# HG changeset patch
# User iuc
# Date 1571221953 14400
# Node ID 6a76b60e05f5c51ce0d2e0a8356ff71b2ad08d29
# Parent  6ea5a05a260aa2e0bd53fd53168302087b430b6f
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"

diff -r 6ea5a05a260a -r 6a76b60e05f5 README.md
--- a/README.md	Mon Mar 04 10:15:02 2019 -0500
+++ b/README.md	Wed Oct 16 06:32:33 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
diff -r 6ea5a05a260a -r 6a76b60e05f5 README.rst
--- a/README.rst	Mon Mar 04 10:15:02 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
diff -r 6ea5a05a260a -r 6a76b60e05f5 filter.xml
--- a/filter.xml	Mon Mar 04 10:15:02 2019 -0500
+++ b/filter.xml	Wed Oct 16 06:32:33 2019 -0400
@@ -1,5 +1,5 @@
-<tool id="scanpy_filter" name="Filter with scanpy" version="@galaxy_version@">
-    <description></description>
+<tool id="scanpy_filter" name="Filter" version="@galaxy_version@">
+    <description>with scanpy</description>
     <macros>
         <import>macros.xml</import>
     </macros>
@@ -14,94 +14,74 @@
 @CMD_read_inputs@
 
 #if $method.method == 'pp.filter_cells'
-res = sc.pp.filter_cells(
-    #if $modify_anndata.modify_anndata == 'true'
+sc.pp.filter_cells(
     adata,
-    #else
-    adata.X,
-    #end if
     #if $method.filter.filter == 'min_counts'
     min_counts=$method.filter.min_counts,
-    #elif $method.filter.filter == 'max_counts'
+    #else if $method.filter.filter == 'max_counts'
     max_counts=$method.filter.max_counts,
-    #elif $method.filter.filter == 'min_genes'
+    #else if $method.filter.filter == 'min_genes'
     min_genes=$method.filter.min_genes,
-    #elif $method.filter.filter == 'max_genes'
+    #else if $method.filter.filter == 'max_genes'
     max_genes=$method.filter.max_genes,
     #end if
     copy=False)
 
-    #if $modify_anndata.modify_anndata == 'true'
-df = adata.obs
-    #else
-df = pd.DataFrame(data=dict(cell_subset=res[0], number_per_cell=res[1]))
-    #end if
-
-    #if $method.filter.filter == 'min_counts' or $method.filter.filter == 'max_counts'
-df.to_csv('$counts_per_cell', sep='\t')
-    #elif $method.filter.filter == 'min_genes' or $method.filter.filter == 'max_genes'
-df.to_csv('$genes_per_cell', sep='\t')
-    #end if
-
-#elif $method.method == 'pp.filter_genes'
-res = sc.pp.filter_genes(
-    #if $modify_anndata.modify_anndata == 'true'
+#else if $method.method == 'pp.filter_genes'
+sc.pp.filter_genes(
     adata,
-    #else
-    adata.X,
-    #end if
     #if $method.filter.filter == 'min_counts'
     min_counts=$method.filter.min_counts,
-    #elif $method.filter.filter == 'max_counts'
+    #else if $method.filter.filter == 'max_counts'
     max_counts=$method.filter.max_counts,
-    #elif $method.filter.filter == 'min_cells'
+    #else if $method.filter.filter == 'min_cells'
     min_cells=$method.filter.min_cells,
-    #elif $method.filter.filter == 'max_cells'
+    #else if $method.filter.filter == 'max_cells'
     max_cells=$method.filter.max_cells,
     #end if
     copy=False)
 
-    #if $modify_anndata.modify_anndata == 'true'
-df = adata.var
-    #else
-df = pd.DataFrame(data=dict(gene_subset=res[0], number_per_gene=res[1]))
+#else if $method.method == 'tl.filter_rank_genes_groups'
+sc.tl.filter_rank_genes_groups(
+    adata,
+    #if str($method.key) != ''
+    key='$method.key',
     #end if
-
-    #if $method.filter.filter == 'min_counts' or $method.filter.filter == 'max_counts'
-df.to_csv('$counts_per_gene', sep='\t')
-    #elif $method.filter.filter == 'min_cells' or $method.filter.filter == 'max_cells'
-df.to_csv('$cells_per_gene', sep='\t')
+    #if str($method.groupby) != ''
+    groupby='$method.groupby',
     #end if
+    use_raw=$method.use_raw,
+    log=$method.log,
+    key_added='$method.key_added',
+    min_in_group_fraction=$method.min_in_group_fraction,
+    max_out_group_fraction=$method.max_out_group_fraction,
+    min_fold_change=$method.min_fold_change)
 
-#elif $method.method == 'pp.filter_genes_dispersion'
-res = sc.pp.filter_genes_dispersion(
-    #if $modify_anndata.modify_anndata == 'true'
-    adata,
-    #else
-    adata.X,
-    #end if
+#else if $method.method == "pp.highly_variable_genes"
+sc.pp.highly_variable_genes(
+    adata=adata,
     flavor='$method.flavor.flavor',
-    #if $method.flavor.flavor=='seurat'
+    #if $method.flavor.flavor == 'seurat'
+        #if str($method.flavor.min_mean) != ''
     min_mean=$method.flavor.min_mean,
+        #end if
+        #if str($method.flavor.max_mean) != ''
     max_mean=$method.flavor.max_mean,
+        #end if
+        #if str($method.flavor.min_disp) != ''
     min_disp=$method.flavor.min_disp,
-        #if $method.flavor.max_disp
+        #end if
+        #if str($method.flavor.max_disp) != ''
     max_disp=$method.flavor.max_disp,
         #end if
-    #else
+    #else if $method.flavor.flavor == 'cell_ranger'
     n_top_genes=$method.flavor.n_top_genes,
     #end if
     n_bins=$method.n_bins,
-    log=$method.log,
-    copy=False)
+    subset=$method.subset,
+    inplace=True)
 
-    #if $modify_anndata.modify_anndata == 'true'
-adata.var.to_csv('$per_gene', sep='\t')
-    #else
-pd.DataFrame(res).to_csv('$per_gene', sep='\t')
-    #end if
-
-#elif $method.method == 'pp.subsample'
+#else if $method.method == 'pp.subsample'
 sc.pp.subsample(
     data=adata,
     #if $method.type.type == 'fraction'
@@ -112,9 +92,21 @@
     random_state=$method.random_state,
     copy=False)
 
+#else if $method.method == "pp.downsample_counts"
+sc.pp.downsample_counts(
+    adata=adata,
+    #if str($method.counts_per_cell) != ''
+    counts_per_cell=$method.counts_per_cell,
+    #end if
+    #if str($method.total_counts) != ''
+    total_counts=$method.total_counts,
+    #end if
+    random_state=$method.random_state,
+    replace=$method.replace,
+    copy=False)
 #end if
 
-@CMD_anndata_write_modify_outputs@
+@CMD_anndata_write_outputs@
 ]]></configfile>
     </configfiles>
     <inputs>
@@ -123,11 +115,10 @@
             <param argument="method" type="select" label="Method used for filtering">
                 <option value="pp.filter_cells">Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`</option>
                 <option value="pp.filter_genes">Filter genes based on number of cells or counts, using `pp.filter_genes`</option>
-                <option value="pp.filter_genes_dispersion">Extract highly variable genes, using `pp.filter_genes_dispersion`</option>
-                <!--<option value="pp.highly_variable_genes">, using `tl.highly_variable_genes`</option>!-->
+                <option value="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, using `tl.filter_rank_genes_groups`</option>
+                <option value="pp.highly_variable_genes">, using `tl.highly_variable_genes`</option>
                 <option value="pp.subsample">Subsample to a fraction of the number of observations, using `pp.subsample`</option>
-                <!--<option value="queries.gene_coordinates">, using `queries.gene_coordinates`</option>!-->
-                <!--<option value="queries.mitochondrial_genes">, using `queries.mitochondrial_genes`</option>!-->
+                <option value="pp.downsample_counts">Downsample counts from count matrix, using `pp.downsample_counts`</option>
             </param>
             <when value="pp.filter_cells">
                 <conditional name="filter">
@@ -160,37 +151,47 @@
                         <option value="max_cells">Maximum number of cells expressed</option>
                     </param>
                     <when value="min_counts">
-                        <param argument="min_counts" type="integer" min="0" value="" label="Minimum number of counts required for a gene to pass filtering" help=""/>
+                        <param argument="min_counts" type="integer" min="0" value="" label="Minimum number of counts required for a gene to pass filtering"/>
                     </when>
                     <when value="max_counts">
-                        <param argument="max_counts" type="integer" min="0" value="" label="Maximum number of counts required for a gene to pass filtering" help=""/>
+                        <param argument="max_counts" type="integer" min="0" value="" label="Maximum number of counts required for a gene to pass filtering"/>
                     </when>
                     <when value="min_cells">
-                        <param argument="min_cells" type="integer" min="0" value="" label="Minimum number of cells expressed required for a gene to pass filtering" help=""/>
+                        <param argument="min_cells" type="integer" min="0" value="" label="Minimum number of cells expressed required for a gene to pass filtering"/>
                     </when>    
                     <when value="max_cells">
-                        <param argument="max_cells" type="integer" min="0" value="" label="Maximum number of cells expressed required for a gene to pass filtering" help=""/>
+                        <param argument="max_cells" type="integer" min="0" value="" label="Maximum number of cells expressed required for a gene to pass filtering"/>
                     </when>
                 </conditional>
             </when>
-            <when value="pp.filter_genes_dispersion">
+            <when value="tl.filter_rank_genes_groups">
+                <param argument="key" type="text" optional="true" label="Key in adata.uns where the rank_genes_groups output is stored"/>
+                <param argument="groupby" type="text" optional="true" label="The key of the observations grouping to consider"/>
+                <expand macro="param_use_raw"/>
+                <expand macro="param_log"/>
+                <param argument="key_added" type="text" value="rank_genes_groups_filtered" label="Key that will contain new values"/>
+                <param argument="min_in_group_fraction" type="float" min="0" max="1" value="0.25" label="Minimum fraction of genes expressing the gene within the categories"/>
+                <param argument="max_out_group_fraction" type="float" min="0" max="1" value="0.5" label="Maximum fraction of genes expressing the gene outside the categories"/>
+                <param argument="min_fold_change" type="integer" value="2" label="Minimum fold change"/>
+            </when>
+            <when value="pp.highly_variable_genes">
                 <conditional name='flavor'>
-                    <param argument="flavor" type="select" label="Flavor for computing normalized dispersion" help="">
+                    <param argument="flavor" type="select" label="Flavor for computing normalized dispersion">
                         <option value="seurat">seurat: expects non-logarithmized data</option>
                         <option value="cell_ranger">cell_ranger: usually called for logarithmized data</option>
                     </param>
                     <when value="seurat">
-                        <param argument="min_mean" type="float" value="0.0125" label="Minimal mean cutoff" help=""/>
-                        <param argument="max_mean" type="float" value="3" label="Maximal mean cutoff" help=""/>
-                        <param argument="min_disp" type="float" value="0.5" label="Minimal normalized dispersion cutoff" help=""/>
-                        <param argument="max_disp" type="float" value="" optional="true" label="Maximal normalized dispersion cutoff" help=""/>
+                        <param argument="min_mean" type="float" value="0.0125" label="Minimal mean cutoff"/>
+                        <param argument="max_mean" type="float" value="3" label="Maximal mean cutoff"/>
+                        <param argument="min_disp" type="float" value="0.5" label="Minimal normalized dispersion cutoff"/>
+                        <param argument="max_disp" type="float" value="" optional="true" label="Maximal normalized dispersion cutoff"/>
                     </when>
                     <when value="cell_ranger">
-                        <param argument="n_top_genes" type="integer" value="" label="Number of highly-variable genes to keep" help=""/>
+                        <param argument="n_top_genes" type="integer" value="" label="Number of highly-variable genes to keep"/>
                     </when>
                 </conditional>
                 <param argument="n_bins" type="integer" value="20" label="Number of bins for binning the mean gene expression" help="Normalization is done with respect to each bin. If just a single gene falls into a bin, the normalized dispersion is artificially set to 1"/>
-                <expand macro="param_log"/>
+                <param argument="subset" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Inplace subset to highly-variable genes?" help="Otherwise it merely indicates highly variable genes."/>
             </when>
             <when value="pp.subsample">
                 <conditional name="type">
@@ -199,44 +200,29 @@
                         <option value="n_obs">By number of observation</option>
                     </param>
                     <when value="fraction">
-                        <param argument="fraction" type="float" value="" label="Subsample to this `fraction` of the number of observations" help=""/>
+                        <param argument="fraction" type="float" value="" label="Subsample to this `fraction` of the number of observations"/>
                     </when>
                     <when value="n_obs">
-                        <param argument="n_obs" type="integer" min="0" value="" label="Subsample to this number of observations" help=""/>
+                        <param argument="n_obs" type="integer" min="0" value="" label="Subsample to this number of observations"/>
                     </when>
                 </conditional>
-                <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling" help=""/>
+                <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling"/>
+            </when>
+            <when value="pp.downsample_counts">
+                <param argument="counts_per_cell" type="integer" min="0" optional="true" label="Target total counts per cell" help="If a cell has more than ‘counts_per_cell’, it will be downsampled to this number. Resulting counts can be specified on a per cell basis by passing an array."/>
+                <param argument="total_counts" type="integer" min="0" optional="true" label="Target total counts" help="If the count matrix has more than total_counts it will be downsampled to have this number."/>
+                <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling"/>
+                <param argument="replace" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Sample the counts with replacement?"/>
             </when>
         </conditional>
-        <expand macro="anndata_modify_output_input"/>
     </inputs>
     <outputs>
-        <expand macro="anndata_modify_outputs"/>
-        <!-- for pp.filter_cells -->
-        <data name="counts_per_cell" format="tabular" label="${tool.name} on ${on_string}: Counts per cells after filtering">
-            <filter>method['method'] == 'pp.filter_cells' and (method['filter']['filter'] == 'min_counts' or method['filter']['filter'] == 'max_counts')</filter>
-        </data>
-        <data name="genes_per_cell" format="tabular" label="${tool.name} on ${on_string}: Number of genes per cell after filtering">
-            <filter>method['method'] == 'pp.filter_cells' and (method['filter']['filter'] == 'min_genes' or method['filter']['filter'] == 'max_genes')</filter>
-        </data>
-        <!-- for pp.filter_genes -->
-        <data name="counts_per_gene" format="tabular" label="${tool.name} on ${on_string}: Counts per genes after filtering">
-            <filter>method['method'] == 'pp.filter_genes' and (method['filter']['filter'] == 'min_counts' or method['filter']['filter'] == 'max_counts')</filter>
-        </data>
-        <data name="cells_per_gene" format="tabular" label="${tool.name} on ${on_string}: Number of cells per genes after filtering">
-            <filter>method['method'] == 'pp.filter_genes' and (method['filter']['filter'] == 'min_cells' or method['filter']['filter'] == 'max_cells')</filter>
-        </data>
-        <!-- for pp.filter_genes_dispersion -->
-        <data name="per_gene" format="tabular" label="${tool.name} on ${on_string}: Means, dispersions and normalized dispersions per gene">
-            <filter>method['method'] == 'pp.filter_genes_dispersion'</filter>
-        </data>
+        <expand macro="anndata_outputs"/>
     </outputs>
     <tests>
-        <test expect_num_outputs="2">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 1 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
                 <param name="method" value="pp.filter_cells"/>
                 <conditional name="filter">
@@ -244,67 +230,15 @@
                     <param name="min_counts" value="3"/>
                 </conditional>
             </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="h5ad" />
-            </conditional>
             <assert_stdout>
                 <has_text_matching expression="sc.pp.filter_cells"/>
                 <has_text_matching expression="min_counts=3"/>
             </assert_stdout>
-            <output name="anndata_out_h5ad" file="pp.filter_cells.krumsiek11-min_counts.h5ad" ftype="h5" compare="sim_size"/>
-            <output name="counts_per_cell">
-                <assert_contents>
-                    <has_text_matching expression="cell_type\tn_counts" />
-                    <has_text_matching expression="46\tprogenitor\t3.028" />
-                    <has_text_matching expression="85\tEry\t3.7001" />
-                    <has_text_matching expression="150\tMk\t4.095" />
-                    <has_n_columns n="3" />
-                </assert_contents>
-            </output>
+            <output name="anndata_out" file="pp.filter_cells.krumsiek11-min_counts.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="2">
-            <conditional name="input">
-                <param name="format" value="loom" />
-                <param name="adata" value="krumsiek11.loom" />
-                <param name="sparse" value="True"/>
-                <param name="cleanup" value="False"/>
-                <param name="x_name"  value="spliced"/>
-                <param name="obs_names" value="CellID" />
-                <param name="var_names" value="Gene"/>
-            </conditional>
-            <conditional name="method">
-                <param name="method" value="pp.filter_cells"/>
-                <conditional name="filter">
-                    <param name="filter" value="min_counts"/>
-                    <param name="min_counts" value="3"/>
-                </conditional>
-            </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="loom" />
-            </conditional>
-            <assert_stdout>
-                <has_text_matching expression="sc.pp.filter_cells"/>
-                <has_text_matching expression="min_counts=3"/>
-            </assert_stdout>
-            <output name="anndata_out_loom" file="pp.filter_cells.krumsiek11-min_counts.loom" ftype="loom" compare="sim_size"/>
-            <output name="counts_per_cell">
-                <assert_contents>
-                    <has_text_matching expression="cell_type\tn_counts" />
-                    <has_text_matching expression="46\tprogenitor\t3.028" />
-                    <has_text_matching expression="85\tEry\t3.7001" />
-                    <has_text_matching expression="97\tMo\t3.925" />
-                    <has_text_matching expression="150\tMk\t4.095" />
-                    <has_n_columns n="3" />
-                </assert_contents>
-            </output>
-        </test>
-        <test expect_num_outputs="1">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad"/>
-            </conditional>
+        <test>
+            <!-- test 2 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
                 <param name="method" value="pp.filter_cells"/>
                 <conditional name="filter">
@@ -312,21 +246,16 @@
                     <param name="max_genes" value="100"/>
                 </conditional>
             </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="false"/>
-            </conditional>
             <assert_stdout>
                 <has_text_matching expression="sc.pp.filter_cells"/>
-                <has_text_matching expression="adata.X"/>
+                <has_text_matching expression="adata"/>
                 <has_text_matching expression="max_genes=100"/>
             </assert_stdout>
-            <output name="genes_per_cell" file="pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular"/>
+            <output name="anndata_out" file="pp.filter_cells.krumsiek11-max_genes.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="2">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 3 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
                 <param name="method" value="pp.filter_genes"/>
                 <conditional name="filter">
@@ -334,102 +263,84 @@
                     <param name="min_counts" value="3"/>
                 </conditional>
             </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="h5ad" />
-            </conditional>
             <assert_stdout>
                 <has_text_matching expression="sc.pp.filter_genes"/>
                 <has_text_matching expression="min_counts=3"/>
             </assert_stdout>
-            <output name="anndata_out_h5ad" file="pp.filter_genes.krumsiek11-min_counts.h5ad" ftype="h5" compare="sim_size"/>
-            <output name="counts_per_gene" file="pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular"/>
+            <output name="anndata_out" file="pp.filter_genes.krumsiek11-min_counts.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="1">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="pbmc68k_reduced.h5ad"/>
-            </conditional>
+        <test>
+            <!-- test 4 -->
+            <param name="adata" value="tl.rank_genes_groups.krumsiek11.h5ad" />
             <conditional name="method">
-                <param name="method" value="pp.filter_genes"/>
-                <conditional name="filter">
-                    <param name="filter" value="max_cells"/>
-                    <param name="max_cells" value="500"/>
-                </conditional>
-            </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="false"/>
+                <param name="method" value="tl.filter_rank_genes_groups"/>
+                <param name="key" value="rank_genes_groups"/>
+                <param name="use_raw" value="False"/>
+                <param name="log" value="False"/>
+                <param name="key_added" value="rank_genes_groups_filtered"/>
+                <param name="min_in_group_fraction" value="0.25"/>
+                <param name="max_out_group_fraction" value="0.5"/>
+                <param name="min_fold_change" value="3"/>
             </conditional>
             <assert_stdout>
-                <has_text_matching expression="sc.pp.filter_genes"/>
-                <has_text_matching expression="adata.X"/>
-                <has_text_matching expression="max_cells=500"/>
+                <has_text_matching expression="tl.filter_rank_genes_groups"/>
+                <has_text_matching expression="key='rank_genes_groups'"/>
+                <has_text_matching expression="use_raw=False"/>
+                <has_text_matching expression="log=False"/>
+                <has_text_matching expression="key_added='rank_genes_groups_filtered'"/>
+                <has_text_matching expression="min_in_group_fraction=0.25"/>
+                <has_text_matching expression="max_out_group_fraction=0.5"/>
+                <has_text_matching expression="min_fold_change=3"/>
             </assert_stdout>
-            <output name="cells_per_gene" file="pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular"/>
+            <output name="anndata_out" file="pp.filter_rank_genes_groups.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="2">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 5 -->
+            <param name="adata" value="blobs.h5ad"/>
             <conditional name="method">
-                <param name="method" value="pp.filter_genes_dispersion"/>
+                <param name="method" value="pp.highly_variable_genes"/>
                 <conditional name="flavor">
                     <param name="flavor" value="seurat"/>
                     <param name="min_mean" value="0.0125"/>
                     <param name="max_mean" value="3"/>
                     <param name="min_disp" value="0.5"/>
                 </conditional>
-                <param name="n_bins" value="20" />
-                <param name="log" value="true"/>
-            </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="h5ad" />
+                <param name="n_bins" value="20"/>
+                <param name="subset" value="false"/>
             </conditional>
             <assert_stdout>
-                <has_text_matching expression="sc.pp.filter_genes_dispersion"/>
+                <has_text_matching expression="sc.pp.highly_variable_genes"/>
                 <has_text_matching expression="flavor='seurat'"/>
                 <has_text_matching expression="min_mean=0.0125"/>
-                <has_text_matching expression="max_mean=3.0"/>
+                <has_text_matching expression="max_mean=3"/>
                 <has_text_matching expression="min_disp=0.5"/>
                 <has_text_matching expression="n_bins=20"/>
-                <has_text_matching expression="log=True"/>
+                <has_text_matching expression="subset=False"/>
             </assert_stdout>
-            <output name="anndata_out_h5ad" file="pp.filter_genes_dispersion.krumsiek11-seurat.h5ad" ftype="h5" compare="sim_size"/>
-            <output name="per_gene" file="pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular"/>
+            <output name="anndata_out" file="pp.highly_variable_genes.seurat.blobs.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="1">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 6 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
-                <param name="method" value="pp.filter_genes_dispersion"/>
+                <param name="method" value="pp.highly_variable_genes"/>
                 <conditional name="flavor">
                     <param name="flavor" value="cell_ranger"/>
                     <param name="n_top_genes" value="2"/>
                 </conditional>
                 <param name="n_bins" value="20"/>
-                <param name="log" value="true"/>
-            </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="false"/>
             </conditional>
             <assert_stdout>
-                <has_text_matching expression="sc.pp.filter_genes_dispersion"/>
+                <has_text_matching expression="sc.pp.highly_variable_genes"/>
                 <has_text_matching expression="flavor='cell_ranger'"/>
                 <has_text_matching expression="n_top_genes=2"/>
                 <has_text_matching expression="n_bins=20"/>
-                <has_text_matching expression="og=True"/>
             </assert_stdout>
-            <output name="per_gene" file="pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular"/>
+            <output name="anndata_out" file="pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="1">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 7 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
                 <param name="method" value="pp.subsample"/>
                 <conditional name="type">
@@ -438,22 +349,16 @@
                 </conditional>
                 <param name="random_state" value="0"/>
             </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="h5ad" />
-            </conditional>
             <assert_stdout>
                 <has_text_matching expression="sc.pp.subsample"/>
                 <has_text_matching expression="fraction=0.5"/>
                 <has_text_matching expression="random_state=0"/>
             </assert_stdout>
-            <output name="anndata_out_h5ad" file="pp.subsample.krumsiek11_fraction.h5ad" ftype="h5" compare="sim_size"/>
+            <output name="anndata_out" file="pp.subsample.krumsiek11_fraction.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test expect_num_outputs="1">
-            <conditional name="input">
-                <param name="format" value="h5ad" />
-                <param name="adata" value="krumsiek11.h5ad" />
-            </conditional>
+        <test>
+            <!-- test 8 -->
+            <param name="adata" value="krumsiek11.h5ad" />
             <conditional name="method">
                 <param name="method" value="pp.subsample"/>
                 <conditional name="type">
@@ -462,16 +367,29 @@
                 </conditional>
                 <param name="random_state" value="0"/>
             </conditional>
-            <conditional name="modify_anndata">
-                <param name="modify_anndata" value="true"/>
-                <param name="anndata_output_format" value="h5ad" />
-            </conditional>
             <assert_stdout>
                 <has_text_matching expression="sc.pp.subsample"/>
                 <has_text_matching expression="n_obs=10"/>
                 <has_text_matching expression="random_state=0"/>
             </assert_stdout>
-            <output name="anndata_out_h5ad" file="pp.subsample.krumsiek11_n_obs.h5ad" ftype="h5" compare="sim_size"/>
+            <output name="anndata_out" file="pp.subsample.krumsiek11_n_obs.h5ad" ftype="h5ad" compare="sim_size"/>
+        </test>
+        <test>
+            <!-- test 9 -->
+            <param name="adata" value="random-randint.h5ad" />
+            <conditional name="method">
+                <param name="method" value="pp.downsample_counts"/>
+                <param name="total_counts" value="20000"/>
+                <param name="random_state" value="0"/>
+                <param name="replace" value="false"/>
+            </conditional>
+            <assert_stdout>
+                <has_text_matching expression="sc.pp.downsample_counts"/>
+                <has_text_matching expression="total_counts=20000"/>
+                <has_text_matching expression="random_state=0"/>
+                <has_text_matching expression="replace=False"/>
+            </assert_stdout>
+            <output name="anndata_out" file="pp.downsample_counts.random-randint.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
     </tests>
     <help><![CDATA[
@@ -487,12 +405,7 @@
 `max_counts`, `max_genes` per call.
 
 More details on the `scanpy documentation
-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.filter_cells.html#scanpy.api.pp.filter_cells>`__
-
-Return
-------
-
-number_per_cell : Number per cell (either `n_counts` or `n_genes` per cell)
+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.filter_cells.html>`__
 
 
 Filter genes based on number of cells or counts (`pp.filter_genes`)
@@ -506,42 +419,38 @@
 `max_counts`, `max_cells` per call.
 
 More details on the `scanpy documentation
-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.filter_genes.html#scanpy.api.pp.filter_genes>`__
-
-Return
-------
-
-number_per_gene : Number per genes (either `n_counts` or `n_genes` per cell)
+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.filter_genes.html>`__
 
 
-Extract highly variable genes (`pp.filter_genes_dispersion`)
-============================================================
-
-If trying out parameters, pass the data matrix instead of AnnData.
-
-Depending on `flavor`, this reproduces the R-implementations of Seurat and Cell Ranger.
-
-The normalized dispersion is obtained by scaling with the mean and standard
-deviation of the dispersions for genes falling into a given bin for mean
-expression of genes. This means that for each bin of mean expression, highly
-variable genes are selected.
-
-Use `flavor='cell_ranger'` with care and in the same way as in `pp.recipe_zheng17`.
+Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (`tl.filter_rank_genes_groups`)
+==========================================================================================================================================================
 
 More details on the `scanpy documentation
-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.filter_genes_dispersion.html#scanpy.api.pp.filter_genes_dispersion>`__
+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.filter_rank_genes_groups.html>`__
+
 
-Returns
--------
-- The annotated matrix filtered, with the annotations
-- A table with the means, dispersions, and normalized dispersions per gene, logarithmized when `log` is `True`.
+Annotate highly variable genes (`pp.highly_variable_genes`)
+===========================================================
+
+It expects logarithmized data.
+
+Depending on flavor, this reproduces the R-implementations of Seurat or Cell Ranger. The normalized dispersion is obtained by scaling with the mean and standard deviation of the dispersions for genes falling into a given bin for mean expression of genes. This means that for each bin of mean expression, highly variable genes are selected.
 
 
 Subsample to a fraction of the number of observations (`pp.subsample`)
 ======================================================================
 
 More details on the `scanpy documentation
-<https://scanpy.readthedocs.io/en/latest/api/scanpy.api.pp.subsample.html#scanpy.api.pp.subsample>`__
+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.subsample.html>`__
+
+Downsample counts (`pp.downsample_counts`)
+==========================================
+
+Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This
+has been implemented by M. D. Luecken.
+
+More details on the `scanpy documentation
+<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.downsample_counts.html>`__
 
 
     ]]></help>
diff -r 6ea5a05a260a -r 6a76b60e05f5 macros.xml
--- a/macros.xml	Mon Mar 04 10:15:02 2019 -0500
+++ b/macros.xml	Wed Oct 16 06:32:33 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>
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/blobs.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.dotplot.krumsiek11.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.draw_graph.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.highly_variable_genes.seurat.blobs.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.pca_overview.pp.pca.krumsiek11.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.rank_genes_groups_violin.Ery.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pl.rank_genes_groups_violin.Neu.png
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_cells.krumsiek11-max_genes.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_cells.krumsiek11-min_counts.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular
--- a/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,641 +0,0 @@
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular
--- a/test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-index	n_counts
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular
--- a/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
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-217	True	211
-218	True	176
-219	True	329
-220	True	8
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad
Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular	Mon Mar 04 10:15:02 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	
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular	Mon Mar 04 10:15:02 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
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.filter_rank_genes_groups.h5ad
Binary file test-data/pp.filter_rank_genes_groups.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad
Binary file test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.highly_variable_genes.seurat.blobs.h5ad
Binary file test-data/pp.highly_variable_genes.seurat.blobs.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.log1p.krumsiek11.h5ad
Binary file test-data/pp.log1p.krumsiek11.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.normalize_per_cell.obs.krumsiek11.tabular
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,641 +0,0 @@
-index	cell_type
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-0	progenitor
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-0	progenitor
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-84	Neu
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-86	Neu
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-115	Neu
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-118	Neu
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-123	Neu
-124	Neu
-125	Neu
-126	Neu
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-128	Neu
-129	Neu
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-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
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-145	Neu
-146	Neu
-147	Neu
-148	Neu
-149	Neu
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.normalize_total.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.recipe_seurat.recipe_zheng17.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.regress_out.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.scale_max_value.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.sqrt.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/pp.subsample.krumsiek11_fraction.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/sparce_csr_matrix.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular
--- a/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,100 +0,0 @@
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular
--- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular	Mon Mar 04 10:15:02 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
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-276	2Ery	1	44	56
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-716	15Mo	0	3	29
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-2688	11DC	2	52	4
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-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
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.pca.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.rank_genes_groups.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad
Binary file test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.score_genes.krumsiek11.h5ad
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diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.score_genes.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular	Mon Mar 04 10:15:02 2019 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,641 +0,0 @@
-index	cell_type	score
-0	progenitor	
-1	progenitor	
-2	progenitor	
-3	progenitor	
-4	progenitor	
-5	progenitor	
-6	progenitor	
-7	progenitor	
-8	progenitor	
-9	progenitor	
-10	progenitor	
-11	progenitor	
-12	progenitor	
-13	progenitor	
-14	progenitor	
-15	progenitor	
-16	progenitor	
-17	progenitor	
-18	progenitor	
-19	progenitor	
-20	progenitor	
-21	progenitor	
-22	progenitor	
-23	progenitor	
-24	progenitor	
-25	progenitor	
-26	progenitor	
-27	progenitor	
-28	progenitor	
-29	progenitor	
-30	progenitor	
-31	progenitor	
-32	progenitor	
-33	progenitor	
-34	progenitor	
-35	progenitor	
-36	progenitor	
-37	progenitor	
-38	progenitor	
-39	progenitor	
-40	progenitor	
-41	progenitor	
-42	progenitor	
-43	progenitor	
-44	progenitor	
-45	progenitor	
-46	progenitor	
-47	progenitor	
-48	progenitor	
-49	progenitor	
-50	progenitor	
-51	progenitor	
-52	progenitor	
-53	progenitor	
-54	progenitor	
-55	progenitor	
-56	progenitor	
-57	progenitor	
-58	progenitor	
-59	progenitor	
-60	progenitor	
-61	progenitor	
-62	progenitor	
-63	progenitor	
-64	progenitor	
-65	progenitor	
-66	progenitor	
-67	progenitor	
-68	progenitor	
-69	progenitor	
-70	progenitor	
-71	progenitor	
-72	progenitor	
-73	progenitor	
-74	progenitor	
-75	progenitor	
-76	progenitor	
-77	progenitor	
-78	progenitor	
-79	progenitor	
-80	Mo	
-81	Mo	
-82	Mo	
-83	Mo	
-84	Mo	
-85	Mo	
-86	Mo	
-87	Mo	
-88	Mo	
-89	Mo	
-90	Mo	
-91	Mo	
-92	Mo	
-93	Mo	
-94	Mo	
-95	Mo	
-96	Mo	
-97	Mo	
-98	Mo	
-99	Mo	
-100	Mo	
-101	Mo	
-102	Mo	
-103	Mo	
-104	Mo	
-105	Mo	
-106	Mo	
-107	Mo	
-108	Mo	
-109	Mo	
-110	Mo	
-111	Mo	
-112	Mo	
-113	Mo	
-114	Mo	
-115	Mo	
-116	Mo	
-117	Mo	
-118	Mo	
-119	Mo	
-120	Mo	
-121	Mo	
-122	Mo	
-123	Mo	
-124	Mo	
-125	Mo	
-126	Mo	
-127	Mo	
-128	Mo	
-129	Mo	
-130	Mo	
-131	Mo	
-132	Mo	
-133	Mo	
-134	Mo	
-135	Mo	
-136	Mo	
-137	Mo	
-138	Mo	
-139	Mo	
-140	Mo	
-141	Mo	
-142	Mo	
-143	Mo	
-144	Mo	
-145	Mo	
-146	Mo	
-147	Mo	
-148	Mo	
-149	Mo	
-150	Mo	
-151	Mo	
-152	Mo	
-153	Mo	
-154	Mo	
-155	Mo	
-156	Mo	
-157	Mo	
-158	Mo	
-159	Mo	
-0	progenitor	
-1	progenitor	
-2	progenitor	
-3	progenitor	
-4	progenitor	
-5	progenitor	
-6	progenitor	
-7	progenitor	
-8	progenitor	
-9	progenitor	
-10	progenitor	
-11	progenitor	
-12	progenitor	
-13	progenitor	
-14	progenitor	
-15	progenitor	
-16	progenitor	
-17	progenitor	
-18	progenitor	
-19	progenitor	
-20	progenitor	
-21	progenitor	
-22	progenitor	
-23	progenitor	
-24	progenitor	
-25	progenitor	
-26	progenitor	
-27	progenitor	
-28	progenitor	
-29	progenitor	
-30	progenitor	
-31	progenitor	
-32	progenitor	
-33	progenitor	
-34	progenitor	
-35	progenitor	
-36	progenitor	
-37	progenitor	
-38	progenitor	
-39	progenitor	
-40	progenitor	
-41	progenitor	
-42	progenitor	
-43	progenitor	
-44	progenitor	
-45	progenitor	
-46	progenitor	
-47	progenitor	
-48	progenitor	
-49	progenitor	
-50	progenitor	
-51	progenitor	
-52	progenitor	
-53	progenitor	
-54	progenitor	
-55	progenitor	
-56	progenitor	
-57	progenitor	
-58	progenitor	
-59	progenitor	
-60	progenitor	
-61	progenitor	
-62	progenitor	
-63	progenitor	
-64	progenitor	
-65	progenitor	
-66	progenitor	
-67	progenitor	
-68	progenitor	
-69	progenitor	
-70	progenitor	
-71	progenitor	
-72	progenitor	
-73	progenitor	
-74	progenitor	
-75	progenitor	
-76	progenitor	
-77	progenitor	
-78	progenitor	
-79	progenitor	
-80	Ery	
-81	Ery	
-82	Ery	
-83	Ery	
-84	Ery	
-85	Ery	
-86	Ery	
-87	Ery	
-88	Ery	
-89	Ery	
-90	Ery	
-91	Ery	
-92	Ery	
-93	Ery	
-94	Ery	
-95	Ery	
-96	Ery	
-97	Ery	
-98	Ery	
-99	Ery	
-100	Ery	
-101	Ery	
-102	Ery	
-103	Ery	
-104	Ery	
-105	Ery	
-106	Ery	
-107	Ery	
-108	Ery	
-109	Ery	
-110	Ery	
-111	Ery	
-112	Ery	
-113	Ery	
-114	Ery	
-115	Ery	
-116	Ery	
-117	Ery	
-118	Ery	
-119	Ery	
-120	Ery	
-121	Ery	
-122	Ery	
-123	Ery	
-124	Ery	
-125	Ery	
-126	Ery	
-127	Ery	
-128	Ery	
-129	Ery	
-130	Ery	
-131	Ery	
-132	Ery	
-133	Ery	
-134	Ery	
-135	Ery	
-136	Ery	
-137	Ery	
-138	Ery	
-139	Ery	
-140	Ery	
-141	Ery	
-142	Ery	
-143	Ery	
-144	Ery	
-145	Ery	
-146	Ery	
-147	Ery	
-148	Ery	
-149	Ery	
-150	Ery	
-151	Ery	
-152	Ery	
-153	Ery	
-154	Ery	
-155	Ery	
-156	Ery	
-157	Ery	
-158	Ery	
-159	Ery	
-0	progenitor	
-1	progenitor	
-2	progenitor	
-3	progenitor	
-4	progenitor	
-5	progenitor	
-6	progenitor	
-7	progenitor	
-8	progenitor	
-9	progenitor	
-10	progenitor	
-11	progenitor	
-12	progenitor	
-13	progenitor	
-14	progenitor	
-15	progenitor	
-16	progenitor	
-17	progenitor	
-18	progenitor	
-19	progenitor	
-20	progenitor	
-21	progenitor	
-22	progenitor	
-23	progenitor	
-24	progenitor	
-25	progenitor	
-26	progenitor	
-27	progenitor	
-28	progenitor	
-29	progenitor	
-30	progenitor	
-31	progenitor	
-32	progenitor	
-33	progenitor	
-34	progenitor	
-35	progenitor	
-36	progenitor	
-37	progenitor	
-38	progenitor	
-39	progenitor	
-40	progenitor	
-41	progenitor	
-42	progenitor	
-43	progenitor	
-44	progenitor	
-45	progenitor	
-46	progenitor	
-47	progenitor	
-48	progenitor	
-49	progenitor	
-50	progenitor	
-51	progenitor	
-52	progenitor	
-53	progenitor	
-54	progenitor	
-55	progenitor	
-56	progenitor	
-57	progenitor	
-58	progenitor	
-59	progenitor	
-60	progenitor	
-61	progenitor	
-62	progenitor	
-63	progenitor	
-64	progenitor	
-65	progenitor	
-66	progenitor	
-67	progenitor	
-68	progenitor	
-69	progenitor	
-70	progenitor	
-71	progenitor	
-72	progenitor	
-73	progenitor	
-74	progenitor	
-75	progenitor	
-76	progenitor	
-77	progenitor	
-78	progenitor	
-79	progenitor	
-80	Mk	
-81	Mk	
-82	Mk	
-83	Mk	
-84	Mk	
-85	Mk	
-86	Mk	
-87	Mk	
-88	Mk	
-89	Mk	
-90	Mk	
-91	Mk	
-92	Mk	
-93	Mk	
-94	Mk	
-95	Mk	
-96	Mk	
-97	Mk	
-98	Mk	
-99	Mk	
-100	Mk	
-101	Mk	
-102	Mk	
-103	Mk	
-104	Mk	
-105	Mk	
-106	Mk	
-107	Mk	
-108	Mk	
-109	Mk	
-110	Mk	
-111	Mk	
-112	Mk	
-113	Mk	
-114	Mk	
-115	Mk	
-116	Mk	
-117	Mk	
-118	Mk	
-119	Mk	
-120	Mk	
-121	Mk	
-122	Mk	
-123	Mk	
-124	Mk	
-125	Mk	
-126	Mk	
-127	Mk	
-128	Mk	
-129	Mk	
-130	Mk	
-131	Mk	
-132	Mk	
-133	Mk	
-134	Mk	
-135	Mk	
-136	Mk	
-137	Mk	
-138	Mk	
-139	Mk	
-140	Mk	
-141	Mk	
-142	Mk	
-143	Mk	
-144	Mk	
-145	Mk	
-146	Mk	
-147	Mk	
-148	Mk	
-149	Mk	
-150	Mk	
-151	Mk	
-152	Mk	
-153	Mk	
-154	Mk	
-155	Mk	
-156	Mk	
-157	Mk	
-158	Mk	
-159	Mk	
-0	progenitor	
-1	progenitor	
-2	progenitor	
-3	progenitor	
-4	progenitor	
-5	progenitor	
-6	progenitor	
-7	progenitor	
-8	progenitor	
-9	progenitor	
-10	progenitor	
-11	progenitor	
-12	progenitor	
-13	progenitor	
-14	progenitor	
-15	progenitor	
-16	progenitor	
-17	progenitor	
-18	progenitor	
-19	progenitor	
-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	
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad
Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular	Mon Mar 04 10:15:02 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
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.tsne.krumsiek11.h5ad
Binary file test-data/tl.tsne.krumsiek11.h5ad has changed
diff -r 6ea5a05a260a -r 6a76b60e05f5 test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
Binary file test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed