# HG changeset patch # User iuc # Date 1571221870 14400 # Node ID a9f14e2d1655b2c6b8e0b312e4642b1d4dce4332 # Parent ed64c90a9b930a7536fcfc004e378135ee29cc79 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5" diff -r ed64c90a9b93 -r a9f14e2d1655 README.md --- a/README.md Mon Mar 04 10:16:12 2019 -0500 +++ b/README.md Wed Oct 16 06:31:10 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 ed64c90a9b93 -r a9f14e2d1655 README.rst --- a/README.rst Mon Mar 04 10:16:12 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 ed64c90a9b93 -r a9f14e2d1655 macros.xml --- a/macros.xml Mon Mar 04 10:16:12 2019 -0500 +++ b/macros.xml Wed Oct 16 06:31:10 2019 -0400 @@ -1,10 +1,12 @@ - 1.4 + 1.4.4 scanpy loompy + h5py + leidenalg @@ -14,102 +16,33 @@ - + - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - - anndata_output_format == 'h5ad' - - - anndata_output_format == 'loom' - - - - - modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad' - - - modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom' - + - - @@ -423,7 +356,7 @@ - + @@ -473,7 +406,7 @@ @@ -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' ]]> - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + -
+ + + + + + + + + + + + + + + + + + + + + + - + @@ -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 ]]> @@ -847,8 +771,6 @@ #silent $components.append(str($s.axis1) + ',' + str($s.axis2)) #end for components=$components, -#else - components=None, #end if ]]> @@ -877,7 +799,7 @@ - + @@ -910,7 +832,7 @@ - + @@ -937,8 +859,8 @@ - - + + @@ -956,7 +878,7 @@ ]]> - + @@ -982,9 +904,13 @@ + + + + + + + + + + + + + + + + + + + + - - - - - - + - - + + - - + + @@ -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 @@ + + + + + + diff -r ed64c90a9b93 -r a9f14e2d1655 normalize.xml --- a/normalize.xml Mon Mar 04 10:16:12 2019 -0500 +++ b/normalize.xml Wed Oct 16 06:31:10 2019 -0400 @@ -1,5 +1,5 @@ - - + + with scanpy macros.xml @@ -13,26 +13,36 @@ @CMD_imports@ @CMD_read_inputs@ -#if $method.method == "pp.normalize_per_cell" -sc.pp.normalize_per_cell( - data=adata, - #if $method.counts_per_cell_after - counts_per_cell_after=$method.counts_per_cell_after, +#if $method.method == "pp.normalize_total" +sc.pp.normalize_total( + adata, + #if str($method.target_sum)!= '' + target_sum=$method.target_sum, + #end if + exclude_highly_expressed=$method.exclude_highly_expressed.exclude_highly_expressed, + #if $method.exclude_highly_expressed.exclude_highly_expressed == "True" + max_fraction=$method.exclude_highly_expressed.max_fraction, #end if - #if $method.counts_per_cell - counts_per_cell=np.loadtxt('$method.counts_per_cell'), + key_added='$method.key_added', + #if str($method.layers) != 'all' + layers[str(x.strip()) for x in str($method.layers).split(',')], + #else + layers='$method.layers', #end if - key_n_counts='$method.key_n_counts', - copy=False) -adata.obs.to_csv('$anndata_obs', sep='\t') -#elif $method.method == "pp.recipe_zheng17" + #if str($method.layer_norm) != "None" + layer_norm='$method.layer_norm', + #end if + inplace=True) + +#else if $method.method == "pp.recipe_zheng17" sc.pp.recipe_zheng17( adata=adata, n_top_genes=$method.n_top_genes, log=$method.log, plot=False, copy=False) -#elif $method.method == "pp.recipe_weinreb17" + +#else if $method.method == "pp.recipe_weinreb17" sc.pp.recipe_weinreb17( adata=adata, log=$method.log, @@ -42,34 +52,14 @@ svd_solver='$method.svd_solver', random_state=$method.random_state, copy=False) -#elif $method.method == "pp.recipe_seurat" + +#else if $method.method == "pp.recipe_seurat" sc.pp.recipe_seurat( adata=adata, log=$method.log, plot=False, copy=False) -#elif $method.method == "pp.log1p" -sc.pp.log1p( - data=adata, - copy=False) -#elif $method.method == "pp.scale" -sc.pp.scale( - data=adata, - zero_center=$method.zero_center, - #if $method.max_value - max_value=$method.max_value, - #end if - copy=False) -#elif $method.method == "pp.sqrt" -sc.pp.sqrt( - data=adata, - copy=False) -#elif $method.method == "pp.downsample_counts" -sc.pp.downsample_counts( - adata=adata, - target_counts=$method.target_counts, - random_state=$method.random_state, - copy=False) + #end if @CMD_anndata_write_outputs@ @@ -79,20 +69,31 @@ - - + + - - - - - - - - + + + + + + + + + + + + + + + + + + + @@ -109,73 +110,50 @@ - - - - - - - - - - - - - method['method'] == 'pp.normalize_per_cell' - - - - - + + - - - - + + + + + + + - - - - - + + + + - - + - - - - + + - - + - - - - + + @@ -185,7 +163,6 @@ - @@ -195,108 +172,22 @@ - + - - - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + `__ +`__ Normalization and filtering as of Zheng et al. (2017), the Cell Ranger R Kit of 10x Genomics (`pp.recipe_zheng17`) @@ -327,7 +218,7 @@ - scale to unit variance and shift to zero mean More details on the `scanpy documentation -`__ +`__ Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`) @@ -336,7 +227,7 @@ Expects non-logarithmized data. If using logarithmized data, pass `log=False`. More details on the `scanpy documentation -`__ +`__ Normalization and filtering as of Seurat et al (2015) (`pp.recipe_seurat`) @@ -347,33 +238,7 @@ Expects non-logarithmized data. If using logarithmized data, pass `log=False`. More details on the `scanpy documentation -`__ - -Logarithmize the data matrix (`pp.log1p`) -========================================= - -More details on the `scanpy documentation -`__ - -Scale data to unit variance and zero mean (`pp.scale`) -====================================================== - -More details on the `scanpy documentation -`__ - -Computes the square root the data matrix (`pp.sqrt`) -==================================================== - -`X = sqrt(X)` - -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 -`__ +`__ ]]> diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/blobs.h5ad Binary file test-data/blobs.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.clustermap.krumsiek11.png Binary file test-data/pl.clustermap.krumsiek11.png has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.dotplot.krumsiek11.png Binary file test-data/pl.dotplot.krumsiek11.png has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.draw_graph.png Binary file test-data/pl.draw_graph.png has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.highly_variable_genes.seurat.blobs.png Binary file test-data/pl.highly_variable_genes.seurat.blobs.png has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.pca_overview.pp.pca.krumsiek11.png Binary file test-data/pl.pca_overview.pp.pca.krumsiek11.png has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png Binary file 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test-data/pp.filter_cells.krumsiek11-min_counts.h5ad Binary file test-data/pp.filter_cells.krumsiek11-min_counts.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 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 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ - cell_subset number_per_cell -0 True 9 -1 True 9 -2 True 9 -3 True 8 -4 True 8 -5 True 8 -6 True 8 -7 True 7 -8 True 8 -9 True 8 -10 True 7 -11 True 7 -12 True 7 -13 True 7 -14 True 8 -15 True 10 -16 True 10 -17 True 10 -18 True 11 -19 True 11 -20 True 11 -21 True 11 -22 True 11 -23 True 11 -24 True 11 -25 True 11 -26 True 11 -27 True 11 -28 True 11 -29 True 11 -30 True 11 -31 True 11 -32 True 11 -33 True 11 -34 True 11 -35 True 11 -36 True 11 -37 True 11 -38 True 11 -39 True 11 -40 True 11 -41 True 11 -42 True 11 -43 True 11 -44 True 11 -45 True 11 -46 True 11 -47 True 11 -48 True 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-cJun 188.10158 -EgrNab 164.99693 -Gfi1 159.99155 diff -r ed64c90a9b93 -r a9f14e2d1655 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 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,222 +0,0 @@ - gene_subset number_per_gene -0 True 34 -1 True 123 -2 True 281 -3 True 54 -4 True 253 -5 True 63 -6 True 9 -7 True 266 -8 True 101 -9 True 233 -10 True 267 -11 True 285 -12 True 332 -13 True 197 -14 True 158 -15 True 64 -16 True 285 -17 True 229 -18 True 43 -19 True 199 -20 True 271 -21 True 318 -22 True 132 -23 True 83 -24 True 88 -25 True 87 -26 True 71 -27 True 258 -28 True 58 -29 True 348 -30 True 280 -31 True 150 -32 True 121 -33 True 237 -34 True 29 -35 True 220 -36 True 103 -37 True 87 -38 True 115 -39 True 100 -40 True 139 -41 True 23 -42 True 162 -43 True 76 -44 True 180 -45 True 51 -46 True 244 -47 True 132 -48 True 244 -49 True 82 -50 True 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13 -200 True 94 -201 True 204 -202 True 245 -203 True 11 -204 True 238 -205 True 301 -206 True 219 -207 True 106 -208 True 253 -209 True 134 -210 True 262 -211 True 222 -212 True 82 -213 True 153 -214 True 122 -215 True 211 -216 True 49 -217 True 211 -218 True 176 -219 True 329 -220 True 8 diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 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 10:16:12 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 ed64c90a9b93 -r a9f14e2d1655 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 10:16:12 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 ed64c90a9b93 -r a9f14e2d1655 test-data/pp.filter_rank_genes_groups.h5ad Binary file test-data/pp.filter_rank_genes_groups.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 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-1.478822380304336548e-01 -9.886784851551055908e-02 -9.984883666038513184e-02 -6.099858880043029785e-02 8.420736342668533325e-02 -9.065923094749450684e-02 diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 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 ed64c90a9b93 -r a9f14e2d1655 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 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -index paul15_clusters dpt_groups dpt_order dpt_order_indices -578 13Baso 2 53 27 -2242 3Ery 1 30 46 -2690 10GMP 2 66 45 -70 5Ery 1 32 65 -758 15Mo 2 67 8 -465 16Neu 2 68 80 -245 16Neu 2 69 87 -2172 10GMP 2 70 90 -2680 10GMP 0 4 36 -1790 7MEP 2 71 59 -855 11DC 2 72 82 -2721 10GMP 2 73 30 -104 2Ery 1 38 62 -1106 2Ery 1 40 32 -2367 15Mo 3 93 35 -124 2Ery 1 41 37 -2477 8Mk 2 74 31 -1968 2Ery 1 42 78 -563 1Ery 1 43 28 -276 2Ery 1 44 56 -192 16Neu 2 75 42 -2409 2Ery 1 45 44 -2054 15Mo 3 95 75 -720 8Mk 2 76 48 -2225 14Mo 3 97 98 -878 6Ery 1 29 54 -156 7MEP 2 77 79 -1244 8Mk 0 0 40 -10 2Ery 1 18 83 -1108 6Ery 2 65 25 -353 5Ery 1 11 1 -182 5Ery 1 16 97 -2053 3Ery 1 13 3 -2291 16Neu 3 92 96 -2056 10GMP 2 79 95 -1047 2Ery 1 14 94 -1947 14Mo 0 8 92 -1390 3Ery 1 15 60 -2317 14Mo 2 90 12 -2348 11DC 2 82 69 -953 5Ery 1 27 13 -628 9GMP 2 83 15 -2691 5Ery 1 20 17 -1499 16Neu 3 96 18 -1083 2Ery 1 21 19 -831 14Mo 0 2 21 -15 7MEP 0 1 86 -2005 7MEP 2 87 66 -1662 3Ery 1 23 84 -2457 7MEP 2 64 89 -757 7MEP 2 81 70 -1642 14Mo 2 91 68 -2520 10GMP 2 89 67 -1393 7MEP 2 88 0 -2170 6Ery 1 25 73 -988 14Mo 2 86 76 -1338 2Ery 1 19 77 -2189 16Neu 2 85 81 -446 13Baso 2 84 85 -2276 14Mo 0 9 88 -317 2Ery 1 37 91 -1540 16Neu 3 99 93 -2164 4Ery 1 12 72 -227 15Mo 2 78 64 -906 12Baso 2 63 49 -716 15Mo 0 3 29 -912 14Mo 1 47 2 -2688 11DC 2 52 4 -1678 7MEP 2 51 5 -1063 6Ery 1 39 6 -1041 5Ery 1 50 7 -2279 15Mo 3 98 9 -558 13Baso 2 62 10 -2196 14Mo 2 54 11 -1270 13Baso 3 94 16 -2259 3Ery 1 22 20 -2410 13Baso 2 55 23 -886 7MEP 2 56 26 -2072 13Baso 1 17 63 -443 5Ery 1 26 34 -910 13Baso 0 5 99 -2608 15Mo 2 57 50 -2645 1Ery 1 10 39 -616 6Ery 1 28 41 -1866 2Ery 1 48 58 -923 7MEP 2 58 57 -1716 4Ery 1 46 55 -2476 11DC 0 6 47 -1872 10GMP 2 59 53 -1009 4Ery 1 49 52 -1680 6Ery 0 7 38 -1490 14Mo 2 60 51 -1454 2Ery 1 36 33 -2580 9GMP 2 61 14 -958 1Ery 1 35 74 -2626 2Ery 1 34 22 -1677 3Ery 1 33 43 -982 4Ery 1 31 24 -202 2Ery 1 24 71 -891 10GMP 2 80 61 diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.pca.krumsiek11.h5ad Binary file test-data/tl.pca.krumsiek11.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.rank_genes_groups.krumsiek11.h5ad Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad Binary file test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 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 ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes.krumsiek11.h5ad Binary file test-data/tl.score_genes.krumsiek11.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes.krumsiek11.obs.tabular --- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:16:12 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 ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed diff -r ed64c90a9b93 -r a9f14e2d1655 test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular --- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:16:12 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 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