Mercurial > repos > iuc > scanpy_inspect
changeset 1:a755eaa1cc32 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 10:15:38 2019 -0500 +++ b/README.md Wed Oct 16 06:31:52 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 10:15:38 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/inspect.xml Mon Mar 04 10:15:38 2019 -0500 +++ b/inspect.xml Wed Oct 16 06:31:52 2019 -0400 @@ -1,7 +1,52 @@ -<tool id="scanpy_inspect" name="Inspect with scanpy" version="@galaxy_version@"> - <description></description> +<tool id="scanpy_inspect" name="Inspect and manipulate" version="@galaxy_version@"> + <description> with scanpy</description> <macros> <import>macros.xml</import> + <xml name="score_genes_params"> + <param argument="n_bins" type="integer" value="25" label="Number of expression level bins for sampling" help=""/> + <param argument="random_state" type="integer" value="0" label="Random seed for sampling" help=""/> + <expand macro="param_use_raw"/> + </xml> + <token name="@CMD_score_genes_inputs@"><![CDATA[ + n_bins=$method.n_bins, + random_state=$method.random_state, + use_raw=$method.use_raw, + copy=False + ]]></token> + <xml name="corr_method"> + <param argument="corr_method" type="select" label="P-value correction method"> + <option value="benjamini-hochberg">Benjamini-Hochberg</option> + <option value="bonferroni">Bonferroni</option> + </param> + </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="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> + <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="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> </macros> <expand macro="requirements"/> <expand macro="version_command"/> @@ -13,22 +58,195 @@ @CMD_imports@ @CMD_read_inputs@ -#if $method.method == "tl.paga" -sc.tl.paga( +#if $method.method == "pp.calculate_qc_metrics" +sc.pp.calculate_qc_metrics( + adata=adata, + expr_type='$method.expr_type', + var_type='$method.var_type', + #if str($method.qc_vars) != '' + #set $qc_vars = [str(x.strip()) for x in str($method.qc_vars).split(',')] + qc_vars=$qc_vars, + #end if + #if str($method.percent_top) != '' + #set $percent_top = [int(x.strip()) for x in str($method.percent_top).split(',')] + percent_top=$method.percent_top, + #end if + inplace=True) + +#else if $method.method == "tl.score_genes" +sc.tl.score_genes( adata=adata, - groups='$method.groups', - use_rna_velocity =$method.use_rna_velocity, - model='$method.model', + #set $gene_list = [str(x.strip()) for x in str($method.gene_list).split(',')] + gene_list=$gene_list, + ctrl_size=$method.ctrl_size, + score_name='$method.score_name', + #if $method.gene_pool + #set $gene_pool = [str(x.strip()) for x in $method.gene_pool.split(',')] + gene_pool=$gene_pool, + #end if + @CMD_score_genes_inputs@) + +#else if $method.method == "tl.score_genes_cell_cycle" + #if str($method.s_genes.format) == 'file' +with open('$method.s_genes.file', 'r') as s_genes_f: + s_genes = [str(x.strip()) for x in s_genes_f.readlines()] +print(s_genes) + #end if + + #if str($method.g2m_genes.format) == 'file' +with open('$method.g2m_genes.file', 'r') as g2m_genes_f: + g2m_genes = [str(x.strip()) for x in g2m_genes_f.readlines()] +print(g2m_genes) + #end if + +sc.tl.score_genes_cell_cycle( + adata=adata, + #if str($method.s_genes.format) == 'text' + #set $s_genes = [str(x.strip()) for x in $method.s_genes.text.split(',')] + s_genes=$s_genes, + #else if str($method.s_genes.format) == 'file' + s_genes=s_genes, + #end if + #if str($method.g2m_genes.format) == 'text' + #set $g2m_genes = [str(x.strip()) for x in $method.g2m_genes.text.split(',')] + g2m_genes=$g2m_genes, + #else if str($method.g2m_genes.format) == 'file' + g2m_genes=g2m_genes, + #end if + @CMD_score_genes_inputs@) + +#else if $method.method == 'pp.neighbors' +sc.pp.neighbors( + adata=adata, + n_neighbors=$method.n_neighbors, + #if str($method.n_pcs) != '' + n_pcs=$method.n_pcs, + #end if + #if str($method.use_rep) != '' + use_rep='$method.use_rep', + #end if + knn=$method.knn, + random_state=$method.random_state, + method='$method.pp_neighbors_method', + metric='$method.metric', copy=False) -#elif $method.method == "tl.dpt" -sc.tl.dpt( + +#else if $method.method == 'tl.rank_genes_groups' +sc.tl.rank_genes_groups( adata=adata, - n_dcs=$method.n_dcs, - n_branchings=$method.n_branchings, - min_group_size=$method.min_group_size, - allow_kendall_tau_shift=$method.allow_kendall_tau_shift, + groupby='$method.groupby', + use_raw=$method.use_raw, + #if str($method.groups) != '' + #set $group=[x.strip() for x in str($method.groups).split(',')] + groups=$group, + #end if + #if $method.ref.rest == 'rest' + reference='$method.ref.rest', + #else + reference='$method.ref.reference', + #end if + n_genes=$method.n_genes, + method='$method.tl_rank_genes_groups_method.method', + #if $method.tl_rank_genes_groups_method.method != 'logreg' + corr_method='$method.tl_rank_genes_groups_method.corr_method', + #else + 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, + #end if + only_positive=$method.only_positive) + +#else if $method.method == "tl.marker_gene_overlap" +reference_markers = {} +#for $i, $s in enumerate($method.reference_markers) + #set $list=[x.strip() for x in str($s.values).split(',')] +reference_markers['$s.key'] = $list +#end for + +sc.tl.marker_gene_overlap( + adata, + reference_markers, + #if str($method.key) != '' + key='$method.key', + #end if + method='$method.overlap.method', + #if $method.overlap.method == 'overlap_count' and str($method.overlap.normalize) != 'None' + normalize='$method.overlap.normalize', + #end if + #if str($method.top_n_markers) != '' + top_n_markers=$method.top_n_markers, + #end if + #if str($method.adj_pval_threshold) != '' + adj_pval_threshold=$method.adj_pval_threshold, + #end if + #if str($method.key_added) != '' + key_added='$method.key_added', + #end if + inplace=True) + +#else if $method.method == "pp.log1p" +sc.pp.log1p( + data=adata, copy=False) -adata.obs.to_csv('$obs', sep='\t') + +#else if $method.method == "pp.scale" +sc.pp.scale( + data=adata, + zero_center=$method.zero_center, + #if $method.max_value + max_value=$method.max_value, + #end if + copy=False) + +#else if $method.method == "pp.sqrt" +sc.pp.sqrt( + data=adata, + copy=False) #end if @CMD_anndata_write_outputs@ @@ -37,143 +255,647 @@ <inputs> <expand macro="inputs_anndata"/> <conditional name="method"> - <param argument="method" type="select" label="Method used for plotting"> - <!--<option value="tl.paga_compare_paths">, using `tl.paga_compare_paths`</option>!--> - <!--<option value="tl.paga_degrees">, using `tl.paga_degrees`</option>!--> - <!--<option value="tl.paga_expression_entropies">, using `tl.paga_expression_entropies`</option>!--> - <option value="tl.paga">Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`</option> - <option value="tl.dpt">Infer progression of cells through geodesic distance along the graph, using `tl.dpt`</option> + <param argument="method" type="select" label="Method used for inspecting"> + <option value="pp.calculate_qc_metrics">Calculate quality control metrics, using `pp.calculate_qc_metrics`</option> + <option value="pp.neighbors">Compute a neighborhood graph of observations, using `pp.neighbors`</option> + <option value="tl.score_genes">Score a set of genes, using `tl.score_genes`</option> + <option value="tl.score_genes_cell_cycle">Score cell cycle genes, using `tl.score_genes_cell_cycle`</option> + <option value="tl.rank_genes_groups">Rank genes for characterizing groups, using `tl.rank_genes_groups`</option> + <!--<option value="tl.marker_gene_overlap">Calculate an overlap score between data-deriven marker genes and provided markers, using `tl.marker_gene_overlap`</option>--> + <option value="pp.log1p">Logarithmize the data matrix, using `pp.log1p`</option> + <option value="pp.scale">Scale data to unit variance and zero mean, using `pp.scale`</option> + <option value="pp.sqrt">Square root the data matrix, using `pp.sqrt`</option> </param> - <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> + <when value="pp.calculate_qc_metrics"> + <param argument="expr_type" type="text" value="counts" label="Name of kind of values in X"/> + <param argument="var_type" type="text" value="genes" label="The kind of thing the variables are"/> + <param argument="qc_vars" type="text" value="" label="Keys for boolean columns of `.var` which identify variables you could want to control for" + help="Keys separated by a comma"/> + <param argument="percent_top" type="text" value="" label="Proportions of top genes to cover" + help=" Values (integers) are considered 1-indexed, `50` finds cumulative proportion to the 50th most expressed genes. Values separated by a comma. + If empty don't calculate"/> + </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="use_rep" type="text" value="" optional="true" label="Indicated representation to use" help="If not set, the representation is chosen automatically: for n_vars below 50, X is used, otherwise X_pca (uns) is used. If X_pca is not present, it's computed with default parameter"/> + <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"/> </param> </when> - <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 value="tl.score_genes"> + <param argument="gene_list" type="text" value="" label="The list of gene names used for score calculation" help="Genes separated by a comma"/> + <param argument="ctrl_size" type="integer" value="50" label="Number of reference genes to be sampled" + help="If `len(gene_list)` is not too low, you can set `ctrl_size=len(gene_list)`."/> + <param argument="gene_pool" type="text" value="" optional="true" label="Genes for sampling the reference set" + help="Default is all genes. Genes separated by a comma"/> + <expand macro="score_genes_params"/> + <param argument="score_name" type="text" value="score" label="Name of the field to be added in `.obs`" help=""/> + </when> + <when value="tl.score_genes_cell_cycle"> + <conditional name='s_genes'> + <param name="format" type="select" label="Format for the list of genes associated with S phase"> + <option value="file">File</option> + <option value="text" selected="true">Text</option> + </param> + <when value="text"> + <param name="text" type="text" value="" label="List of genes associated with S phase" help="Genes separated by a comma"/> + </when> + <when value="file"> + <param name="file" type="data" format="txt" label="File with the list of genes associated with S phase" help="One gene per line"/> + </when> + </conditional> + <conditional name='g2m_genes'> + <param name="format" type="select" label="Format for the list of genes associated with G2M phase"> + <option value="file">File</option> + <option value="text" selected="true">Text</option> + </param> + <when value="text"> + <param name="text" type="text" value="" label="List of genes associated with G2M phase" help="Genes separated by a comma"/> + </when> + <when value="file"> + <param name="file" type="data" format="txt" label="File with the list of genes associated with G2M phase" help="One gene per line"/> + </when> + </conditional> + <expand macro="score_genes_params"/> </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"> + <expand macro="corr_method"/> + </when> + <when value="wilcoxon"> + <expand macro="corr_method"/> + </when> + <when value="t-test_overestim_var"> + <expand macro="corr_method"/> + </when> + <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> + <!--<when value="tl.marker_gene_overlap"> + <repeat name="reference_markers" title="Marker genes"> + <param name="key" type="text" value="" label="Cell identity name" help=""/> + <param name="values" type="text" value="" label="List of genes" help="Comma-separated names from `var`"/> + </repeat> + <param argument="key" type="text" value="rank_genes_groups" label="Key in adata.uns where the rank_genes_groups output is stored"/> + <conditional name="overlap"> + <param argument="method" type="select" label="Method to calculate marker gene overlap"> + <option value="overlap_count">overlap_count: Intersection of the gene set</option> + <option value="overlap_coef">overlap_coef: Overlap coefficient</option> + <option value="jaccard">jaccard: Jaccard index</option> + </param> + <when value="overlap_count"> + <param argument="normalize" type="select" label="Normalization option for the marker gene overlap output"> + <option value="None">None</option> + <option value="reference">reference: Normalization of the data by the total number of marker genes given in the reference annotation per group</option> + <option value="data">data: Normalization of the data by the total number of marker genes used for each cluster</option> + </param> + </when> + <when value="overlap_coef"/> + <when value="jaccard"/> + </conditional> + <param argument="top_n_markers" type="integer" optional="true" label="Number of top data-derived marker genes to use" help="By default all calculated marker genes are used. If adj_pval_threshold is set along with top_n_markers, then adj_pval_threshold is ignored."/> + <param argument="adj_pval_threshold" type="float" optional="true" label="Significance threshold on the adjusted p-values to select marker genes" help=" This can only be used when adjusted p-values are calculated by 'tl.rank_genes_groups'. If adj_pval_threshold is set along with top_n_markers, then adj_pval_threshold is ignored."/> + <param argument="key_added" type="text" value="" optional="true" label="Key that will contain the marker overlap scores in 'uns'"/> + </when>--> + <when value="pp.log1p"/> + <when value="pp.scale"> + <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" + label="Zero center?" help="If not, it omits zero-centering variables, which allows to handle sparse input efficiently."/> + <param argument="max_value" type="float" value="" optional="true" label="Maximum value" + help="Clip (truncate) to this value after scaling. If not set, it does not clip."/> + </when> + <when value="pp.sqrt"/> </conditional> - <expand macro="anndata_output_format"/> </inputs> <outputs> <expand macro="anndata_outputs"/> - <data name="obs" format="tabular" label="${tool.name} on ${on_string}: Observations annotation"> - <filter>method['method'] == 'tl.dpt'</filter> - </data> </outputs> <tests> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> - </conditional> + <!-- test 1 --> + <param name="adata" value="sparce_csr_matrix.h5ad" /> <conditional name="method"> - <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"/> + <param name="method" value="pp.calculate_qc_metrics"/> + <param name="expr_type" value="counts"/> + <param name="var_type" value="genes"/> + <param name="qc_vars" value="mito,negative"/> + <param name="percent_top" value=""/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> - <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'"/> + <has_text_matching expression="sc.pp.calculate_qc_metrics" /> + <has_text_matching expression="expr_type='counts'" /> + <has_text_matching expression="var_type='genes'" /> + <has_text_matching expression="qc_vars=\['mito', 'negative'\]" /> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"> + <output name="anndata_out" file="pp.calculate_qc_metrics.sparce_csr_matrix.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 2 --> + <param name="adata" value="pp.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="random_state" value="0"/> + <param name="pp_neighbors_method" value="umap"/> + <param name="metric" value="euclidean"/> + </conditional> + <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" file="pp.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> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" /> + <!-- test 3 --> + <param name="adata" value="pp.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"/> </conditional> + <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" file="pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 4 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> - <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"/> + <param name="method" value="tl.score_genes"/> + <param name="gene_list" value="Gata2, Fog1"/> + <param name="ctrl_size" value="2"/> + <param name="n_bins" value="2"/> + <param name="random_state" value="2"/> + <param name="use_raw" value="False"/> + <param name="score_name" value="score"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.tl.score_genes" /> + <has_text_matching expression="gene_list=\['Gata2', 'Fog1'\]" /> + <has_text_matching expression="ctrl_size=2" /> + <has_text_matching expression="score_name='score'" /> + <has_text_matching expression="n_bins=2" /> + <has_text_matching expression="random_state=2" /> + <has_text_matching expression="use_raw=False" /> + <has_text_matching expression="copy=False" /> + </assert_stdout> + <output name="anndata_out" file="tl.score_genes.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 5 --> + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="tl.score_genes_cell_cycle"/> + <conditional name='s_genes'> + <param name="format" value="text"/> + <param name="text" value="Gata2, Fog1, EgrNab"/> + </conditional> + <conditional name='g2m_genes'> + <param name="format" value="text"/> + <param name="text" value="Gata2, Fog1, EgrNab"/> + </conditional> + <param name="n_bins" value="2"/> + <param name="random_state" value="1"/> + <param name="use_raw" value="False"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> + <assert_stdout> + <has_text_matching expression="sc.tl.score_genes_cell_cycle"/> + <has_text_matching expression="s_genes=\['Gata2', 'Fog1', 'EgrNab'\]"/> + <has_text_matching expression="g2m_genes=\['Gata2', 'Fog1', 'EgrNab'\]"/> + <has_text_matching expression="n_bins=2"/> + <has_text_matching expression="random_state=1"/> + <has_text_matching expression="use_raw=False"/> + </assert_stdout> + <output name="anndata_out" file="tl.score_genes_cell_cycle.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 6 --> + <param name="adata" value="krumsiek11.h5ad" /> + <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"/> + <param name="corr_method" value="benjamini-hochberg"/> + </conditional> + <param name="only_positive" value="true"/> + </conditional> <assert_stdout> - <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"/> + <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="corr_method='benjamini-hochberg'"/> + <has_text_matching expression="only_positive=True"/> </assert_stdout> - <output name="anndata_out_h5ad" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"> + <output name="anndata_out" file="tl.rank_genes_groups.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 7 --> + <param name="adata" value="pbmc68k_reduced.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"/> + </conditional> + <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"/> + </assert_stdout> + <output name="anndata_out" file="tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad" ftype="h5ad" compare="sim_size"> <assert_contents> - <has_h5_keys keys="X, obs, obsm, uns, var" /> + <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" /> </assert_contents> </output> - <output name="obs" file="tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular" compare="sim_size"/> + </test> + <test> + <!-- test 8 --> + <param name="adata" value="pbmc68k_reduced.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"/> + </conditional> + <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"/> + </assert_stdout> + <output name="anndata_out" file="tl.rank_genes_groups.liblinear.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"> + <assert_contents> + <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" /> + </assert_contents> + </output> + </test> + <!--<test> + < test 9 > + <param name="adata" value="tl.rank_genes_groups.louvain.neighbors.pca.pbmc68k_reduced.h5ad" /> + <conditional name="method"> + <param name="method" value="tl.marker_gene_overlap"/> + <repeat name="reference_markers"> + <param name="key" value="CD4 T cells"/> + <param name="value" value="IL7R"/> + </repeat> + <repeat name="reference_markers"> + <param name="key" value="CD14+ Monocytes"/> + <param name="value" value="CD14,LYZ"/> + </repeat> + <repeat name="reference_markers"> + <param name="key" value="B cells"/> + <param name="value" value="MS4A1"/> + </repeat> + <conditional name="overlap"> + <param argument="method" value="overlap_count"/> + <param argument="normalize" value="None"/> + </conditional> + </conditional> + <assert_stdout> + <has_text_matching expression="tl.marker_gene_overlap"/> + <has_text_matching expression="key='rank_genes_groups'"/> + <has_text_matching expression="method='overlap_count'"/> + </assert_stdout> + <output name="anndata_out" file="pp.log1p.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test>--> + <test> + <!-- test 9 --> + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="pp.log1p"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.pp.log1p"/> + </assert_stdout> + <output name="anndata_out" file="pp.log1p.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 10 --> + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="pp.scale"/> + <param name="zero_center" value="true"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.pp.scale"/> + <has_text_matching expression="zero_center=True"/> + </assert_stdout> + <output name="anndata_out" file="pp.scale.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 11 --> + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="pp.scale"/> + <param name="zero_center" value="true"/> + <param name="max_value" value="10"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.pp.scale"/> + <has_text_matching expression="zero_center=True"/> + <has_text_matching expression="max_value=10.0"/> + </assert_stdout> + <output name="anndata_out" file="pp.scale_max_value.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> + </test> + <test> + <!-- test 12 --> + <param name="adata" value="krumsiek11.h5ad" /> + <conditional name="method"> + <param name="method" value="pp.sqrt"/> + </conditional> + <assert_stdout> + <has_text_matching expression="sc.pp.sqrt"/> + </assert_stdout> + <output name="anndata_out" file="pp.sqrt.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> </tests> <help><![CDATA[ -Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`) -======================================================================================= +Calculate quality control metrics., using `pp.calculate_qc_metrics` +=================================================================== + +Calculates a number of qc metrics for an AnnData object, largely based on calculateQCMetrics from scater. +Currently is most efficient on a sparse CSR or dense matrix. + +It updates the observation level metrics: + +- total_{var_type}_by_{expr_type} (e.g. "total_genes_by_counts", number of genes with positive counts in a cell) +- total_{expr_type} (e.g. "total_counts", total number of counts for a cell) +- pct_{expr_type}_in_top_{n}_{var_type} (e.g. "pct_counts_in_top_50_genes", cumulative percentage of counts for 50 most expressed genes in a cell) +- total_{expr_type}_{qc_var} (e.g. "total_counts_mito", total number of counts for variabes in qc_vars ) +- pct_{expr_type}_{qc_var} (e.g. "pct_counts_mito", proportion of total counts for a cell which are mitochondrial) + +And also the variable level metrics: -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. +- total_{expr_type} (e.g. "total_counts", sum of counts for a gene) +- mean_{expr_type} (e.g. "mean counts", mean expression over all cells. +- n_cells_by_{expr_type} (e.g. "n_cells_by_counts", number of cells this expression is measured in) +- pct_dropout_by_{expr_type} (e.g. "pct_dropout_by_counts", percentage of cells this feature does not appear in) + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.calculate_qc_metrics.html>`__ + +Compute a neighborhood graph of observations, using `pp.neighbors` +================================================================== + +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). + +The returned AnnData object contains: + +- Weighted adjacency matrix of the neighborhood graph of data points (connectivities). Weights should be interpreted as connectivities. +- Distances for each pair of neighbors (distances) + +This data are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects -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). +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.neighbors.html>`__ + +Score a set of genes, using `tl.score_genes` +============================================ + +The score is the average expression of a set of genes subtracted with the +average expression of a reference set of genes. The reference set is +randomly sampled from the `gene_pool` for each binned expression value. + +This reproduces the approach in Seurat (Satija et al, 2015) and has been implemented +for Scanpy by Davide Cittaro. + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.score_genes.html>`__ + +Score cell cycle genes, using `tl.score_genes_cell_cycle` +========================================================= -Together with a random walk-based distance measure, this generates a partial -coordinatization of data useful for exploring and explaining its variation. +Given two lists of genes associated to S phase and G2M phase, calculates +scores and assigns a cell cycle phase (G1, S or G2M). See +`score_genes` for more explanation. + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.score_genes_cell_cycle.html>`__ + +Rank genes for characterizing groups, using `tl.rank_genes_groups` +================================================================== -More details on the `tl.paga scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.paga.html#scanpy.api.tl.paga>`_ +The returned AnnData object contains: + +- Gene names, ordered according to scores +- Z-score underlying the computation of a p-value for each gene for each group, prdered according to scores +- Log2 fold change for each gene for each group, ordered according to scores. It is only provided if method is ‘t-test’ like. This is an approximation calculated from mean-log values. +- P-values +- Ajusted p-values + +This data are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects + +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.rank_genes_groups.html>`__ -Infer progression of cells through geodesic distance along the graph (`tl.dpt`) -=============================================================================== +Calculate an overlap score between data-deriven marker genes and provided markers (`tl.marker_gene_overlap`) +============================================================================================================ -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. - -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. +Marker gene overlap scores can be quoted as overlap counts, overlap coefficients, or jaccard indices. The method returns a pandas dataframe which can be used to annotate clusters based on marker gene overlaps. -If `n_branchings==0`, no field `dpt_groups` will be written. +Logarithmize the data matrix (`pp.log1p`) +========================================= -- 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. +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.log1p.html>`__ -The tool is similar to the R package `destiny` of Angerer et al (2016). +Scale data to unit variance and zero mean (`pp.scale`) +====================================================== -More details on the `tl.dpt scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.tl.dpt.html#scanpy.api.tl.dpt>`_ +More details on the `scanpy documentation +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.scale.html>`__ +Computes the square root the data matrix (`pp.sqrt`) +==================================================== + +`X = sqrt(X)` ]]></help> <expand macro="citations"/> </tool> \ No newline at end of file
--- a/macros.xml Mon Mar 04 10:15:38 2019 -0500 +++ b/macros.xml Wed Oct 16 06:31:52 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 10:15:38 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 True 11 -232 True 11 -233 True 11 -234 True 11 -235 True 11 -236 True 11 -237 True 11 -238 True 11 -239 True 11 -240 True 11 -241 True 11 -242 True 11 -243 True 11 -244 True 11 -245 True 11 -246 True 11 -247 True 11 -248 True 11 -249 True 11 -250 True 11 -251 True 11 -252 True 11 -253 True 11 -254 True 11 -255 True 11 -256 True 11 -257 True 11 -258 True 11 -259 True 11 -260 True 11 -261 True 11 -262 True 11 -263 True 11 -264 True 11 -265 True 11 -266 True 11 -267 True 11 -268 True 11 -269 True 11 -270 True 11 -271 True 11 -272 True 10 -273 True 11 -274 True 11 -275 True 11 -276 True 11 -277 True 11 -278 True 11 -279 True 10 -280 True 11 -281 True 9 -282 True 9 -283 True 8 -284 True 9 -285 True 9 -286 True 10 -287 True 9 -288 True 9 -289 True 9 -290 True 9 -291 True 9 -292 True 9 -293 True 10 -294 True 10 -295 True 10 -296 True 10 -297 True 9 -298 True 10 -299 True 9 -300 True 8 -301 True 8 -302 True 8 -303 True 8 -304 True 8 -305 True 9 -306 True 8 -307 True 8 -308 True 8 -309 True 8 -310 True 9 -311 True 8 -312 True 9 -313 True 9 -314 True 10 -315 True 10 -316 True 10 -317 True 10 -318 True 10 -319 True 10 -320 True 4 -321 True 8 -322 True 8 -323 True 8 -324 True 8 -325 True 7 -326 True 8 -327 True 8 -328 True 7 -329 True 9 -330 True 8 -331 True 9 -332 True 8 -333 True 8 -334 True 8 -335 True 10 -336 True 10 -337 True 9 -338 True 10 -339 True 10 -340 True 10 -341 True 10 -342 True 10 -343 True 10 -344 True 10 -345 True 11 -346 True 11 -347 True 11 -348 True 11 -349 True 11 -350 True 11 -351 True 11 -352 True 11 -353 True 11 -354 True 11 -355 True 11 -356 True 11 -357 True 11 -358 True 11 -359 True 11 -360 True 11 -361 True 11 -362 True 11 -363 True 11 -364 True 11 -365 True 11 -366 True 11 -367 True 11 -368 True 11 -369 True 11 -370 True 11 -371 True 11 -372 True 11 -373 True 11 -374 True 11 -375 True 11 -376 True 11 -377 True 11 -378 True 11 -379 True 11 -380 True 11 -381 True 11 -382 True 11 -383 True 11 -384 True 11 -385 True 11 -386 True 11 -387 True 11 -388 True 11 -389 True 11 -390 True 11 -391 True 11 -392 True 11 -393 True 11 -394 True 11 -395 True 11 -396 True 11 -397 True 11 -398 True 11 -399 True 11 -400 True 11 -401 True 11 -402 True 11 -403 True 11 -404 True 11 -405 True 11 -406 True 11 -407 True 11 -408 True 11 -409 True 11 -410 True 11 -411 True 11 -412 True 11 -413 True 11 -414 True 11 -415 True 11 -416 True 11 -417 True 11 -418 True 11 -419 True 11 -420 True 11 -421 True 11 -422 True 11 -423 True 11 -424 True 11 -425 True 11 -426 True 11 -427 True 11 -428 True 11 -429 True 11 -430 True 11 -431 True 11 -432 True 11 -433 True 11 -434 True 11 -435 True 11 -436 True 11 -437 True 11 -438 True 11 -439 True 11 -440 True 11 -441 True 11 -442 True 11 -443 True 11 -444 True 11 -445 True 11 -446 True 10 -447 True 10 -448 True 10 -449 True 10 -450 True 10 -451 True 11 -452 True 11 -453 True 11 -454 True 10 -455 True 10 -456 True 11 -457 True 10 -458 True 10 -459 True 11 -460 True 11 -461 True 10 -462 True 9 -463 True 10 -464 True 10 -465 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 10:15:38 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 10:15:38 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 10:15:38 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 10:15:38 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 10:15:38 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 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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 10:15:38 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 10:15:38 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 -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 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 Ery -81 Ery -82 Ery -83 Ery -84 Ery -85 Ery -86 Ery -87 Ery -88 Ery -89 Ery -90 Ery -91 Ery -92 Ery -93 Ery -94 Ery -95 Ery -96 Ery -97 Ery -98 Ery -99 Ery -100 Ery -101 Ery -102 Ery -103 Ery -104 Ery -105 Ery -106 Ery -107 Ery -108 Ery -109 Ery -110 Ery -111 Ery -112 Ery -113 Ery -114 Ery -115 Ery -116 Ery -117 Ery -118 Ery -119 Ery -120 Ery -121 Ery -122 Ery -123 Ery -124 Ery -125 Ery -126 Ery -127 Ery -128 Ery -129 Ery -130 Ery -131 Ery -132 Ery -133 Ery -134 Ery -135 Ery -136 Ery -137 Ery -138 Ery -139 Ery -140 Ery -141 Ery -142 Ery -143 Ery -144 Ery -145 Ery -146 Ery -147 Ery -148 Ery -149 Ery -150 Ery -151 Ery -152 Ery -153 Ery -154 Ery -155 Ery -156 Ery -157 Ery -158 Ery -159 Ery -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 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-75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mk -81 Mk -82 Mk -83 Mk -84 Mk -85 Mk -86 Mk -87 Mk -88 Mk -89 Mk -90 Mk -91 Mk -92 Mk -93 Mk -94 Mk -95 Mk -96 Mk -97 Mk -98 Mk -99 Mk -100 Mk -101 Mk -102 Mk -103 Mk -104 Mk -105 Mk -106 Mk -107 Mk -108 Mk -109 Mk -110 Mk -111 Mk -112 Mk -113 Mk -114 Mk -115 Mk -116 Mk -117 Mk -118 Mk -119 Mk -120 Mk -121 Mk -122 Mk -123 Mk -124 Mk -125 Mk -126 Mk -127 Mk -128 Mk -129 Mk -130 Mk -131 Mk -132 Mk -133 Mk -134 Mk -135 Mk -136 Mk -137 Mk -138 Mk -139 Mk -140 Mk -141 Mk -142 Mk -143 Mk -144 Mk -145 Mk -146 Mk -147 Mk -148 Mk -149 Mk -150 Mk -151 Mk -152 Mk -153 Mk -154 Mk -155 Mk -156 Mk -157 Mk -158 Mk -159 Mk -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -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 Neu -81 Neu -82 Neu -83 Neu -84 Neu -85 Neu -86 Neu -87 Neu -88 Neu -89 Neu -90 Neu -91 Neu -92 Neu -93 Neu -94 Neu -95 Neu -96 Neu -97 Neu -98 Neu -99 Neu -100 Neu -101 Neu -102 Neu -103 Neu -104 Neu -105 Neu -106 Neu -107 Neu -108 Neu -109 Neu -110 Neu -111 Neu -112 Neu -113 Neu -114 Neu -115 Neu -116 Neu -117 Neu -118 Neu -119 Neu -120 Neu -121 Neu -122 Neu -123 Neu -124 Neu -125 Neu -126 Neu -127 Neu -128 Neu -129 Neu -130 Neu -131 Neu -132 Neu -133 Neu -134 Neu -135 Neu -136 Neu -137 Neu -138 Neu -139 Neu -140 Neu -141 Neu -142 Neu -143 Neu -144 Neu -145 Neu -146 Neu -147 Neu -148 Neu -149 Neu -150 Neu -151 Neu -152 Neu -153 Neu -154 Neu -155 Neu -156 Neu -157 Neu -158 Neu -159 Neu
--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:15:38 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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