" help=""/> - </xml> - <xml name="max_iter"> - <param argument="max_iter" type="integer" min="0" value="100" label="Maximum number of iterations taken for the solvers to converge" help=""/> - </xml> - <xml name="multi_class"> - <param argument="multi_class" type="select" label="Multi class" help=""> - <option value="ovr">ovr: a binary problem is fit for each label</option> - <option value="multinomial">multinomial: the multinomial loss fit across the entire probability distribution, even when the data is binary</option> - <option value="auto">auto: selects ‘ovr’ if the data is binary and otherwise selects ‘multinomial’</option> - </param> - </xml> </macros> <expand macro="requirements"/> <expand macro="version_command"/> @@ -90,20 +77,33 @@ #end if random_state=$method.random_state, key_added='$method.key_added', + directed=$method.directed, + use_weights=$method.use_weights, copy=False) -#elif $method.method == 'pp.pca' + +#else if $method.method == 'tl.leiden' +sc.tl.leiden( + adata=adata, + resolution=$method.resolution, + random_state=$method.random_state, + key_added='$method.key_added', + use_weights=$method.use_weights, + n_iterations=$method.n_iterations, + copy=False) + +#else if $method.method == 'pp.pca' sc.pp.pca(@CMD_pca_params@) -@CMD_pca_outputs@ -#elif $method.method == 'tl.pca' + +#else if $method.method == 'tl.pca' sc.tl.pca(@CMD_pca_params@) -@CMD_pca_outputs@ -#elif $method.method == 'tl.diffmap' + +#else if $method.method == 'tl.diffmap' sc.tl.diffmap( adata=adata, n_comps=$method.n_comps, copy =False) -np.savetxt('$X_diffmap', adata.obsm['X_diffmap'], delimiter='\t') -#elif $method.method == 'tl.tsne' + +#else if $method.method == 'tl.tsne' sc.tl.tsne( adata=adata, #if $method.n_pcs @@ -113,9 +113,10 @@ early_exaggeration=$method.early_exaggeration, learning_rate=$method.learning_rate, random_state=$method.random_state, + use_fast_tsne=$method.use_fast_tsne, copy=False) -np.savetxt('$X_tsne', adata.obsm['X_tsne'], delimiter='\t') -#elif $method.method == 'tl.umap' + +#else if $method.method == 'tl.umap' sc.tl.umap( adata=adata, min_dist=$method.min_dist, @@ -130,88 +131,49 @@ init_pos='$method.init_pos', random_state=$method.random_state, copy=False) -np.savetxt('$X_umap', adata.obsm['X_umap'], delimiter='\t') -#elif $method.method == 'pp.neighbors' -sc.pp.neighbors( + +#else if $method.method == 'tl.draw_graph' + + #if str($method.adjacency) != 'None' +from scipy import io +adjacency = io.mmread('$method.adjacency') + #end if + +sc.tl.draw_graph( adata=adata, - n_neighbors=$method.n_neighbors, - #if $method.n_pcs - n_pcs=$method.n_pcs, - #end if - knn=$method.knn, + layout='$method.layout', +#if str($method.root) != '' + #set $root=([int(x.strip()) for x in str($method.root).split(',')]) + root=$root, +#end if random_state=$method.random_state, - method='$method.pp_neighbors_method', - metric='$method.metric', - copy=False) -#elif $method.method == 'tl.rank_genes_groups' -sc.tl.rank_genes_groups( - adata=adata, - groupby='$method.groupby', - use_raw=$method.use_raw, - #if str($method.groups) != '' - groups='$method.groups', + #if str($method.init_pos) != '' + init_pos='$method.init_pos', #end if - #if $method.ref.rest == 'rest' - reference='$method.ref.rest', - #else - reference='$method.ref.reference', + #if str($method.adjacency) != 'None' + adjacency=adjacency, #end if - n_genes=$method.n_genes, - method='$method.tl_rank_genes_groups_method.method', - #if $method.tl_rank_genes_groups_method.method == 'logreg' - solver='$method.tl_rank_genes_groups_method.solver.solver', - #if $method.tl_rank_genes_groups_method.solver.solver == 'newton-cg' - penalty='l2', - fit_intercept=$method.tl_rank_genes_groups_method.solver.fit_intercept, - max_iter=$method.tl_rank_genes_groups_method.solver.max_iter, - multi_class='$method.tl_rank_genes_groups_method.solver.multi_class', - #else if $method.tl_rank_genes_groups_method.solver.solver == 'lbfgs' - penalty='l2', - fit_intercept=$method.tl_rank_genes_groups_method.solver.fit_intercept, - max_iter=$method.tl_rank_genes_groups_method.solver.max_iter, - multi_class='$method.tl_rank_genes_groups_method.solver.multi_class', - #else if $method.tl_rank_genes_groups_method.solver.solver == 'liblinear' - #if $method.tl_rank_genes_groups_method.solver.penalty.penalty == 'l1' - penalty='l1', - #else if $method.tl_rank_genes_groups_method.solver.penalty.penalty == 'l2' - penalty='l2', - dual=$method.tl_rank_genes_groups_method.solver.penalty.dual, - #else - penalty='$method.tl_rank_genes_groups_method.solver.penalty.pen', - #end if - fit_intercept=$method.tl_rank_genes_groups_method.solver.intercept_scaling.fit_intercept, - #if $method.tl_rank_genes_groups_method.solver.intercept_scaling.fit_intercept == 'True' - intercept_scaling=$method.tl_rank_genes_groups_method.solver.intercept_scaling.intercept_scaling, - #end if - #if $method.tl_rank_genes_groups_method.solver.random_state - random_state=$method.tl_rank_genes_groups_method.solver.random_state, - #end if - #else if $method.tl_rank_genes_groups_method.solver.solver == 'sag' - penalty='l2', - fit_intercept=$method.tl_rank_genes_groups_method.solver.fit_intercept, - #if $method.tl_rank_genes_groups_method.solver.random_state - random_state=$method.tl_rank_genes_groups_method.solver.random_state, - #end if - max_iter=$method.tl_rank_genes_groups_method.solver.max_iter, - multi_class='$method.tl_rank_genes_groups_method.solver.multi_class', - #else if $method.tl_rank_genes_groups_method.solver.solver == 'saga' - #if $method.tl_rank_genes_groups_method.solver.penalty.penalty == 'l1' - penalty='l1', - #else if $method.tl_rank_genes_groups_method.solver.penalty.penalty == 'l2' - penalty='l2', - #else - penalty='$method.tl_rank_genes_groups_method.solver.penalty.pen', - #end if - fit_intercept=$method.tl_rank_genes_groups_method.solver.fit_intercept, - multi_class='$method.tl_rank_genes_groups_method.solver.multi_class', - #end if - tol=$method.tl_rank_genes_groups_method.tol, - C=$method.tl_rank_genes_groups_method.c, + #if str($method.key_ext) != '' + key_ext='$method.key_ext', #end if - only_positive=$method.only_positive) -pd.options.display.precision = 15 -pd.DataFrame(adata.uns['rank_genes_groups']['names']).to_csv("$names", sep="\t", index = False) -pd.DataFrame(adata.uns['rank_genes_groups']['scores']).to_csv("$scores", sep="\t", index = False) + copy=False) + +#else if $method.method == "tl.paga" +sc.tl.paga( + adata=adata, + groups='$method.groups', + use_rna_velocity=$method.use_rna_velocity, + model='$method.model', + copy=False) + +#else if $method.method == "tl.dpt" +sc.tl.dpt( + adata=adata, + n_dcs=$method.n_dcs, + n_branchings=$method.n_branchings, + min_group_size=$method.min_group_size, + allow_kendall_tau_shift=$method.allow_kendall_tau_shift, + copy=False) #end if @CMD_anndata_write_outputs@ @@ -221,16 +183,16 @@ <expand macro="inputs_anndata"/> <conditional name="method"> <param argument="method" type="select" label="Method used for plotting"> - <!--<option value="tl.leiden">, using `tl.leiden`</option>!--> <option value="tl.louvain">Cluster cells into subgroups, using `tl.louvain`</option> + <option value="tl.leiden">Cluster cells into subgroups, using `tl.leiden`</option> <option value="pp.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`</option> <option value="tl.pca">Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`</option> <option value="tl.diffmap">Diffusion Maps, using `tl.diffmap`</option> <option value="tl.tsne">t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`</option> <option value="tl.umap">Embed the neighborhood graph using UMAP, using `tl.umap`</option> - <!--<option value="tl.phate">, using `tl.phate`</option>!--> - <option value="pp.neighbors">Compute a neighborhood graph of observations, using `pp.neighbors`</option> - <option value="tl.rank_genes_groups">Rank genes for characterizing groups, using `tl.rank_genes_groups`</option> + <option value="tl.draw_graph">Force-directed graph drawing, using `tl.draw_graph`</option> + <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using `tl.dpt`</option> + <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`</option> </param> <when value="tl.louvain"> <conditional name="flavor"> @@ -245,8 +207,17 @@ </when> <when value="igraph"/> </conditional> - <param argument="random_state" type="integer" value="0" label="Random state" help="Change the initialization of the optimization."/> + <expand macro="param_random_state"/> <param argument="key_added" type="text" value="louvain" optional="true" label="Key under which to add the cluster labels" help=""/> + <param argument="directed" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Interpret the adjacency matrix as directed graph?"/> + <expand macro="param_use_weights"/> + </when> + <when value="tl.leiden"> + <param argument="resolution" type="float" value="1" label="Coarseness of the clusterin" help="Higher values lead to more clusters"/> + <expand macro="param_random_state"/> + <param argument="key_added" type="text" value="leiden" label="Key under which to add the cluster labels" help=""/> + <expand macro="param_use_weights"/> + <param argument="n_iterations" type="integer" value="-1" label="How many iterations of the Leiden clustering algorithm to perform." help="Positive values above 2 define the total number of iterations to perform, -1 has the algorithm run until it reaches its optimal clustering."/> </when> <when value="pp.pca"> <expand macro="pca_inputs"/> @@ -263,6 +234,7 @@ <param name="early_exaggeration" type="float" value="12.0" label="Early exaggeration" help="Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them. For larger values, the space between natural clusters will be larger in the embedded space. Again, the choice of this parameter is not very critical. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high."/> <param name="learning_rate" type="float" value="1000" label="Learning rate" help="The learning rate can be a critical parameter. It should be between 100 and 1000. If the cost function increases during initial optimization, the early exaggeration factor or the learning rate might be too high. If the cost function gets stuck in a bad local minimum increasing the learning rate helps sometimes."/> <param name="random_state" type="integer" value="0" label="Random state" help="Change this to use different intial states for the optimization"/> + <param argument="use_fast_tsne" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use the MulticoreTSNE package if possible?"/> </when> <when value="tl.umap"> <param argument="min_dist" type="float" value="0.5" label="Effective minimum distance between embedded points" help="Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the `spread` value, which determines the scale at which embedded points will be spread out. The default of in the `umap-learn` package is 0.1."/> @@ -277,158 +249,39 @@ <option value="spectral" selected="true">Spectral embedding of the graph</option> <option value="random">Initial embedding positions at random</option> </param> - <param argument="random_state" type="integer" value="0" label="Random state" help="Change this to use different intial states for the optimization"/> + <expand macro="param_random_state"/> + </when> + <when value="tl.draw_graph"> + <expand macro="param_layout"/> + <expand macro="param_root"/> + <expand macro="param_random_state"/> + <param argument="init_pos" type="text" optional="true" value="" label="Precomputed coordinates for initialization" help="It should be a valid 2d observation (e.g. paga)"/> + <param argument="adjacency" type="data" format="mtx" optional="true" label="Sparse adjacency matrix of the graph" help="If not set, it uses the unstructured annotation (uns) / neighbors / connectivities"/> + <param argument="key_ext" type="text" optional="true" value="" label="External key" help="If not set, it appends `layout`"/> </when> - <when value="pp.neighbors"> - <param argument="n_neighbors" type="integer" min="0" value="15" label="The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation" help="Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100. If `knn` is `True`, number of nearest neighbors to be searched. If `knn` is `False`, a Gaussian kernel width is set to the distance of the `n_neighbors` neighbor."/> - <param argument="n_pcs" type="integer" min="0" value="" optional="true" label="Number of PCs to use" help=""/> - <param argument="knn" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use a hard threshold to restrict the number of neighbors to n_neighbors?" help="If true, it considers a knn graph. Otherwise, it uses a Gaussian Kernel to assign low weights to neighbors more distant than the `n_neighbors` nearest neighbor."/> - <param argument="random_state" type="integer" value="0" label="Numpy random seed" help=""/> - <param name="pp_neighbors_method" argument="method" type="select" label="Method for computing connectivities" help=""> - <option value="umap">umap (McInnes et al, 2018)</option> - <option value="gauss">gauss: Gauss kernel following (Coifman et al 2005) with adaptive width (Haghverdi et al 2016)</option> - </param> - <param argument="metric" type="select" label="Distance metric" help=""> - <expand macro="distance_metric_options"/> + <when value="tl.dpt"> + <param argument="n_dcs" type="integer" min="0" value="10" label="Number of diffusion components to use" help=""/> + <param argument="n_branchings" type="integer" min="0" value="0" label="Number of branchings to detect" help=""/> + <param argument="min_group_size" type="float" min="0" value="0.01" label="Min group size" help="During recursive splitting of branches ('dpt groups') for `n_branchings` > 1, do not consider groups that contain less than `min_group_size` data points. If a float, `min_group_size` refers to a fraction of the total number of data points."/> + <param argument="allow_kendall_tau_shift" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Allow Kendal tau shift?" help="If a very small branch is detected upon splitting, shift away from maximum correlation in Kendall tau criterion of Haghverdi et al (2016) to stabilize the splitting."/> + </when> + <when value="tl.paga"> + <param argument="groups" type="text" value="louvain" label="Key for categorical in the input" help="You can pass your predefined groups by choosing any categorical annotation of observations (`adata.obs`)."/> + <param argument="use_rna_velocity" type="boolean" truevalue="False" falsevalue="False" checked="false" label="Use RNA velocity to orient edges in the abstracted graph and estimate transitions?" help="Requires that `adata.uns` contains a directed single-cell graph with key `['velocyto_transitions']`. This feature might be subject to change in the future."/> + <param argument="model" type="select" label="PAGA connectivity model" help=""> + <option value="v1.2">v1.2</option> + <option value="v1.0">v1.0</option> </param> </when> - <when value="tl.rank_genes_groups"> - <param argument="groupby" type="text" value="" label="The key of the observations grouping to consider" help=""/> - <expand macro="param_use_raw"/> - <param argument="groups" type="text" value="" label="Subset of groups to which comparison shall be restricted" help="e.g. ['g1', 'g2', 'g3']. If not passed, a ranking will be generated for all groups."/> - <conditional name="ref"> - <param name="rest" type="select" label="Comparison"> - <option value="rest">Compare each group to the union of the rest of the group</option> - <option value="group_id">Compare with respect to a specific group</option> - </param> - <when value="rest"/> - <when value="group_id"> - <param argument="reference" type="text" value="" label="Group identifier with respect to which compare"/> - </when> - </conditional> - <param argument="n_genes" type="integer" min="0" value="100" label="The number of genes that appear in the returned tables" help=""/> - <conditional name="tl_rank_genes_groups_method"> - <param argument="method" type="select" label="Method"> - <option value="t-test">t-test</option> - <option value="wilcoxon">Wilcoxon-Rank-Sum</option> - <option value="t-test_overestim_var" selected="true">t-test with overestimate of variance of each group</option> - <option value="logreg">Logistic regression</option> - </param> - <when value="t-test"/> - <when value="wilcoxon"/> - <when value="t-test_overestim_var"/> - <when value="logreg"> - <conditional name="solver"> - <param argument="solver" type="select" label="Algorithm to use in the optimization problem" help="For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones. For multiclass problems, only ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ handle multinomial loss; ‘liblinear’ is limited to one-versus-rest schemes. ‘newton-cg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty, whereas ‘liblinear’ and ‘saga’ handle L1 penalty."> - <option value="newton-cg">newton-cg</option> - <option value="lbfgs">lbfgs</option> - <option value="liblinear">liblinear</option> - <option value="sag">sag</option> - <option value="saga">saga</option> - </param> - <when value="newton-cg"> - <expand macro="fit_intercept"/> - <expand macro="max_iter"/> - <expand macro="multi_class"/> - </when> - <when value="lbfgs"> - <expand macro="fit_intercept"/> - <expand macro="max_iter"/> - <expand macro="multi_class"/> - </when> - <when value="liblinear"> - <conditional name="penalty"> - <expand macro="penalty"/> - <when value="l1"/> - <when value="l2"> - <param argument="dual" type="boolean" truevalue="True" falsevalue="False" checked="false" - label="Dual (not primal) formulation?" help="Prefer primal when n_samples > n_features"/> - </when> - <when value="customized"> - <expand macro="custom_penalty"/> - </when> - </conditional> - <conditional name="intercept_scaling"> - <param argument="fit_intercept" type="select" - label="Should a constant (a.k.a. bias or intercept) be added to the decision function?" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <param argument="intercept_scaling" type="float" value="1.0" - label="Intercept scaling" - help="x becomes [x, self.intercept_scaling], i.e. a 'synthetic' feature with constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic_feature_weight."/> - </when> - <when value="False"/> - </conditional> - <expand macro="random_state"/> - </when> - <when value="sag"> - <expand macro="fit_intercept"/> - <expand macro="random_state"/> - <expand macro="max_iter"/> - <expand macro="multi_class"/> - </when> - <when value="saga"> - <conditional name="penalty"> - <expand macro="penalty"/> - <when value="l1"/> - <when value="l2"/> - <when value="customized"> - <expand macro="custom_penalty"/> - </when> - </conditional> - <expand macro="fit_intercept"/> - <expand macro="multi_class"/> - </when> - </conditional> - <param argument="tol" type="float" value="1e-4" label="Tolerance for stopping criteria" help=""/> - <param argument="c" type="float" value="1.0" label="Inverse of regularization strength" - help="It must be a positive float. Like in support vector machines, smaller values specify stronger regularization."/> - </when> - </conditional> - <param argument="only_positive" type="boolean" truevalue="True" falsevalue="False" checked="true" - label="Only consider positive differences?" help=""/> - </when> - </conditional> - <expand macro="anndata_output_format"/> + </conditional> </inputs> <outputs> <expand macro="anndata_outputs"/> - <data name="X_pca" format="tabular" label="${tool.name} on ${on_string}: PCA representation of data"> - <filter>method['method'] == 'pp.pca' or method['method'] == 'tl.pca'</filter> - </data> - <data name="PCs" format="tabular" label="${tool.name} on ${on_string}: Principal components containing the loadings"> - <filter>method['method'] == 'pp.pca' or method['method'] == 'tl.pca'</filter> - </data> - <data name="variance_ratio" format="tabular" label="${tool.name} on ${on_string}: Ratio of explained variance"> - <filter>method['method'] == 'pp.pca' or method['method'] == 'tl.pca'</filter> - </data> - <data name="variance" format="tabular" label="${tool.name} on ${on_string}: Explained variance, equivalent to the eigenvalues of the covariance matrix"> - <filter>method['method'] == 'pp.pca' or method['method'] == 'tl.pca'</filter> - </data> - <data name="X_diffmap" format="tabular" label="${tool.name} on ${on_string}: Diffusion map representation"> - <filter>method['method'] == 'tl.diffmap'</filter> - </data> - <data name="X_tsne" format="tabular" label="${tool.name} on ${on_string}: tSNE coordinates"> - <filter>method['method'] == 'tl.tsne'</filter> - </data> - <data name="X_umap" format="tabular" label="${tool.name} on ${on_string}: UMAP coordinates"> - <filter>method['method'] == 'tl.umap'</filter> - </data> - <data name="names" format="tabular" label="${tool.name} on ${on_string}: Gene names"> - <filter>method['method'] == 'tl.rank_genes_groups'</filter> - </data> - <data name="scores" format="tabular" label="${tool.name} on ${on_string}: Scores"> - <filter>method['method'] == 'tl.rank_genes_groups'</filter> - </data> </outputs> <tests> - <test expect_num_outputs="1"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> + <test> + <!-- test 1 --> + <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.louvain"/> <conditional name="flavor"> @@ -437,8 +290,9 @@ </conditional> <param name="random_state" value="10"/> <param name="key_added" value="louvain"/> + <param name="directed" value="true"/> + <param name="use_weights" value="false"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.tl.louvain"/> <has_text_matching expression="adata=adata"/> @@ -446,70 +300,63 @@ <has_text_matching expression="resolution=1.0"/> <has_text_matching expression="random_state=10"/> <has_text_matching expression="key_added='louvain'"/> + <has_text_matching expression="directed=True"/> + <has_text_matching expression="use_weights=False"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"/> + <output name="anndata_out" file="tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="5"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> + <test> + <!-- test 2 --> + <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> + <conditional name="method"> + <param name="method" value="tl.leiden"/> + <param name="random_state" value="1"/> + <param name="random_state" value="10"/> + <param name="key_added" value="leiden"/> + <param name="use_weights" value="false"/> + <param name="n_iterations" value="-1"/> </conditional> + <assert_stdout> + <has_text_matching expression="sc.tl.leiden"/> + <has_text_matching expression="resolution=1"/> + <has_text_matching expression="random_state=10"/> + <has_text_matching expression="key_added='leiden'"/> + <has_text_matching expression="use_weights=False"/> + <has_text_matching expression="n_iterations=-1"/> + </assert_stdout> + <output name="anndata_out" file="tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 2 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="pp.pca"/> <param name="n_comps" value="50"/> <param name="dtype" value="float32"/> <conditional name="pca"> <param name="chunked" value="False"/> - <param name="zero_center" value="True"/> + <param name="zero_center" value="true"/> <param name="svd_solver" value="auto"/> <param name="random_state" value="0"/> </conditional> + <param name="use_highly_variable" value="false"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.pp.pca"/> <has_text_matching expression="n_comps=50"/> <has_text_matching expression="dtype='float32'"/> <has_text_matching expression="copy=False"/> <has_text_matching expression="chunked=False"/> - <has_text_matching expression="zero_center='True'"/> + <has_text_matching expression="zero_center=True"/> <has_text_matching expression="svd_solver='auto'"/> <has_text_matching expression="random_state=0"/> + <has_text_matching expression="use_highly_variable=False"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.pca.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="X_pca"> - <assert_contents> - <has_text_matching expression="-2.579\d{15}e-01" /> - <has_text_matching expression="3.452\d{15}e-01" /> - <has_text_matching expression="-6.088\d{15}e-03" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="PCs"> - <assert_contents> - <has_text_matching expression="-2.285\d{15}e-01" /> - <has_text_matching expression="-3.042\d{15}e-01" /> - <has_text_matching expression="-2.863\d{15}e-02" /> - <has_text_matching expression="1.294\d{15}e-01" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="variance_ratio"> - <assert_contents> - <has_text_matching expression="2.148\d{15}e-01" /> - <has_text_matching expression="7.596\d{15}e-02" /> - <has_text_matching expression="5.033\d{15}e-03" /> - <has_text_matching expression="2.801\d{15}e-05" /> - <has_n_columns n="1" /> - </assert_contents> - </output> - <output name="variance" file="pp.pca.variance.krumsiek11.tabular" /> + <output name="anndata_out" file="pp.pca.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="5"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + <!--<test> + < test 3 > + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="pp.pca"/> <param name="n_comps" value="20"/> @@ -518,8 +365,8 @@ <param name="chunked" value="True"/> <param name="chunk_size" value="50"/> </conditional> + <param name="use_highly_variable" value="false"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.pp.pca"/> <has_text_matching expression="data=adata"/> @@ -528,45 +375,14 @@ <has_text_matching expression="copy=False"/> <has_text_matching expression="chunked=True"/> <has_text_matching expression="chunk_size=50"/> + <has_text_matching expression="use_highly_variable=False"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.pca.krumsiek11_chunk.h5ad" ftype="h5" compare="sim_size"/> - <output name="X_pca"> - <assert_contents> - <has_text_matching expression="1.290\d{15}e-03" /> - <has_text_matching expression="9.231\d{15}e-04" /> - <has_text_matching expression="-3.498\d{15}e-02" /> - <has_text_matching expression="-4.921\d{15}e-03" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="PCs"> - <assert_contents> - <has_text_matching expression="2.35298924\d\d\d\d\d\d\d\d\d\de-0\d" /> - <has_text_matching expression="2.4286999\d\d\d\d\d\d\d\d\d\d\de-0\d" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="variance_ratio"> - <assert_contents> - <has_text text="6.4362" /> - <has_text text="2.7348" /> - <has_n_columns n="1" /> - </assert_contents> - </output> - <output name="variance"> - <assert_contents> - <has_text_matching expression="7.540\d{15}e-01" /> - <has_text_matching expression="1.173\d{15}e-03" /> - <has_text_matching expression="3.204\d{15}e-05" /> - <has_n_columns n="1" /> - </assert_contents> - </output> + <output name="anndata_out" file="pp.pca.krumsiek11_chunk.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="5"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + --> + <test> + <!-- test 3 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="tl.pca"/> <param name="n_comps" value="50"/> @@ -577,75 +393,36 @@ <param name="svd_solver" value="auto"/> <param name="random_state" value="0"/> </conditional> + <param name="use_highly_variable" value="false"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.tl.pca"/> <has_text_matching expression="n_comps=50"/> <has_text_matching expression="dtype='float32'"/> <has_text_matching expression="copy=False"/> <has_text_matching expression="chunked=False"/> - <has_text_matching expression="zero_center='True'"/> + <has_text_matching expression="zero_center=True"/> <has_text_matching expression="svd_solver='auto'"/> + <has_text_matching expression="use_highly_variable=False"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.pca.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="X_pca"> - <assert_contents> - <has_text_matching expression="-6.366\d{15}e-01" /> - <has_text_matching expression="5.702\d{15}e-03" /> - <has_text_matching expression="1.862\d{15}e-02" /> - <has_text_matching expression="-6.861\d{15}e-02" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="PCs"> - <assert_contents> - <has_text_matching expression="1.341\d{15}e-01" /> - <has_text_matching expression="-3.478\d{15}e-03" /> - <has_text_matching expression="-4.890\d{15}e-02" /> - <has_text_matching expression="-2.628\d{15}e-02" /> - <has_n_columns n="10" /> - </assert_contents> - </output> - <output name="variance_ratio"> - <assert_contents> - <has_text_matching expression="6.436\d{15}e-01" /> - <has_text_matching expression="1.316\d{15}e-04" /> - <has_text_matching expression="2.801\d{15}e-05" /> - <has_n_columns n="1" /> - </assert_contents> - </output> - <output name="variance"> - <assert_contents> - <has_text_matching expression="4.575\d{15}e-02" /> - <has_text_matching expression="2.166\d{15}e-02" /> - <has_text_matching expression="5.896\d{15}e-03" /> - <has_n_columns n="1" /> - </assert_contents> - </output> + <output name="anndata_out" file="tl.pca.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="2"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> + <test> + <!-- test 4 --> + <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.diffmap"/> <param name="n_comps" value="15"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.tl.diffmap"/> <has_text_matching expression="n_comps=15"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"/> - <output name="X_diffmap" file="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular"/> + <output name="anndata_out" file="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="2"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + <test> + <!-- test 5 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> <param name="method" value="tl.tsne"/> <param name="n_pcs" value="10"/> @@ -653,8 +430,8 @@ <param name="early_exaggeration" value="12.0"/> <param name="learning_rate" value="1000"/> <param name="random_state" value="0"/> + <param name="use_fast_tsne" value="true"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.tl.tsne"/> <has_text_matching expression="n_pcs=10"/> @@ -662,15 +439,13 @@ <has_text_matching expression="early_exaggeration=12.0"/> <has_text_matching expression="learning_rate=1000.0"/> <has_text_matching expression="random_state=0"/> + <has_text_matching expression="use_fast_tsne=True"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.tsne.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="X_tsne" file="tl.tsne.krumsiek11_X_tsne.tabular"/> + <output name="anndata_out" file="tl.tsne.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="2" > - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> + <test> + <!-- test 6 --> + <param name="adata" value="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="tl.umap"/> <param name="min_dist" value="0.5"/> @@ -683,7 +458,6 @@ <param name="init_pos" value="spectral"/> <param name="random_state" value="0"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.tl.umap"/> <has_text_matching expression="min_dist=0.5"/> @@ -696,263 +470,67 @@ <has_text_matching expression="init_pos='spectral'"/> <has_text_matching expression="random_state=0"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"> - <assert_contents> - <has_h5_keys keys="X, obs, obsm, uns, var" /> - </assert_contents> - </output> - <output name="X_umap"> - <assert_contents> - <has_text text="2.31791388" /> - <has_text text="-4.8602690" /> - <has_text text="-1.8031970" /> - <has_text text="2.31166780" /> - <has_n_columns n="2" /> - </assert_contents> - </output> - </test> - <test expect_num_outputs="1"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.neighbors"/> - <param name="n_neighbors" value="15"/> - <param name="knn" value="True"/> - <param name="random_state" value="0"/> - <param name="pp_neighbors_method" value="umap"/> - <param name="metric" value="euclidean"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.neighbors"/> - <has_text_matching expression="n_neighbors=15"/> - <has_text_matching expression="knn=True"/> - <has_text_matching expression="random_state=0"/> - <has_text_matching expression="method='umap'"/> - <has_text_matching expression="metric='euclidean'"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"> + <output name="anndata_out" file="tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"> <assert_contents> <has_h5_keys keys="X, obs, obsm, uns, var" /> </assert_contents> </output> </test> - <test expect_num_outputs="1"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> + <test> + <!-- test 7 --> + <param name="adata" value="pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad"/> <conditional name="method"> - <param name="method" value="pp.neighbors"/> - <param name="n_neighbors" value="15"/> - <param name="knn" value="True"/> - <param name="pp_neighbors_method" value="gauss"/> - <param name="metric" value="braycurtis"/> + <param name="method" value="tl.draw_graph"/> + <param name="layout" value="fa"/> + <param name="random_state" value="0"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.neighbors"/> - <has_text_matching expression="n_neighbors=15"/> - <has_text_matching expression="knn=True"/> - <has_text_matching expression="random_state=0"/> - <has_text_matching expression="method='gauss'"/> - <has_text_matching expression="metric='braycurtis'"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"/> - </test> - <test expect_num_outputs="3"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="tl.rank_genes_groups"/> - <param name="groupby" value="cell_type"/> - <param name="use_raw" value="True"/> - <conditional name="ref"> - <param name="rest" value="rest"/> - </conditional> - <param name="n_genes" value="100"/> - <conditional name="tl_rank_genes_groups_method"> - <param name="method" value="t-test_overestim_var"/> - </conditional> - <param name="only_positive" value="True"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> - <has_text_matching expression="sc.tl.rank_genes_groups"/> - <has_text_matching expression="groupby='cell_type'"/> - <has_text_matching expression="use_raw=True"/> - <has_text_matching expression="reference='rest'"/> - <has_text_matching expression="n_genes=100"/> - <has_text_matching expression="method='t-test_overestim_var'"/> - <has_text_matching expression="only_positive=True"/> + <has_text_matching expression="sc.tl.draw_graph"/> + <has_text_matching expression="layout='fa'"/> + <has_text_matching expression="random_state=0"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.rank_genes_groups.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="names"> - <assert_contents> - <has_n_columns n="5" /> - <has_text_matching expression="Ery\tMk\tMo\tNeu\tprogenitor"/> - <has_text_matching expression="Gata1\tFog1\tCebpa\tFli1\tGata2"/> - <has_text_matching expression="EgrNab\tEgrNab\tSCL\tSCL\tGfi1"/> - </assert_contents> - </output> - <output name="scores"> - <assert_contents> - <has_n_columns n="5" /> - <has_text_matching expression="Ery\tMk\tMo\tNeu\tprogenitor"/> - <has_text_matching expression="18.86\d{4}"/> - <has_text_matching expression="17.85\d{4}"/> - <has_text_matching expression="-2.63\d{4}"/> - <has_text_matching expression="-2.98\d{4}"/> - <has_text_matching expression="-6.41\d{4}"/> - </assert_contents> - </output> + <output name="anndata_out" file="tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="3"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pbmc68k_reduced.h5ad" /> - </conditional> + <test> + <!-- test 8 --> + <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad"/> <conditional name="method"> - <param name="method" value="tl.rank_genes_groups"/> - <param name="groupby" value="louvain"/> - <param name="use_raw" value="True"/> - <conditional name="ref"> - <param name="rest" value="rest"/> - </conditional> - <param name="n_genes" value="100"/> - <conditional name="tl_rank_genes_groups_method"> - <param name="method" value="logreg"/> - <conditional name="solver"> - <param name="solver" value="newton-cg"/> - <param name="fit_intercept" value="True"/> - <param name="max_iter" value="100"/> - <param name="multi_class" value="auto"/> - </conditional> - <param name="tol" value="1e-4"/> - <param name="c" value="1.0"/> - </conditional> - <param name="only_positive" value="True"/> + <param name="method" value="tl.paga"/> + <param name="groups" value="paul15_clusters"/> + <param name="use_rna_velocity" value="False"/> + <param name="model" value="v1.2"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> - <has_text_matching expression="sc.tl.rank_genes_groups"/> - <has_text_matching expression="groupby='louvain'"/> - <has_text_matching expression="use_raw=True"/> - <has_text_matching expression="reference='rest'"/> - <has_text_matching expression="n_genes=100"/> - <has_text_matching expression="method='logreg'"/> - <has_text_matching expression="solver='newton-cg'"/> - <has_text_matching expression="penalty='l2'"/> - <has_text_matching expression="fit_intercept=True"/> - <has_text_matching expression="max_iter=100"/> - <has_text_matching expression="multi_class='auto'"/> - <has_text_matching expression="tol=0.0001"/> - <has_text_matching expression="C=1.0"/> - <has_text_matching expression="only_positive=True"/> + <has_text_matching expression="sc.tl.paga"/> + <has_text_matching expression="groups='paul15_clusters'"/> + <has_text_matching expression="use_rna_velocity=False"/> + <has_text_matching expression="model='v1.2'"/> </assert_stdout> - <output name="anndata_out_h5ad" ftype="h5"> - <assert_contents> - <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" /> - </assert_contents> - </output> - <output name="names"> - <assert_contents> - <has_n_columns n="7" /> - <has_text_matching expression="IL32\tFCGR3A\tFCER1A\tLTB\tCPVL\tIGJ\tPRSS57"/> - <has_text_matching expression="KIAA0101\tFCER1G\tHLA-DMA\tHLA-DQA1\tNAAA\tMANF\tCCDC104"/> - <has_text_matching expression="CCNB2\t"/> - </assert_contents> - </output> - <output name="scores"> - <assert_contents> - <has_n_columns n="7" /> - <has_text_matching expression="0.088\d+"/> - <has_text_matching expression="0.114\d+"/> - <has_text_matching expression="0.034\d+"/> - <has_text_matching expression="0.035\d+"/> - <has_text_matching expression="0.041\d+"/> - </assert_contents> - </output> + <output name="anndata_out" file="tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test expect_num_outputs="3"> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pbmc68k_reduced.h5ad" /> - </conditional> + <test> + <!-- test 9 --> + <param name="adata" value="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> <conditional name="method"> - <param name="method" value="tl.rank_genes_groups"/> - <param name="groupby" value="louvain"/> - <param name="use_raw" value="True"/> - <conditional name="ref"> - <param name="rest" value="rest"/> - </conditional> - <param name="n_genes" value="100"/> - <conditional name="tl_rank_genes_groups_method"> - <param name="method" value="logreg"/> - <conditional name="solver"> - <param name="solver" value="liblinear"/> - <conditional name="penalty"> - <param name="penalty" value="l2"/> - <param name="dual" value="False"/> - <conditional name="intercept_scaling"> - <param name="fit_intercept" value="True"/> - <param name="intercept_scaling" value="1.0" /> - </conditional> - <param name="random_state" value="1"/> - </conditional> - </conditional> - <param name="tol" value="1e-4"/> - <param name="c" value="1.0"/> - </conditional> - <param name="only_positive" value="True"/> + <param name="method" value="tl.dpt"/> + <param name="n_dcs" value="15"/> + <param name="n_branchings" value="1"/> + <param name="min_group_size" value="0.01"/> + <param name="allow_kendall_tau_shift" value="True"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> - <has_text_matching expression="sc.tl.rank_genes_groups"/> - <has_text_matching expression="groupby='louvain'"/> - <has_text_matching expression="use_raw=True"/> - <has_text_matching expression="reference='rest'"/> - <has_text_matching expression="n_genes=100"/> - <has_text_matching expression="method='logreg'"/> - <has_text_matching expression="solver='liblinear'"/> - <has_text_matching expression="penalty='l2'"/> - <has_text_matching expression="dual=False"/> - <has_text_matching expression="fit_intercept=True"/> - <has_text_matching expression="intercept_scaling=1.0"/> - <has_text_matching expression="tol=0.0001"/> - <has_text_matching expression="C=1.0"/> - <has_text_matching expression="only_positive=True"/> + <has_text_matching expression="sc.tl.dpt"/> + <has_text_matching expression="n_dcs=15"/> + <has_text_matching expression="n_branchings=1"/> + <has_text_matching expression="min_group_size=0.01"/> + <has_text_matching expression="allow_kendall_tau_shift=True"/> </assert_stdout> - <output name="anndata_out_h5ad" ftype="h5"> - <assert_contents> - <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" /> - </assert_contents> - </output> - <output name="names"> - <assert_contents> - <has_n_columns n="7" /> - <has_text_matching expression="AES\tLST1\tRNASE6\tPLAC8\tCST3\tSEC11C\tNASP"/> - <has_text_matching expression="GIMAP4\tMIS18BP1\tLILRB4\tTUBA4A\tCOMTD1\tSLC25A4\tLEPROT"/> - <has_text_matching expression="GGH\tLYN\tMAGOHB\tAL928768.3\tITGB2-AS1\tCENPH\tASRGL1"/> - </assert_contents> - </output> - <output name="scores"> - <assert_contents> - <has_n_columns n="7" /> - <has_text_matching expression="0.1680\d{4}\t0.2156\d{4}\t0.281\d{4}\t0.2100\d{4}\t0.2332\d{4}\t0.1586\d{4}\t0.12057\d{4}"/> - <has_text_matching expression="0.0784\d{4}\t0.0699\d{4}\t0.06912\d{4}\t0.05364\d{4}\t0.03933\d{4}\t0.03994\d{4}\t0.0411\d{4}"/> - <has_text_matching expression="0.06232\d{4}\t0.05563\d{4}\t0.0565\d{4}\t0.04164\d{4}\t0.02636\d{4}\t0.03002\d{4}\t0.032\d{4}"/> - </assert_contents> - </output> + <output name="anndata_out" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> </test> </tests> <help><![CDATA[ -Cluster cells into subgroups, using `tl.louvain` -================================================ +Cluster cells into subgroups (`tl.louvain`) +=========================================== Cluster cells using the Louvain algorithm (Blondel et al, 2008) in the implementation of Traag et al,2017. The Louvain algorithm has been proposed for single-cell @@ -961,25 +539,33 @@ This requires to run `pp.neighbors`, first. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.louvain.html#scanpy.api.tl.louvain>`_ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.louvain.html>`_ + +Cluster cells into subgroups (`tl.leiden`) +========================================== + +Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved version of the Louvain algorithm (Blondel et al, 2008). + +The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015. + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.leiden.html>`_ Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca` ============================================================================================================ -It uses the implementation of *scikit-learn*. +@CMD_pca_outputs@ More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.pca.html#scanpy.api.pp.pca>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.pca.html>`__ Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca` ============================================================================================================ -It uses the implementation of *scikit-learn*. - -Diffusion Maps +@CMD_pca_outputs@ More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.pca.html#scanpy.api.tl.pca>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.pca.html>`__ Diffusion Maps, using `tl.diffmap` ================================== @@ -996,10 +582,12 @@ `method=='umap'`. Differences between these options shouldn't usually be dramatic. -It returns `X_diffmap`, diffusion map representation of data, which is the right eigen basis of the transition matrix with eigenvectors as columns. +The diffusion map representation of data are added to the return AnnData in the multi-dimensional +observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors +as colum. It can be accessed using the inspect tool for AnnData More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.diffmap.html#scanpy.api.tl.diffmap>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.diffmap.html>`__ t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne` ======================================================================= @@ -1011,7 +599,7 @@ It returns `X_tsne`, tSNE coordinates of data. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.tsne.html#scanpy.api.tl.tsne>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.tsne.html>`__ Embed the neighborhood graph using UMAP, using `tl.umap` ======================================================== @@ -1027,34 +615,90 @@ (McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint <https://doi.org/10.1101/298430>`__. -It returns `X_umap`, UMAP coordinates of data. +The UMAP coordinates of data are added to the return AnnData in the multi-dimensional +observations annotation (obsm). This data is accessible using the inspect tool for AnnData + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.umap.html>`__ + +Force-directed graph drawing, using `tl.draw_graph` +=================================================== + +Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. +It has been suggested for visualizing single-cell data by Islam et al, 11. +Many other layouts as implemented in igraph are available. Similar approaches have been used by +Zunder et al, 2015 or Weinreb et al, 2016. + +This is an alternative to tSNE that often preserves the topology of the data better. +This requires to run `pp.neighbors`, first. + +The default layout (ForceAtlas2) uses the package fa2. + +The coordinates of graph layout are added to the return AnnData in the multi-dimensional +observations annotation (obsm). This data is accessible using the inspect tool for AnnData. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.umap.html#scanpy.api.tl.umap>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.draw_graph.html>`__ + +Infer progression of cells through geodesic distance along the graph (`tl.dpt`) +=============================================================================== -Compute a neighborhood graph of observations, using `pp.neighbors` -================================================================== +Reconstruct the progression of a biological process from snapshot +data. `Diffusion Pseudotime` has been introduced by Haghverdi et al (2016) and +implemented within Scanpy (Wolf et al, 2017). Here, we use a further developed +version, which is able to deal with disconnected graphs (Wolf et al, 2017) and can +be run in a `hierarchical` mode by setting the parameter +`n_branchings>1`. We recommend, however, to only use +`tl.dpt` for computing pseudotime (`n_branchings=0`) and +to detect branchings via `paga`. For pseudotime, you need +to annotate your data with a root cell. -The neighbor search efficiency of this heavily relies on UMAP (McInnes et al, 2018), -which also provides a method for estimating connectivities of data points - -the connectivity of the manifold (`method=='umap'`). If `method=='diffmap'`, -connectivities are computed according to Coifman et al (2005), in the adaption of -Haghverdi et al (2016). +This requires to run `pp.neighbors`, first. In order to +reproduce the original implementation of DPT, use `method=='gauss'` in +this. Using the default `method=='umap'` only leads to minor quantitative +differences, though. + + +If `n_branchings==0`, no field `dpt_groups` will be written. + +- 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. +- 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. + +The tool is similar to the R package `destiny` of Angerer et al (2016). + +More details on the `tl.dpt scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_ -More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.neighbors.html#scanpy.api.pp.neighbors>`__ + +Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`) +======================================================================================= -Rank genes for characterizing groups, using `tl.rank_genes_groups` -================================================================== +By quantifying the connectivity of partitions (groups, clusters) of the +single-cell graph, partition-based graph abstraction (PAGA) generates a much +simpler abstracted graph (*PAGA graph*) of partitions, in which edge weights +represent confidence in the presence of connections. By tresholding this +confidence in `paga`, a much simpler representation of data +can be obtained. -It returns: +The confidence can be interpreted as the ratio of the actual versus the +expected value of connetions under the null model of randomly connecting +partitions. We do not provide a p-value as this null model does not +precisely capture what one would consider "connected" in real data, hence it +strongly overestimates the expected value. See an extensive discussion of +this in Wolf et al (2017). + +Together with a random walk-based distance measure, this generates a partial +coordinatization of data useful for exploring and explaining its variation. -- `Gene names`: Gene names ordered in column by group id and in rows according to scores -- `Scores`: Score for each gene (rows) for each group (columns), same order as for the names +The returned AnnData object contains: + +- Full adjacency matrix of the abstracted graph, weights correspond to confidence in the connectivities of partition (connectivities) +- Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree) -More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.rank_genes_groups.html#scanpy.api.tl.rank_genes_groups>`__ +These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects +More details on the `tl.paga scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_ ]]></help> <expand macro="citations"/> </tool>
--- a/macros.xml Mon Mar 04 10:13:44 2019 -0500 +++ b/macros.xml Wed Oct 16 06:29:43 2019 -0400 @@ -1,10 +1,12 @@ <macros> - <token name="@version@">1.4</token> + <token name="@version@">1.4.4</token> <token name="@galaxy_version@"><![CDATA[@version@+galaxy0]]></token> <xml name="requirements"> <requirements> <requirement type="package" version="@version@">scanpy</requirement> <requirement type="package" version="2.0.17">loompy</requirement> + <requirement type="package" version="2.9.0">h5py</requirement> + <requirement type="package" version="0.7.0">leidenalg</requirement> <yield /> </requirements> </xml> @@ -14,102 +16,33 @@ </citations> </xml> <xml name="version_command"> - <version_command><![CDATA[python -c "import scanpy.api as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> + <version_command><![CDATA[python -c "import scanpy as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> </xml> <token name="@CMD@"><![CDATA[ +cp '$adata' 'anndata.h5ad' && cat '$script_file' && -python '$script_file' +python '$script_file' && +ls . ]]> </token> <token name="@CMD_imports@"><![CDATA[ -import scanpy.api as sc +import scanpy as sc import pandas as pd import numpy as np ]]> </token> <xml name="inputs_anndata"> - <conditional name="input"> - <param name="format" type="select" label="Format for the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - <when value="loom"> - <param name="adata" type="data" format="loom" label="Annotated data matrix"/> - <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/> - <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/> - <param name="x_name" type="text" value="spliced" label="X_name"/> - <param name="obs_names" type="text" value="CellID" label="obs_names"/> - <param name="var_names" type="text" value="Gene" label="var_names"/> - </when> - <when value="h5ad"> - <param name="adata" type="data" format="h5" label="Annotated data matrix"/> - </when> - </conditional> + <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/> </xml> <token name="@CMD_read_inputs@"><![CDATA[ -#if $input.format == 'loom' -adata = sc.read_loom( - '$input.adata', - sparse=$input.sparse, - cleanup=$input.cleanup, - X_name='$input.x_name', - obs_names='$input.obs_names', - var_names='$input.var_names') -#else if $input.format == 'h5ad' -adata = sc.read_h5ad('$input.adata') -#end if +adata = sc.read('anndata.h5ad') ]]> </token> - <xml name="anndata_output_format"> - <param name="anndata_output_format" type="select" label="Format to write the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - </xml> - <xml name="anndata_modify_output_input"> - <conditional name="modify_anndata"> - <param name="modify_anndata" type="select" label="Return modify annotate data matrix?"> - <option value="true">Yes</option> - <option value="false">No</option> - </param> - <when value="true"> - <expand macro="anndata_output_format"/> - </when> - <when value="false"/> - </conditional> - </xml> <xml name="anndata_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'loom'</filter> - </data> - </xml> - <xml name="anndata_modify_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom'</filter> - </data> + <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/> </xml> <token name="@CMD_anndata_write_outputs@"><![CDATA[ -#if $anndata_output_format == 'loom' -adata.write_loom('anndata.loom') -#else if $anndata_output_format == 'h5ad' adata.write('anndata.h5ad') -#end if -]]> - </token> - <token name="@CMD_anndata_write_modify_outputs@"><![CDATA[ -#if $modify_anndata.modify_anndata == 'true' - #if $modify_anndata.anndata_output_format == 'loom' -adata.write_loom('anndata.loom') - #elif $modify_anndata.anndata_output_format == 'h5ad' -adata.write('anndata.h5ad') - #end if -#end if ]]> </token> <xml name="svd_solver"> @@ -423,7 +356,7 @@ <param argument="use_raw" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use `raw` attribute of input if present" help=""/> </xml> <xml name="param_log"> - <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?" help=""/> + <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?"/> </xml> <xml name="pl_figsize"> <conditional name="figsize"> @@ -473,7 +406,7 @@ <param argument="layer" type="text" value="" label="Name of the AnnData object layer that wants to be plotted" help="By default `adata.raw.X` is plotted. If `use_raw=False` is set, then `adata.X` is plotted. If layer is set to a valid layer name, then the layer is plotted. layer takes precedence over `use_raw`."/> </xml> <token name="@CMD_param_plot_inputs@"><![CDATA[ - adata=adata, + adata, save='.$format', show=False, ]]></token> @@ -512,9 +445,6 @@ #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, @@ -729,44 +659,42 @@ linewidths=$method.matplotlib_pyplot_scatter.linewidths, edgecolors='$method.matplotlib_pyplot_scatter.edgecolors' ]]></token> - <xml name="section_violin_plots"> - <section name="violin_plot" title="Violin plot attributes"> - <conditional name="stripplot"> - <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <conditional name="jitter"> - <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> - </when> - <when value="False"/> - </conditional> - </when> - <when value="False"/> - </conditional> - <conditional name="multi_panel"> - <param argument="multi_panel" type="select" label="Display keys in multiple panels" help="Also when `groupby is not provided"> - <option value="True">Yes</option> - <option value="False" selected="true">No</option> - </param> - <when value="True"> - <param argument="width" type="integer" min="0" value="" optional="true" label="Width of the figure" help=""/> - <param argument="height" type="integer" min="0" value="" optional="true" label="Height of the figure" help=""/> - </when> - <when value="False"/> - </conditional> - <param argument="scale" type="select" label="Method used to scale the width of each violin"> - <option value="area">area: each violin will have the same area</option> - <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> - <option value="width" selected="true">width: each violin will have the same width</option> + <xml name="conditional_stripplot"> + <conditional name="stripplot"> + <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> </param> - </section> + <when value="True"> + <conditional name="jitter"> + <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> + </param> + <when value="True"> + <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> + </when> + <when value="False"/> + </conditional> + </when> + <when value="False"/> + </conditional> + </xml> + <token name="@CMD_conditional_stripplot@"><![CDATA[ + stripplot=$method.violin_plot.stripplot.stripplot, +#if $method.violin_plot.stripplot.stripplot == "True" + jitter=$method.violin_plot.stripplot.jitter.jitter, + #if $method.violin_plot.stripplot.jitter.jitter == "True" + size=$method.violin_plot.stripplot.jitter.size, + #end if +#end if + ]]></token> + <xml name="param_scale"> + <param argument="scale" type="select" label="Method used to scale the width of each violin"> + <option value="area">area: each violin will have the same area</option> + <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> + <option value="width" selected="true">width: each violin will have the same width</option> + </param> </xml> <token name="@CMD_params_violin_plots@"><![CDATA[ stripplot=$method.violin_plot.stripplot.stripplot, @@ -777,7 +705,7 @@ #end if #end if multi_panel=$method.violin_plot.multi_panel.multi_panel, -#if $method.multi_panel.violin_plot.multi_panel == "True" and $method.violin_plot.multi_panel.width and $method.violin_plot.multi_panel.height +#if $method.multi_panel.violin_plot.multi_panel == "True" and str($method.violin_plot.multi_panel.width) != '' and str($method.violin_plot.multi_panel.height) != '' figsize=($method.violin_plot.multi_panel.width, $method.violin_plot.multi_panel.height) #end if scale='$method.violin_plot.scale', @@ -813,14 +741,12 @@ saturation=$method.seaborn_violinplot.saturation, ]]></token> <xml name="param_color"> - <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes`" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> + <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> </xml> <token name="@CMD_param_color@"><![CDATA[ #if str($method.color) != '' #set $color = ([x.strip() for x in str($method.color).split(',')]) color=$color, -#else - color=None, #end if ]]></token> <xml name="pl_groups"> @@ -830,8 +756,6 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if ]]></token> <xml name="pl_components"> @@ -847,8 +771,6 @@ #silent $components.append(str($s.axis1) + ',' + str($s.axis2)) #end for components=$components, -#else - components=None, #end if ]]> </token> @@ -877,7 +799,7 @@ </param> </xml> <xml name="param_legend_fontsize"> - <param argument="legend_fontsize" type="integer" min="0" value="1" label="Legend font size" help=""/> + <param argument="legend_fontsize" type="integer" optional="true" value="" label="Legend font size" help=""/> </xml> <xml name="param_legend_fontweight"> <param argument="legend_fontweight" type="select" label="Legend font weight" help=""> @@ -910,7 +832,7 @@ <param argument="left_margin" type="float" value="1" label="Width of the space left of each plotting panel" help=""/> </xml> <xml name="param_size"> - <param argument="size" type="float" value="1" label="Point size" help=""/> + <param argument="size" type="float" optional="true" value="" label="Point size" help=""/> </xml> <xml name="param_title"> <param argument="title" type="text" value="" optional="true" label="Title for panels" help="Titles must be separated by a comma"/> @@ -937,8 +859,8 @@ <option value="False" selected="true">No</option> </param> <when value="True"> - <param name="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> - <param name="edges_color" type="select" label="Color of edges"> + <param argument="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> + <param argument="edges_color" type="select" label="Color of edges"> <expand macro="matplotlib_color"/> </param> </when> @@ -956,7 +878,7 @@ ]]> </token> <xml name="param_arrows"> - <param name="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> + <param argument="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> </xml> <xml name="param_cmap"> <param argument="cmap" type="select" label="Colors to use for plotting categorical annotation groups" help=""> @@ -982,9 +904,13 @@ <token name="@CMD_pl_attribute_section@"><![CDATA[ projection='$method.plot.projection', legend_loc='$method.plot.legend_loc', + #if str($method.plot.legend_fontsize) != '' legend_fontsize=$method.plot.legend_fontsize, + #end if legend_fontweight='$method.plot.legend_fontweight', + #if str($method.plot.size) != '' size=$method.plot.size, + #end if palette='$method.plot.palette', frameon=$method.plot.frameon, ncols=$method.plot.ncols, @@ -995,24 +921,39 @@ #end if ]]> </token> + <xml name="options_layout"> + <option value="fa">fa: ForceAtlas2</option> + <option value="fr">fr: Fruchterman-Reingold</option> + <option value="grid_fr">grid_fr: Grid Fruchterman Reingold, faster than "fr"</option> + <option value="kk">kk: Kamadi Kawai’, slower than "fr"</option> + <option value="drl">drl: Distributed Recursive Layout, pretty fast</option> + <option value="rt">rt: Reingold Tilford tree layout</option> + <option value="eq_tree">eq_tree: Equally spaced tree</option> + </xml> + <xml name="param_layout"> + <param argument="layout" type="select" label="Plotting layout" help=""> + <expand macro="options_layout"/> + </param> + </xml> + <xml name="param_root"> + <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."/> + </xml> + <xml name="param_random_state"> + <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."/> + </xml> <xml name="inputs_paga"> <param argument="threshold" type="float" min="0" value="0.01" label="Threshold to draw edges" help="Do not draw edges for weights below this threshold. Set to 0 if you want all edges. Discarding low-connectivity edges helps in getting a much clearer picture of the graph."/> <expand macro="pl_groups"/> <param argument="color" type="text" value="" label="The node colors" help="Gene name or obs. annotation, and also plots the degree of the abstracted graph when passing 'degree_dashed', 'degree_solid'."/> <param argument="pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for drawing" help=""/> <param argument="labels" type="text" value="" label="Comma-separated node labels" help="If none is provided, this defaults to the group labels stored in the categorical for which `tl.paga` has been computed."/> - <param argument="layout" type="select" value="" label="Plotting layout" help=""> - <option value="fa">fa: ForceAtlas2</option> - <option value="fr">fr: Fruchterman-Reingold</option> - <option value="fr">rt: stands for Reingold Tilford</option> - <option value="fr">eq_tree: equally spaced tree</option> - </param> + <expand macro="param_layout"/> <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=""/> - <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."/> - <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."/> + <expand macro="param_random_state"/> + <expand macro="param_root"/> <param argument="transitions" type="text" value="" label="Key corresponding to the matrix storing the arrows" help="Key for `.uns['paga']`, e.g. 'transistions_confidence'"/> - <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']`"/> - <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."/> + <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"/> + <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."/> <param argument="single_component" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Restrict to largest connected component?" help=""/> <param argument="fontsize" type="integer" min="0" value="1" label="Font size for node labels" help=""/> <param argument="node_size_scale" type="float" min="0" value="1.0" label="Size of the nodes" help=""/> @@ -1031,10 +972,11 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if - color='$method.color', +#if str($method.color) != '' + #set $color=([x.strip() for x in str($method.color).split(',')]) + color=$color, +#end if #if $method.pos pos=np.fromfile($method.pos, dtype=dt), #end if @@ -1081,4 +1023,10 @@ <xml name="param_swap_axes"> <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`."/> </xml> + <xml name="gene_symbols"> + <param argument="gene_symbols" type="text" value="" optional="true" label="Key for field in `.var` that stores gene symbols"/> + </xml> + <xml name="n_genes"> + <param argument="n_genes" type="integer" min="0" value="20" label="Number of genes to show" help=""/> + </xml> </macros>
--- 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 @@ -1,641 +0,0 @@ - cell_subset number_per_cell -0 True 9 -1 True 9 -2 True 9 -3 True 8 -4 True 8 -5 True 8 -6 True 8 -7 True 7 -8 True 8 -9 True 8 -10 True 7 -11 True 7 -12 True 7 -13 True 7 -14 True 8 -15 True 10 -16 True 10 -17 True 10 -18 True 11 -19 True 11 -20 True 11 -21 True 11 -22 True 11 -23 True 11 -24 True 11 -25 True 11 -26 True 11 -27 True 11 -28 True 11 -29 True 11 -30 True 11 -31 True 11 -32 True 11 -33 True 11 -34 True 11 -35 True 11 -36 True 11 -37 True 11 -38 True 11 -39 True 11 -40 True 11 -41 True 11 -42 True 11 -43 True 11 -44 True 11 -45 True 11 -46 True 11 -47 True 11 -48 True 10 -49 True 10 -50 True 10 -51 True 10 -52 True 10 -53 True 10 -54 True 10 -55 True 10 -56 True 11 -57 True 11 -58 True 11 -59 True 10 -60 True 10 -61 True 11 -62 True 10 -63 True 11 -64 True 10 -65 True 10 -66 True 11 -67 True 11 -68 True 11 -69 True 10 -70 True 10 -71 True 10 -72 True 10 -73 True 10 -74 True 11 -75 True 10 -76 True 10 -77 True 10 -78 True 9 -79 True 10 -80 True 9 -81 True 9 -82 True 10 -83 True 9 -84 True 9 -85 True 9 -86 True 9 -87 True 9 -88 True 9 -89 True 9 -90 True 9 -91 True 10 -92 True 10 -93 True 9 -94 True 9 -95 True 9 -96 True 9 -97 True 7 -98 True 7 -99 True 6 -100 True 6 -101 True 7 -102 True 8 -103 True 8 -104 True 8 -105 True 8 -106 True 9 -107 True 9 -108 True 9 -109 True 8 -110 True 8 -111 True 10 -112 True 9 -113 True 8 -114 True 9 -115 True 10 -116 True 9 -117 True 8 -118 True 7 -119 True 7 -120 True 7 -121 True 7 -122 True 9 -123 True 9 -124 True 9 -125 True 8 -126 True 7 -127 True 6 -128 True 6 -129 True 8 -130 True 8 -131 True 8 -132 True 8 -133 True 10 -134 True 10 -135 True 8 -136 True 6 -137 True 6 -138 True 8 -139 True 9 -140 True 8 -141 True 7 -142 True 7 -143 True 8 -144 True 7 -145 True 7 -146 True 7 -147 True 5 -148 True 6 -149 True 8 -150 True 9 -151 True 6 -152 True 6 -153 True 6 -154 True 7 -155 True 8 -156 True 7 -157 True 7 -158 True 7 -159 True 8 -160 True 9 -161 True 8 -162 True 8 -163 True 9 -164 True 9 -165 True 9 -166 True 8 -167 True 8 -168 True 9 -169 True 9 -170 True 8 -171 True 9 -172 True 9 -173 True 10 -174 True 10 -175 True 10 -176 True 10 -177 True 10 -178 True 10 -179 True 10 -180 True 10 -181 True 10 -182 True 10 -183 True 10 -184 True 10 -185 True 10 -186 True 10 -187 True 10 -188 True 11 -189 True 11 -190 True 11 -191 True 11 -192 True 11 -193 True 11 -194 True 11 -195 True 11 -196 True 11 -197 True 11 -198 True 11 -199 True 11 -200 True 11 -201 True 11 -202 True 11 -203 True 11 -204 True 11 -205 True 11 -206 True 11 -207 True 11 -208 True 11 -209 True 11 -210 True 11 -211 True 11 -212 True 11 -213 True 11 -214 True 11 -215 True 11 -216 True 11 -217 True 11 -218 True 11 -219 True 11 -220 True 11 -221 True 11 -222 True 11 -223 True 11 -224 True 11 -225 True 11 -226 True 11 -227 True 11 -228 True 11 -229 True 11 -230 True 11 -231 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True 10 -466 True 10 -467 True 9 -468 True 10 -469 True 10 -470 True 11 -471 True 11 -472 True 9 -473 True 9 -474 True 9 -475 True 9 -476 True 9 -477 True 10 -478 True 10 -479 True 9 -480 True 8 -481 True 10 -482 True 10 -483 True 10 -484 True 8 -485 True 8 -486 True 9 -487 True 8 -488 True 9 -489 True 10 -490 True 11 -491 True 11 -492 True 11 -493 True 9 -494 True 10 -495 True 10 -496 True 10 -497 True 10 -498 True 11 -499 True 11 -500 True 11 -501 True 11 -502 True 11 -503 True 11 -504 True 11 -505 True 11 -506 True 11 -507 True 11 -508 True 11 -509 True 11 -510 True 11 -511 True 11 -512 True 11 -513 True 11 -514 True 11 -515 True 11 -516 True 11 -517 True 11 -518 True 11 -519 True 11 -520 True 11 -521 True 11 -522 True 11 -523 True 11 -524 True 11 -525 True 11 -526 True 11 -527 True 11 -528 True 11 -529 True 11 -530 True 11 -531 True 11 -532 True 11 -533 True 11 -534 True 11 -535 True 11 -536 True 10 -537 True 10 -538 True 10 -539 True 10 -540 True 10 -541 True 10 -542 True 11 -543 True 11 -544 True 11 -545 True 11 -546 True 11 -547 True 10 -548 True 9 -549 True 9 -550 True 10 -551 True 11 -552 True 10 -553 True 9 -554 True 9 -555 True 9 -556 True 8 -557 True 9 -558 True 7 -559 True 8 -560 True 8 -561 True 10 -562 True 9 -563 True 8 -564 True 8 -565 True 8 -566 True 8 -567 True 8 -568 True 6 -569 True 6 -570 True 6 -571 True 6 -572 True 8 -573 True 8 -574 True 7 -575 True 9 -576 True 7 -577 True 7 -578 True 8 -579 True 8 -580 True 6 -581 True 7 -582 True 7 -583 True 8 -584 True 6 -585 True 5 -586 True 5 -587 True 5 -588 True 6 -589 True 7 -590 True 6 -591 True 8 -592 True 7 -593 True 7 -594 True 8 -595 True 7 -596 True 7 -597 True 8 -598 True 5 -599 True 4 -600 True 5 -601 True 6 -602 True 5 -603 True 6 -604 True 7 -605 True 7 -606 True 9 -607 True 10 -608 True 8 -609 True 8 -610 True 10 -611 True 10 -612 True 9 -613 True 8 -614 True 8 -615 True 8 -616 True 7 -617 True 8 -618 True 7 -619 True 6 -620 True 6 -621 True 7 -622 True 7 -623 True 7 -624 True 8 -625 True 6 -626 True 7 -627 True 7 -628 True 7 -629 True 6 -630 True 5 -631 True 7 -632 True 6 -633 True 6 -634 True 7 -635 True 6 -636 True 8 -637 True 8 -638 True 6 -639 True 8
--- 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 @@ -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
--- 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 @@ -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
--- 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 @@ -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
--- 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 @@ -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
Binary file test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type -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 -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 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 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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 -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 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--- a/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,100 +0,0 @@ -1.006254479289054871e-01 7.390013337135314941e-02 5.162549763917922974e-02 -6.088243797421455383e-02 1.660361140966415405e-02 2.669865079224109650e-02 -1.013666391372680664e-01 5.378784239292144775e-02 2.008118629455566406e-01 1.484276503324508667e-01 1.083310469985008240e-01 1.318635195493698120e-01 1.997928470373153687e-01 -7.899370044469833374e-02 -2.425468564033508301e-01 -9.916571527719497681e-02 -6.192789599299430847e-02 3.743748366832733154e-02 5.766532197594642639e-02 2.186784986406564713e-03 -1.058542281389236450e-01 5.377947166562080383e-02 1.402157917618751526e-02 1.486204266548156738e-01 8.553525805473327637e-02 2.134956121444702148e-01 -9.449188411235809326e-02 -9.736447781324386597e-02 -6.411797553300857544e-02 -3.109178543090820312e-01 -1.063194051384925842e-01 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Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -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
Binary file test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:13:44 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type score -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 -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 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--- 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 @@ -1,641 +0,0 @@ -index cell_type S_score G2M_score phase -0 progenitor 0.2681 0.20055 S -1 progenitor 0.24346666 0.15855001 S -2 progenitor 0.2276 0.13482499 S -3 progenitor 0.21043333 0.12637499 S -4 progenitor 0.19113334 0.1272 S -5 progenitor 0.17531666 0.13072497 S -6 progenitor 0.16073334 0.13242501 S -7 progenitor 0.15353334 0.13672501 S -8 progenitor 0.14314999 0.1399 S -9 progenitor 0.1337 0.14515 G2M -10 progenitor 0.12695001 0.15165001 G2M -11 progenitor 0.11726667 0.16077498 G2M -12 progenitor 0.11081667 0.16735 G2M -13 progenitor 0.104849994 0.17429999 G2M -14 progenitor 0.09816667 0.18152499 G2M -15 progenitor 0.095350005 0.186625 G2M -16 progenitor 0.09528333 0.19447501 G2M -17 progenitor 0.09463333 0.199675 G2M -18 progenitor 0.0947 0.205275 G2M -19 progenitor 0.0947 0.20802501 G2M -20 progenitor 0.097733326 0.21100001 G2M -21 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