changeset 1:a755eaa1cc32 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
author iuc
date Wed, 16 Oct 2019 06:31:52 -0400
parents 5d2e17328afe
children 7d22964a8639
files README.md README.rst inspect.xml macros.xml test-data/blobs.h5ad test-data/pl.clustermap.krumsiek11.png test-data/pl.dotplot.krumsiek11.png test-data/pl.draw_graph.png test-data/pl.highly_variable_genes.seurat.blobs.png test-data/pl.pca_overview.pp.pca.krumsiek11.png test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_violin.Ery.png test-data/pl.rank_genes_groups_violin.Mk.png test-data/pl.rank_genes_groups_violin.Mo.png test-data/pl.rank_genes_groups_violin.Neu.png test-data/pl.rank_genes_groups_violin.progenitor.png test-data/pl.scatter.krumsiek11.png test-data/pl.scatter.umap.pbmc68k_reduced.png test-data/pl.stacked_violin.krumsiek11.png test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad test-data/pp.combat.blobs.h5ad test-data/pp.downsample_counts.random-randint.h5ad test-data/pp.filter_cells.krumsiek11-max_genes.h5ad test-data/pp.filter_cells.krumsiek11-min_counts.h5ad test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular test-data/pp.filter_genes.krumsiek11-min_counts.h5ad test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular test-data/pp.filter_rank_genes_groups.h5ad test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad test-data/pp.highly_variable_genes.seurat.blobs.h5ad test-data/pp.log1p.krumsiek11.h5ad test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/pp.normalize_per_cell.obs.krumsiek11.tabular test-data/pp.normalize_total.krumsiek11.h5ad test-data/pp.pca.krumsiek11.h5ad test-data/pp.recipe_seurat.recipe_zheng17.h5ad test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad test-data/pp.recipe_zheng17.random-randint.h5ad test-data/pp.regress_out.krumsiek11.h5ad test-data/pp.scale.krumsiek11.h5ad test-data/pp.scale_max_value.krumsiek11.h5ad test-data/pp.sqrt.krumsiek11.h5ad test-data/pp.subsample.krumsiek11_fraction.h5ad test-data/sparce_csr_matrix.h5ad test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.pca.krumsiek11.h5ad test-data/tl.rank_genes_groups.krumsiek11.h5ad test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad test-data/tl.score_genes.krumsiek11.h5ad test-data/tl.score_genes.krumsiek11.obs.tabular test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular test-data/tl.tsne.krumsiek11.h5ad test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
diffstat 67 files changed, 995 insertions(+), 3473 deletions(-) [+]
line wrap: on
line diff
--- 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` &gt; 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>
Binary file test-data/blobs.h5ad has changed
Binary file test-data/pl.clustermap.krumsiek11.png has changed
Binary file test-data/pl.dotplot.krumsiek11.png has changed
Binary file test-data/pl.draw_graph.png has changed
Binary file test-data/pl.highly_variable_genes.seurat.blobs.png has changed
Binary file test-data/pl.pca_overview.pp.pca.krumsiek11.png has changed
Binary file test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png has changed
Binary file test-data/pl.rank_genes_groups_violin.Ery.png has changed
Binary file test-data/pl.rank_genes_groups_violin.Mk.png has changed
Binary file test-data/pl.rank_genes_groups_violin.Mo.png has changed
Binary file test-data/pl.rank_genes_groups_violin.Neu.png has changed
Binary file test-data/pl.rank_genes_groups_violin.progenitor.png has changed
Binary file test-data/pl.scatter.krumsiek11.png has changed
Binary file test-data/pl.scatter.umap.pbmc68k_reduced.png has changed
Binary file test-data/pl.stacked_violin.krumsiek11.png has changed
Binary file test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad has changed
Binary file test-data/pp.combat.blobs.h5ad has changed
Binary file test-data/pp.downsample_counts.random-randint.h5ad has changed
Binary file test-data/pp.filter_cells.krumsiek11-max_genes.h5ad has changed
Binary file test-data/pp.filter_cells.krumsiek11-min_counts.h5ad has changed
--- 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
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Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed
--- 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
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-18	True	43
-19	True	199
-20	True	271
-21	True	318
-22	True	132
-23	True	83
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-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
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-36	True	103
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-40	True	139
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-93	True	76
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-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
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-206	True	219
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-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
Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed
--- 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.filter_rank_genes_groups.h5ad has changed
Binary file test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad has changed
Binary file test-data/pp.highly_variable_genes.seurat.blobs.h5ad has changed
Binary file test-data/pp.log1p.krumsiek11.h5ad has changed
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
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-0	progenitor
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-80	Ery
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-87	Ery
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-89	Ery
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-97	Ery
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-141	Ery
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-146	Ery
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-156	Ery
-157	Ery
-158	Ery
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-0	progenitor
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-80	Mk
-81	Mk
-82	Mk
-83	Mk
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-85	Mk
-86	Mk
-87	Mk
-88	Mk
-89	Mk
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-97	Mk
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-112	Mk
-113	Mk
-114	Mk
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-137	Mk
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-147	Mk
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-149	Mk
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-152	Mk
-153	Mk
-154	Mk
-155	Mk
-156	Mk
-157	Mk
-158	Mk
-159	Mk
-0	progenitor
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-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
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-103	Neu
-104	Neu
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-110	Neu
-111	Neu
-112	Neu
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-114	Neu
-115	Neu
-116	Neu
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-122	Neu
-123	Neu
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-128	Neu
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-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
Binary file test-data/pp.normalize_total.krumsiek11.h5ad has changed
Binary file test-data/pp.pca.krumsiek11.h5ad has changed
Binary file test-data/pp.recipe_seurat.recipe_zheng17.h5ad has changed
Binary file test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad has changed
Binary file test-data/pp.recipe_zheng17.random-randint.h5ad has changed
Binary file test-data/pp.regress_out.krumsiek11.h5ad has changed
Binary file test-data/pp.scale.krumsiek11.h5ad has changed
Binary file test-data/pp.scale_max_value.krumsiek11.h5ad has changed
Binary file test-data/pp.sqrt.krumsiek11.h5ad has changed
Binary file test-data/pp.subsample.krumsiek11_fraction.h5ad has changed
Binary file test-data/sparce_csr_matrix.h5ad has changed
--- a/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular	Mon Mar 04 10:15:38 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,100 +0,0 @@
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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
Binary file test-data/tl.pca.krumsiek11.h5ad has changed
Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed
Binary file test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad has changed
Binary file test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad has changed
Binary file test-data/tl.score_genes.krumsiek11.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
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-26	progenitor	
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-28	progenitor	
-29	progenitor	
-30	progenitor	
-31	progenitor	
-32	progenitor	
-33	progenitor	
-34	progenitor	
-35	progenitor	
-36	progenitor	
-37	progenitor	
-38	progenitor	
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-72	progenitor	
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-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	
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-105	Mo	
-106	Mo	
-107	Mo	
-108	Mo	
-109	Mo	
-110	Mo	
-111	Mo	
-112	Mo	
-113	Mo	
-114	Mo	
-115	Mo	
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-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	
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-48	progenitor	
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-51	progenitor	
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-53	progenitor	
-54	progenitor	
-55	progenitor	
-56	progenitor	
-57	progenitor	
-58	progenitor	
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-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	
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-82	Ery	
-83	Ery	
-84	Ery	
-85	Ery	
-86	Ery	
-87	Ery	
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-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	
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-107	Ery	
-108	Ery	
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-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	
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-135	Ery	
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-139	Ery	
-140	Ery	
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-142	Ery	
-143	Ery	
-144	Ery	
-145	Ery	
-146	Ery	
-147	Ery	
-148	Ery	
-149	Ery	
-150	Ery	
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-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	progenitor	
-9	progenitor	
-10	progenitor	
-11	progenitor	
-12	progenitor	
-13	progenitor	
-14	progenitor	
-15	progenitor	
-16	progenitor	
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-18	progenitor	
-19	progenitor	
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-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	
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-40	progenitor	
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-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	
Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed
--- 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	progenitor	0.09881667	0.21964999	G2M
-22	progenitor	0.10131666	0.22662501	G2M
-23	progenitor	0.104849994	0.23022501	G2M
-24	progenitor	0.112266675	0.23387499	G2M
-25	progenitor	0.120283335	0.2393	G2M
-26	progenitor	0.12826668	0.24174997	G2M
-27	progenitor	0.13323334	0.24710001	G2M
-28	progenitor	0.13971666	0.25280002	G2M
-29	progenitor	0.14393334	0.256775	G2M
-30	progenitor	0.15066667	0.259775	G2M
-31	progenitor	0.15316668	0.26244998	G2M
-32	progenitor	0.15993333	0.26487502	G2M
-33	progenitor	0.16430001	0.266275	G2M
-34	progenitor	0.16598332	0.270625	G2M
-35	progenitor	0.17068332	0.2715	G2M
-36	progenitor	0.17713334	0.276475	G2M
-37	progenitor	0.17893334	0.27514997	G2M
-38	progenitor	0.18013333	0.278025	G2M
-39	progenitor	0.18251666	0.279675	G2M
-40	progenitor	0.18876666	0.27925	G2M
-41	progenitor	0.19041668	0.281775	G2M
-42	progenitor	0.19083333	0.2824	G2M
-43	progenitor	0.19411668	0.281725	G2M
-44	progenitor	0.19639999	0.2844	G2M
-45	progenitor	0.19843334	0.285375	G2M
-46	progenitor	0.20406666	0.284075	G2M
-47	progenitor	0.20673332	0.28625	G2M
-48	progenitor	0.20769998	0.2885	G2M
-49	progenitor	0.21186668	0.28935	G2M
-50	progenitor	0.21285	0.28867498	G2M
-51	progenitor	0.21443334	0.28855002	G2M
-52	progenitor	0.21568334	0.28705	G2M
-53	progenitor	0.21788335	0.29035	G2M
-54	progenitor	0.22551665	0.28815	G2M
-55	progenitor	0.22586668	0.28689998	G2M
-56	progenitor	0.23069999	0.2816	G2M
-57	progenitor	0.23118332	0.282375	G2M
-58	progenitor	0.23160002	0.28230003	G2M
-59	progenitor	0.23546667	0.28329998	G2M
-60	progenitor	0.23661667	0.28195	G2M
-61	progenitor	0.24134998	0.27899998	G2M
-62	progenitor	0.24546666	0.27855	G2M
-63	progenitor	0.24836665	0.27609998	G2M
-64	progenitor	0.25375	0.27562502	G2M
-65	progenitor	0.25834998	0.273525	G2M
-66	progenitor	0.26393333	0.27015	G2M
-67	progenitor	0.26746666	0.26622498	S
-68	progenitor	0.2706333	0.267025	S
-69	progenitor	0.27618334	0.2651	S
-70	progenitor	0.28033334	0.263975	S
-71	progenitor	0.2868167	0.2622	S
-72	progenitor	0.29141667	0.26174998	S
-73	progenitor	0.29198334	0.26385	S
-74	progenitor	0.29348332	0.26275003	S
-75	progenitor	0.29788333	0.263575	S
-76	progenitor	0.30125	0.26232502	S
-77	progenitor	0.29955	0.261825	S
-78	progenitor	0.30065	0.2623	S
-79	progenitor	0.30573332	0.2588	S
-80	Mo	0.30818334	0.25555003	S
-81	Mo	0.31073332	0.25422502	S
-82	Mo	0.31378332	0.25410002	S
-83	Mo	0.31268334	0.25304997	S
-84	Mo	0.31355	0.25059998	S
-85	Mo	0.3157	0.251275	S
-86	Mo	0.3139333	0.25072497	S
-87	Mo	0.3151833	0.25165	S
-88	Mo	0.3149333	0.25079998	S
-89	Mo	0.31440002	0.25172502	S
-90	Mo	0.31251666	0.254725	S
-91	Mo	0.31613332	0.25347498	S
-92	Mo	0.31813332	0.25372502	S
-93	Mo	0.31543335	0.25340003	S
-94	Mo	0.31663334	0.257025	S
-95	Mo	0.31793332	0.25435	S
-96	Mo	0.3184333	0.2527	S
-97	Mo	0.31743336	0.25052497	S
-98	Mo	0.3164667	0.24747501	S
-99	Mo	0.31841668	0.2466	S
-100	Mo	0.31648335	0.24679999	S
-101	Mo	0.31504998	0.2501	S
-102	Mo	0.31489998	0.250375	S
-103	Mo	0.31256667	0.25195	S
-104	Mo	0.31425	0.250675	S
-105	Mo	0.31441668	0.248675	S
-106	Mo	0.31828332	0.24724999	S
-107	Mo	0.32236665	0.25105	S
-108	Mo	0.32341668	0.2527	S
-109	Mo	0.32334998	0.25145	S
-110	Mo	0.32061666	0.2516	S
-111	Mo	0.3239333	0.24855	S
-112	Mo	0.3217833	0.248275	S
-113	Mo	0.3194833	0.25055	S
-114	Mo	0.32711667	0.24814999	S
-115	Mo	0.32861665	0.244375	S
-116	Mo	0.33048332	0.244225	S
-117	Mo	0.33173332	0.24415	S
-118	Mo	0.32801664	0.24665	S
-119	Mo	0.3321833	0.245675	S
-120	Mo	0.32905	0.24717501	S
-121	Mo	0.33133334	0.245975	S
-122	Mo	0.33201668	0.24515	S
-123	Mo	0.33265	0.24475	S
-124	Mo	0.32968336	0.24344999	S
-125	Mo	0.32461664	0.245175	S
-126	Mo	0.32303333	0.24647498	S
-127	Mo	0.32363334	0.24472499	S
-128	Mo	0.3234	0.24480002	S
-129	Mo	0.32494998	0.24702501	S
-130	Mo	0.32526666	0.24975002	S
-131	Mo	0.32278332	0.24785002	S
-132	Mo	0.3201	0.24885	S
-133	Mo	0.32130003	0.25125	S
-134	Mo	0.32468334	0.2521	S
-135	Mo	0.32040003	0.25545	S
-136	Mo	0.31931666	0.25560004	S
-137	Mo	0.31963333	0.25262502	S
-138	Mo	0.31644997	0.253575	S
-139	Mo	0.31913334	0.251575	S
-140	Mo	0.32393336	0.24987501	S
-141	Mo	0.32683334	0.2504	S
-142	Mo	0.32791668	0.24740002	S
-143	Mo	0.329	0.247075	S
-144	Mo	0.32784998	0.24852501	S
-145	Mo	0.32766664	0.24740002	S
-146	Mo	0.32676667	0.2485	S
-147	Mo	0.3254	0.24985	S
-148	Mo	0.32301664	0.24805	S
-149	Mo	0.32369998	0.25047502	S
-150	Mo	0.3267	0.250475	S
-151	Mo	0.3280667	0.252875	S
-152	Mo	0.32885	0.25315002	S
-153	Mo	0.32688335	0.2515	S
-154	Mo	0.32666668	0.25325	S
-155	Mo	0.3258167	0.25137502	S
-156	Mo	0.32818332	0.2465	S
-157	Mo	0.32963336	0.24692501	S
-158	Mo	0.3318167	0.24837498	S
-159	Mo	0.33176666	0.247625	S
-0	progenitor	0.26751667	0.2007	S
-1	progenitor	0.24345	0.157325	S
-2	progenitor	0.22616667	0.139575	S
-3	progenitor	0.20503333	0.13499999	S
-4	progenitor	0.18988334	0.1349	S
-5	progenitor	0.17425	0.134875	S
-6	progenitor	0.16213334	0.13455	S
-7	progenitor	0.14678332	0.14125	S
-8	progenitor	0.1336	0.146375	G2M
-9	progenitor	0.1237	0.15342501	G2M
-10	progenitor	0.11621666	0.16017501	G2M
-11	progenitor	0.10858333	0.1669	G2M
-12	progenitor	0.09945001	0.17515	G2M
-13	progenitor	0.09445	0.182875	G2M
-14	progenitor	0.091649994	0.18862501	G2M
-15	progenitor	0.08881667	0.196275	G2M
-16	progenitor	0.08878334	0.20034999	G2M
-17	progenitor	0.09183334	0.208125	G2M
-18	progenitor	0.094516665	0.21419999	G2M
-19	progenitor	0.094349995	0.223	G2M
-20	progenitor	0.09643334	0.228775	G2M
-21	progenitor	0.09933333	0.232925	G2M
-22	progenitor	0.10111666	0.2406	G2M
-23	progenitor	0.10683333	0.24365	G2M
-24	progenitor	0.11198333	0.24744998	G2M
-25	progenitor	0.1187	0.24800001	G2M
-26	progenitor	0.12223333	0.253475	G2M
-27	progenitor	0.12516668	0.25777498	G2M
-28	progenitor	0.13296667	0.261875	G2M
-29	progenitor	0.13638332	0.2664	G2M
-30	progenitor	0.14060001	0.27075002	G2M
-31	progenitor	0.14363334	0.27295002	G2M
-32	progenitor	0.14310001	0.277825	G2M
-33	progenitor	0.14686668	0.2806	G2M
-34	progenitor	0.14746666	0.28705	G2M
-35	progenitor	0.1488	0.291375	G2M
-36	progenitor	0.14686665	0.295125	G2M
-37	progenitor	0.14803334	0.29590002	G2M
-38	progenitor	0.14774999	0.30242503	G2M
-39	progenitor	0.14461668	0.30615002	G2M
-40	progenitor	0.14245	0.3091	G2M
-41	progenitor	0.14150001	0.313175	G2M
-42	progenitor	0.13565	0.316325	G2M
-43	progenitor	0.12808332	0.3198	G2M
-44	progenitor	0.12049997	0.3262	G2M
-45	progenitor	0.11080001	0.33355	G2M
-46	progenitor	0.09916668	0.33852503	G2M
-47	progenitor	0.08836666	0.34457502	G2M
-48	progenitor	0.0756	0.35015002	G2M
-49	progenitor	0.061966658	0.354175	G2M
-50	progenitor	0.04515001	0.361325	G2M
-51	progenitor	0.026250005	0.365675	G2M
-52	progenitor	0.008533329	0.371575	G2M
-53	progenitor	-0.0058666766	0.36895004	G2M
-54	progenitor	-0.01971668	0.36967498	G2M
-55	progenitor	-0.035949945	0.36804998	G2M
-56	progenitor	-0.04824999	0.36305	G2M
-57	progenitor	-0.06161666	0.35704997	G2M
-58	progenitor	-0.07620004	0.34805	G2M
-59	progenitor	-0.09081668	0.341575	G2M
-60	progenitor	-0.103000015	0.32840002	G2M
-61	progenitor	-0.11609998	0.317375	G2M
-62	progenitor	-0.12736666	0.30952498	G2M
-63	progenitor	-0.13890001	0.30077502	G2M
-64	progenitor	-0.15153334	0.28985	G2M
-65	progenitor	-0.16445002	0.28004998	G2M
-66	progenitor	-0.17231664	0.26997498	G2M
-67	progenitor	-0.18360004	0.2623	G2M
-68	progenitor	-0.19526666	0.250675	G2M
-69	progenitor	-0.20973334	0.24382502	G2M
-70	progenitor	-0.22153333	0.23462497	G2M
-71	progenitor	-0.23176664	0.22642499	G2M
-72	progenitor	-0.23878333	0.21525	G2M
-73	progenitor	-0.24899998	0.20759997	G2M
-74	progenitor	-0.25769997	0.196425	G2M
-75	progenitor	-0.266	0.190525	G2M
-76	progenitor	-0.27291664	0.185325	G2M
-77	progenitor	-0.27909997	0.17939998	G2M
-78	progenitor	-0.28546664	0.16992497	G2M
-79	progenitor	-0.2924833	0.16142498	G2M
-80	Ery	-0.30063334	0.158275	G2M
-81	Ery	-0.3081833	0.14844999	G2M
-82	Ery	-0.31410003	0.13829997	G2M
-83	Ery	-0.32265	0.12857503	G2M
-84	Ery	-0.33280006	0.12172499	G2M
-85	Ery	-0.34323335	0.11087498	G2M
-86	Ery	-0.3550667	0.09400001	G2M
-87	Ery	-0.36045003	0.074499995	G2M
-88	Ery	-0.36565	0.056499988	G2M
-89	Ery	-0.37118334	0.03820002	G2M
-90	Ery	-0.3749	0.026174992	G2M
-91	Ery	-0.37931666	0.019950002	G2M
-92	Ery	-0.38785002	0.012800008	G2M
-93	Ery	-0.3930334	0.0039000213	G2M
-94	Ery	-0.39623332	-0.0038750172	G1
-95	Ery	-0.40098336	-0.008474976	G1
-96	Ery	-0.41044998	-0.008125007	G1
-97	Ery	-0.41723332	-0.0113250315	G1
-98	Ery	-0.42673334	-0.008574992	G1
-99	Ery	-0.43113336	-0.009875029	G1
-100	Ery	-0.4387667	-0.009699941	G1
-101	Ery	-0.44501665	-0.006850004	G1
-102	Ery	-0.44808337	-0.0041999817	G1
-103	Ery	-0.45334998	-0.0044499934	G1
-104	Ery	-0.4579167	-0.0024499893	G1
-105	Ery	-0.4625	0.0014250278	G2M
-106	Ery	-0.46655002	0.003275007	G2M
-107	Ery	-0.4705	0.0074749887	G2M
-108	Ery	-0.47354996	0.011525005	G2M
-109	Ery	-0.47571668	0.012849987	G2M
-110	Ery	-0.47741672	0.014274985	G2M
-111	Ery	-0.47899997	0.015850008	G2M
-112	Ery	-0.48184994	0.017825007	G2M
-113	Ery	-0.48310003	0.021875024	G2M
-114	Ery	-0.48716664	0.023850024	G2M
-115	Ery	-0.48626667	0.024949968	G2M
-116	Ery	-0.4891	0.03274998	G2M
-117	Ery	-0.4909666	0.035274982	G2M
-118	Ery	-0.4974334	0.037799954	G2M
-119	Ery	-0.5008334	0.040574998	G2M
-120	Ery	-0.50525	0.044800013	G2M
-121	Ery	-0.50745004	0.045899987	G2M
-122	Ery	-0.51255	0.048699975	G2M
-123	Ery	-0.5148666	0.051825017	G2M
-124	Ery	-0.51621664	0.054074973	G2M
-125	Ery	-0.52003336	0.058725	G2M
-126	Ery	-0.5201167	0.06525004	G2M
-127	Ery	-0.5232334	0.06755	G2M
-128	Ery	-0.5255166	0.06912503	G2M
-129	Ery	-0.52691674	0.071750015	G2M
-130	Ery	-0.5294833	0.07469997	G2M
-131	Ery	-0.5308	0.07885	G2M
-132	Ery	-0.53328335	0.08200002	G2M
-133	Ery	-0.53339994	0.082275	G2M
-134	Ery	-0.5356667	0.08287498	G2M
-135	Ery	-0.53651667	0.083850026	G2M
-136	Ery	-0.53586674	0.08415002	G2M
-137	Ery	-0.5371834	0.08655003	G2M
-138	Ery	-0.53768337	0.08915001	G2M
-139	Ery	-0.5387167	0.086775005	G2M
-140	Ery	-0.5398166	0.08837497	G2M
-141	Ery	-0.5402333	0.09094998	G2M
-142	Ery	-0.5395833	0.09077501	G2M
-143	Ery	-0.5413166	0.094074994	G2M
-144	Ery	-0.5375334	0.094500035	G2M
-145	Ery	-0.5376667	0.09659997	G2M
-146	Ery	-0.5442666	0.09917498	G2M
-147	Ery	-0.5433	0.10099995	G2M
-148	Ery	-0.54293334	0.09899998	G2M
-149	Ery	-0.5396333	0.09729999	G2M
-150	Ery	-0.53550005	0.09580001	G2M
-151	Ery	-0.5340333	0.094500005	G2M
-152	Ery	-0.53835	0.094024986	G2M
-153	Ery	-0.5339166	0.09344998	G2M
-154	Ery	-0.5354667	0.095400006	G2M
-155	Ery	-0.5398333	0.09622499	G2M
-156	Ery	-0.54073334	0.09739998	G2M
-157	Ery	-0.54269993	0.09609997	G2M
-158	Ery	-0.54613334	0.09427503	G2M
-159	Ery	-0.5469167	0.09325001	G2M
-0	progenitor	0.26924998	0.20047499	S
-1	progenitor	0.24753334	0.15694998	S
-2	progenitor	0.2261	0.129575	S
-3	progenitor	0.20549999	0.12205	S
-4	progenitor	0.18906666	0.118075006	S
-5	progenitor	0.17461666	0.1156	S
-6	progenitor	0.1549	0.112900004	S
-7	progenitor	0.14206666	0.11277501	S
-8	progenitor	0.12825	0.11547499	S
-9	progenitor	0.11403333	0.116174996	G2M
-10	progenitor	0.10465	0.11955001	G2M
-11	progenitor	0.09291667	0.123825	G2M
-12	progenitor	0.08646667	0.12455	G2M
-13	progenitor	0.07824999	0.13115	G2M
-14	progenitor	0.07111666	0.13497499	G2M
-15	progenitor	0.06305	0.138225	G2M
-16	progenitor	0.059816666	0.14175001	G2M
-17	progenitor	0.055983335	0.1487	G2M
-18	progenitor	0.05093333	0.15525001	G2M
-19	progenitor	0.048833337	0.161075	G2M
-20	progenitor	0.047583334	0.16835001	G2M
-21	progenitor	0.040233333	0.1783	G2M
-22	progenitor	0.038650002	0.18339998	G2M
-23	progenitor	0.034033336	0.19080001	G2M
-24	progenitor	0.0334	0.19689998	G2M
-25	progenitor	0.036050003	0.19765002	G2M
-26	progenitor	0.037483335	0.20150003	G2M
-27	progenitor	0.0379	0.205475	G2M
-28	progenitor	0.03891667	0.21019998	G2M
-29	progenitor	0.041166663	0.21605001	G2M
-30	progenitor	0.041533336	0.22262499	G2M
-31	progenitor	0.0463	0.226375	G2M
-32	progenitor	0.048683327	0.22929999	G2M
-33	progenitor	0.057249997	0.233375	G2M
-34	progenitor	0.06268333	0.236	G2M
-35	progenitor	0.06565	0.23992498	G2M
-36	progenitor	0.06738335	0.24414998	G2M
-37	progenitor	0.07278331	0.24974999	G2M
-38	progenitor	0.07835	0.25365	G2M
-39	progenitor	0.08574999	0.25655	G2M
-40	progenitor	0.089816675	0.25997502	G2M
-41	progenitor	0.094816685	0.268325	G2M
-42	progenitor	0.10088334	0.27127498	G2M
-43	progenitor	0.10618336	0.27574998	G2M
-44	progenitor	0.11181665	0.27997503	G2M
-45	progenitor	0.12016666	0.28125003	G2M
-46	progenitor	0.120766655	0.2857	G2M
-47	progenitor	0.12061668	0.289625	G2M
-48	progenitor	0.12701666	0.292675	G2M
-49	progenitor	0.13323334	0.294025	G2M
-50	progenitor	0.13686669	0.29399997	G2M
-51	progenitor	0.14141665	0.296375	G2M
-52	progenitor	0.14054999	0.29835	G2M
-53	progenitor	0.13769999	0.30177498	G2M
-54	progenitor	0.13920003	0.306425	G2M
-55	progenitor	0.13541666	0.30935	G2M
-56	progenitor	0.13395001	0.31435	G2M
-57	progenitor	0.12931666	0.319175	G2M
-58	progenitor	0.12291667	0.32285002	G2M
-59	progenitor	0.11760001	0.32947502	G2M
-60	progenitor	0.1109	0.33325002	G2M
-61	progenitor	0.098733336	0.33807498	G2M
-62	progenitor	0.08863334	0.345725	G2M
-63	progenitor	0.074066654	0.347775	G2M
-64	progenitor	0.062050015	0.3543	G2M
-65	progenitor	0.050833344	0.359575	G2M
-66	progenitor	0.038566664	0.36534998	G2M
-67	progenitor	0.022033334	0.37015	G2M
-68	progenitor	0.009916633	0.37007502	G2M
-69	progenitor	-0.002099961	0.37010002	G2M
-70	progenitor	-0.013416678	0.36807504	G2M
-71	progenitor	-0.026216656	0.36464998	G2M
-72	progenitor	-0.04154995	0.357625	G2M
-73	progenitor	-0.054400027	0.35250002	G2M
-74	progenitor	-0.06606665	0.3451	G2M
-75	progenitor	-0.07311666	0.33777502	G2M
-76	progenitor	-0.077833325	0.32635	G2M
-77	progenitor	-0.08776665	0.3159	G2M
-78	progenitor	-0.09445	0.30395	G2M
-79	progenitor	-0.102666676	0.2935	G2M
-80	Mk	-0.10896668	0.282375	G2M
-81	Mk	-0.12169999	0.27165002	G2M
-82	Mk	-0.12861666	0.26255003	G2M
-83	Mk	-0.13356665	0.2516	G2M
-84	Mk	-0.1381667	0.2421	G2M
-85	Mk	-0.14588335	0.23299998	G2M
-86	Mk	-0.14643335	0.220175	G2M
-87	Mk	-0.15011665	0.216025	G2M
-88	Mk	-0.15608332	0.20797502	G2M
-89	Mk	-0.1635333	0.20320001	G2M
-90	Mk	-0.1667167	0.19779998	G2M
-91	Mk	-0.16811666	0.18747498	G2M
-92	Mk	-0.16958332	0.17795	G2M
-93	Mk	-0.17056668	0.16855001	G2M
-94	Mk	-0.17408332	0.16107498	G2M
-95	Mk	-0.17345	0.1532	G2M
-96	Mk	-0.17251664	0.147325	G2M
-97	Mk	-0.17686662	0.141125	G2M
-98	Mk	-0.17819998	0.1339	G2M
-99	Mk	-0.18205002	0.12702498	G2M
-100	Mk	-0.18008336	0.12057501	G2M
-101	Mk	-0.17778334	0.10987502	G2M
-102	Mk	-0.17706665	0.10052502	G2M
-103	Mk	-0.17208335	0.09229997	G2M
-104	Mk	-0.17455	0.09097502	G2M
-105	Mk	-0.17273334	0.087374985	G2M
-106	Mk	-0.17373335	0.08560002	G2M
-107	Mk	-0.17395002	0.07944998	G2M
-108	Mk	-0.17468333	0.07655001	G2M
-109	Mk	-0.1739833	0.07757497	G2M
-110	Mk	-0.17766666	0.08107501	G2M
-111	Mk	-0.17615	0.07807499	G2M
-112	Mk	-0.17605004	0.077325016	G2M
-113	Mk	-0.17686665	0.07712501	G2M
-114	Mk	-0.17955002	0.07734999	G2M
-115	Mk	-0.17851666	0.07519999	G2M
-116	Mk	-0.17718336	0.076775014	G2M
-117	Mk	-0.17596671	0.07339999	G2M
-118	Mk	-0.1750167	0.07412499	G2M
-119	Mk	-0.17744997	0.076675	G2M
-120	Mk	-0.1789	0.074625015	G2M
-121	Mk	-0.17714998	0.071624994	G2M
-122	Mk	-0.1736333	0.068425	G2M
-123	Mk	-0.17461663	0.06832498	G2M
-124	Mk	-0.17366666	0.069875	G2M
-125	Mk	-0.17350003	0.07087502	G2M
-126	Mk	-0.17423335	0.073125005	G2M
-127	Mk	-0.17289999	0.07657498	G2M
-128	Mk	-0.17336664	0.07489997	G2M
-129	Mk	-0.16989997	0.07117501	G2M
-130	Mk	-0.16938332	0.06972501	G2M
-131	Mk	-0.17073336	0.07189995	G2M
-132	Mk	-0.16995004	0.07332501	G2M
-133	Mk	-0.16946661	0.07052502	G2M
-134	Mk	-0.16478333	0.070250005	G2M
-135	Mk	-0.16570002	0.072375	G2M
-136	Mk	-0.16755003	0.073075026	G2M
-137	Mk	-0.16876668	0.076124996	G2M
-138	Mk	-0.16663334	0.07460004	G2M
-139	Mk	-0.1660833	0.07682499	G2M
-140	Mk	-0.16843331	0.0783	G2M
-141	Mk	-0.17143327	0.07712501	G2M
-142	Mk	-0.17213336	0.07727498	G2M
-143	Mk	-0.16951668	0.07885	G2M
-144	Mk	-0.16820005	0.078149974	G2M
-145	Mk	-0.16826665	0.07882503	G2M
-146	Mk	-0.17055002	0.08182496	G2M
-147	Mk	-0.17345	0.082975	G2M
-148	Mk	-0.17216668	0.086125016	G2M
-149	Mk	-0.17273334	0.09057501	G2M
-150	Mk	-0.17401668	0.092824996	G2M
-151	Mk	-0.17518333	0.091575	G2M
-152	Mk	-0.17483333	0.09237501	G2M
-153	Mk	-0.17593333	0.092875004	G2M
-154	Mk	-0.1739333	0.094374955	G2M
-155	Mk	-0.1740667	0.09417495	G2M
-156	Mk	-0.17770004	0.09324998	G2M
-157	Mk	-0.17335	0.09350002	G2M
-158	Mk	-0.1704	0.09047499	G2M
-159	Mk	-0.17143336	0.089825004	G2M
-0	progenitor	0.2660833	0.20005001	S
-1	progenitor	0.24146667	0.1564	S
-2	progenitor	0.22096668	0.12695	S
-3	progenitor	0.19886668	0.112325005	S
-4	progenitor	0.18153334	0.102675	S
-5	progenitor	0.16055	0.10249999	S
-6	progenitor	0.14478332	0.098000005	S
-7	progenitor	0.13021666	0.092875004	S
-8	progenitor	0.11686668	0.091899976	S
-9	progenitor	0.10476667	0.091975	S
-10	progenitor	0.09625	0.094950005	S
-11	progenitor	0.09105	0.09615001	G2M
-12	progenitor	0.0822	0.102025	G2M
-13	progenitor	0.074	0.10612498	G2M
-14	progenitor	0.062583335	0.10890001	G2M
-15	progenitor	0.052600004	0.11175001	G2M
-16	progenitor	0.045050006	0.112574995	G2M
-17	progenitor	0.038033333	0.11227499	G2M
-18	progenitor	0.03231667	0.11082502	G2M
-19	progenitor	0.028383333	0.11277501	G2M
-20	progenitor	0.021966662	0.11262502	G2M
-21	progenitor	0.02043334	0.110575005	G2M
-22	progenitor	0.017400004	0.110875025	G2M
-23	progenitor	0.017300002	0.111875	G2M
-24	progenitor	0.015683334	0.112075	G2M
-25	progenitor	0.014233332	0.11295	G2M
-26	progenitor	0.012683332	0.11170004	G2M
-27	progenitor	0.011016667	0.112225026	G2M
-28	progenitor	0.010216668	0.116024986	G2M
-29	progenitor	0.0077833347	0.11819999	G2M
-30	progenitor	0.004733335	0.119224995	G2M
-31	progenitor	0.002683334	0.12180002	G2M
-32	progenitor	0.006133329	0.11839999	G2M
-33	progenitor	0.0052000023	0.116349995	G2M
-34	progenitor	0.006116666	0.115775004	G2M
-35	progenitor	0.0019833297	0.115449995	G2M
-36	progenitor	0.0007166676	0.114999995	G2M
-37	progenitor	0.00016666204	0.113325	G2M
-38	progenitor	-0.0018666722	0.116224974	G2M
-39	progenitor	-0.0030833334	0.11517501	G2M
-40	progenitor	-0.0030166656	0.11082499	G2M
-41	progenitor	-0.0019833334	0.10034999	G2M
-42	progenitor	0.0010500029	0.094025	G2M
-43	progenitor	-0.0039666668	0.086150005	G2M
-44	progenitor	-0.0024000034	0.077549994	G2M
-45	progenitor	-0.0036166683	0.07370001	G2M
-46	progenitor	-0.00485	0.0651	G2M
-47	progenitor	-0.002550004	0.05520001	G2M
-48	progenitor	0.0003666673	0.041975006	G2M
-49	progenitor	-0.0010499991	0.030874997	G2M
-50	progenitor	0.0007333346	0.014625013	G2M
-51	progenitor	9.999983e-05	-0.0017999709	S
-52	progenitor	0.0018333336	-0.01987499	S
-53	progenitor	0.00090000033	-0.032900006	S
-54	progenitor	0.0029999996	-0.05064997	S
-55	progenitor	0.003983334	-0.068574995	S
-56	progenitor	-0.0008500004	-0.08140004	G1
-57	progenitor	-0.0029833335	-0.09470001	G1
-58	progenitor	-0.0021333336	-0.106824994	G1
-59	progenitor	-0.0015000002	-0.11555001	G1
-60	progenitor	-0.0022999998	-0.12255001	G1
-61	progenitor	-0.0017666668	-0.13125	G1
-62	progenitor	0.0033999998	-0.14297503	S
-63	progenitor	0.006316666	-0.15117502	S
-64	progenitor	0.005033333	-0.15187496	S
-65	progenitor	0.0031333338	-0.15367502	S
-66	progenitor	0.0035666667	-0.15197498	S
-67	progenitor	0.005016666	-0.14580002	S
-68	progenitor	0.0061	-0.142575	S
-69	progenitor	0.00515	-0.13665	S
-70	progenitor	0.0028499998	-0.12865001	S
-71	progenitor	0.0030833331	-0.12112501	S
-72	progenitor	0.0032833333	-0.11669999	S
-73	progenitor	0.0006166666	-0.11475003	S
-74	progenitor	-0.0006833335	-0.11412498	G1
-75	progenitor	-2.3283064e-10	-0.11702502	G1
-76	progenitor	0.0007333332	-0.121425	S
-77	progenitor	0.0005833333	-0.12972501	S
-78	progenitor	-0.0015333334	-0.13869998	G1
-79	progenitor	-0.0023	-0.14877501	G1
-80	Neu	-0.0046166666	-0.157125	G1
-81	Neu	-0.0035666665	-0.16639996	G1
-82	Neu	-0.0011666667	-0.17409998	G1
-83	Neu	-0.0026166667	-0.180325	G1
-84	Neu	-0.0001833333	-0.18502498	G1
-85	Neu	0.0035666665	-0.18762502	S
-86	Neu	0.0024333335	-0.19062501	S
-87	Neu	0.0023	-0.19277498	S
-88	Neu	0.0014833333	-0.19762498	S
-89	Neu	0.0024333335	-0.201725	S
-90	Neu	0.003866667	-0.20430002	S
-91	Neu	0.0067999996	-0.20865002	S
-92	Neu	0.0079333335	-0.21332502	S
-93	Neu	0.0068	-0.21907501	S
-94	Neu	0.0058	-0.22292498	S
-95	Neu	0.0069666663	-0.22795	S
-96	Neu	0.0023833334	-0.22915001	S
-97	Neu	0.0056166667	-0.230375	S
-98	Neu	0.0051666666	-0.23027502	S
-99	Neu	0.001966667	-0.23169999	S
-100	Neu	0.0033666668	-0.23169999	S
-101	Neu	0.0047333334	-0.233675	S
-102	Neu	0.0045666667	-0.23272496	S
-103	Neu	0.00060000026	-0.23024999	S
-104	Neu	0.0036833333	-0.22662503	S
-105	Neu	0.0025166667	-0.22107498	S
-106	Neu	0.0038833334	-0.22109997	S
-107	Neu	0.0033666666	-0.21907501	S
-108	Neu	0.0042666667	-0.218675	S
-109	Neu	0.0038	-0.2165	S
-110	Neu	0.004633333	-0.21385	S
-111	Neu	0.003266667	-0.21305001	S
-112	Neu	0.0034166668	-0.21077499	S
-113	Neu	0.0017166669	-0.21184999	S
-114	Neu	0.0012666667	-0.21437502	S
-115	Neu	-0.0016333334	-0.2153	G1
-116	Neu	-0.00043333336	-0.215675	G1
-117	Neu	0.0018833333	-0.21865001	S
-118	Neu	0.0014666667	-0.22187501	S
-119	Neu	-0.0020833332	-0.21787499	G1
-120	Neu	-0.0039000001	-0.21329997	G1
-121	Neu	-0.0023	-0.20820004	G1
-122	Neu	-0.00195	-0.21035	G1
-123	Neu	-0.0058833333	-0.20732501	G1
-124	Neu	-0.0070166667	-0.20714998	G1
-125	Neu	-0.008633333	-0.20389998	G1
-126	Neu	-0.006616667	-0.20832503	G1
-127	Neu	-0.003116667	-0.21047503	G1
-128	Neu	-0.0055833333	-0.21289998	G1
-129	Neu	-0.0042333333	-0.216575	G1
-130	Neu	-0.005016667	-0.21945003	G1
-131	Neu	-0.0020833334	-0.21799998	G1
-132	Neu	-0.0032	-0.21150002	G1
-133	Neu	-0.0033333339	-0.20989999	G1
-134	Neu	-0.0011666667	-0.21257499	G1
-135	Neu	-0.0023	-0.21222499	G1
-136	Neu	-0.0034666667	-0.2146	G1
-137	Neu	-0.0011833335	-0.21350002	G1
-138	Neu	-0.00325	-0.21377501	G1
-139	Neu	-0.0040166667	-0.213925	G1
-140	Neu	-0.0024666665	-0.21455	G1
-141	Neu	0.0028333336	-0.21975	S
-142	Neu	0.0032833333	-0.220325	S
-143	Neu	0.004766667	-0.22119999	S
-144	Neu	0.00705	-0.22362499	S
-145	Neu	0.0036	-0.22555003	S
-146	Neu	0.006733333	-0.22377498	S
-147	Neu	0.0025000002	-0.22509998	S
-148	Neu	0.0018	-0.221075	S
-149	Neu	6.666663e-05	-0.22125	S
-150	Neu	-0.0014499999	-0.219875	G1
-151	Neu	-0.0012833332	-0.21900001	G1
-152	Neu	-0.0001999999	-0.21767499	G1
-153	Neu	-0.0032666668	-0.21407498	G1
-154	Neu	-0.0017	-0.2116	G1
-155	Neu	-0.0028166666	-0.2077	G1
-156	Neu	-0.00245	-0.2053	G1
-157	Neu	-0.0011166666	-0.203825	G1
-158	Neu	-0.001966667	-0.20535001	G1
-159	Neu	-0.0027	-0.20725003	G1
Binary file test-data/tl.tsne.krumsiek11.h5ad has changed
Binary file test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed