# HG changeset patch
# User iuc
# Date 1571221737 14400
# Node ID e4c0f5ee8e17adbde9167563840dae70b31ab258
# Parent 397d2c97af057eab99f17a86ff9feea3a06aaa74
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
diff -r 397d2c97af05 -r e4c0f5ee8e17 README.md
--- a/README.md Mon Mar 04 10:14:25 2019 -0500
+++ b/README.md Wed Oct 16 06:28:57 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 397d2c97af05 -r e4c0f5ee8e17 README.rst
--- a/README.rst Mon Mar 04 10:14:25 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 397d2c97af05 -r e4c0f5ee8e17 macros.xml
--- a/macros.xml Mon Mar 04 10:14:25 2019 -0500
+++ b/macros.xml Wed Oct 16 06:28:57 2019 -0400
@@ -1,10 +1,12 @@
- 1.4
+ 1.4.4
scanpy
loompy
+ h5py
+ leidenalg
@@ -14,102 +16,33 @@
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- anndata_output_format == 'h5ad'
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- anndata_output_format == 'loom'
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- modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad'
-
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- modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom'
-
+
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@@ -423,7 +356,7 @@
-
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@@ -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'
]]>
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@@ -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 @@
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#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 @@
+
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diff -r 397d2c97af05 -r e4c0f5ee8e17 plot.xml
--- a/plot.xml Mon Mar 04 10:14:25 2019 -0500
+++ b/plot.xml Wed Oct 16 06:28:57 2019 -0400
@@ -1,5 +1,5 @@
-
-
+
+ with scanpy
macros.xml
@@ -25,14 +25,14 @@
#if $method.type.type == 'xy'
x='$method.type.x',
y='$method.type.y',
- layers=('$method.type.layer_x', '$method.type.layer_y', '$method.type.layer_color'),
+ #if str('$method.type.layers.use_layers') == 'true'
+ layers=('$method.type.layers.layer_x', '$method.type.layers.layer_y', '$method.type.layers.layer_color'),
+ #end if
#else
basis='$method.type.basis',
#if str($method.type.color) != ''
#set $color = ([x.strip() for x in str($method.type.color).split(',')])
color=$color,
- #else
- color=None,
#end if
#end if
use_raw=$method.use_raw,
@@ -51,14 +51,16 @@
#end if
size=$method.plot.size)
-#elif $method.method == 'pl.heatmap'
+#else if $method.method == 'pl.heatmap'
sc.pl.heatmap(
@CMD_param_plot_inputs@
@CMD_params_plots@
swap_axes=$method.swap_axes,
show_gene_labels=$method.show_gene_labels,
cmap='$method.matplotlib_pyplot_imshow.cmap',
+ #if str($method.matplotlib_pyplot_imshow.interpolation) != 'None'
interpolation='$method.matplotlib_pyplot_imshow.interpolation',
+ #end if
#if $method.matplotlib_pyplot_imshow.alpha
alpha=$method.matplotlib_pyplot_imshow.alpha,
#end if
@@ -70,7 +72,7 @@
#end if
origin='$method.matplotlib_pyplot_imshow.origin')
-#elif $method.method == 'pl.dotplot'
+#else if $method.method == 'pl.dotplot'
sc.pl.dotplot(
@CMD_param_plot_inputs@
@CMD_params_plots@
@@ -83,14 +85,14 @@
#end if
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.violin'
+#else if $method.method == 'pl.violin'
sc.pl.violin(
@CMD_param_plot_inputs@
#if $method.key_variables.type == "var_names"
#set $key_list = adata.var_names
- #elif $method.key_variables.type == "obs"
+ #else if $method.key_variables.type == "obs"
#set $key_list = adata.obs.select_dtypes(exclude=['category']).columns
- #elif $method.key_variables.type == "custom"
+ #else if $method.key_variables.type == "custom"
#set $key_list=([x.strip() for x in str($method.key_variables.keys).split(',')])
#end if
keys=$key_list,
@@ -99,14 +101,11 @@
#end if
log=$method.log,
use_raw=$method.use_raw,
- sstripplot=$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
+ @CMD_conditional_stripplot@
+ multi_panel=$method.violin_plot.multi_panel.multi_panel,
+ #if $method.violin_plot.multi_panel.multi_panel == "True" and str($method.violin_plot.multi_panel.width) != '' and str($method.violin_plot.multi_panel.height) != ''
+ figsize=($method.violin_plot.multi_panel.width, $method.violin_plot.multi_panel.height),
#end if
- multi_panel=$method.violin_plot.multi_panel.multi_panel,
scale='$method.violin_plot.scale',
#if $method.xlabel
xlabel='$method.xlabel',
@@ -116,7 +115,20 @@
#end if
@CMD_params_seaborn_violinplot@)
-#elif $method.method == 'pl.matrixplot'
+#else if $method.method == 'pl.stacked_violin'
+sc.pl.stacked_violin(
+ @CMD_param_plot_inputs@
+ @CMD_params_plots@
+ swap_axes=$method.swap_axes,
+ @CMD_conditional_stripplot@
+ scale='$method.violin_plot.scale',
+ row_palette='$method.row_palette',
+ #if str($method.standard_scale) != 'None'
+ standard_scale='$method.standard_scale',
+ #end if
+ @CMD_params_seaborn_violinplot@)
+
+#else if $method.method == 'pl.matrixplot'
sc.pl.matrixplot(
@CMD_param_plot_inputs@
@CMD_params_plots@
@@ -134,7 +146,7 @@
#end if
snap=$method.matplotlib_pyplot_pcolor.snap)
-#elif $method.method == 'pl.clustermap'
+#else if $method.method == 'pl.clustermap'
sc.pl.clustermap(
#if str($method.use_raw) == 'True'
adata=adata.X,
@@ -142,24 +154,22 @@
adata=adata,
#end if
show=False,
+ #if str($method.obs_keys) != ''
obs_keys='$method.obs_keys',
+ #end if
use_raw=$method.use_raw,
method='$method.seaborn_clustermap.method',
metric='$method.seaborn_clustermap.metric',
- #if str($method.seaborn_clustermap.z_score) == 'None'
- z_score=None,
- #else
+ #if str($method.seaborn_clustermap.z_score) != 'None'
z_score=$method.seaborn_clustermap.z_score,
#end if
- #if str($method.seaborn_clustermap.standard_scale) == 'None'
- standard_scale=None,
- #else
+ #if str($method.seaborn_clustermap.standard_scale) != 'None'
standard_scale=$method.seaborn_clustermap.standard_scale,
#end if
col_cluster=$method.seaborn_clustermap.col_cluster,
row_cluster=$method.seaborn_clustermap.row_cluster).savefig(fname="output.$format")
-#elif $method.method == 'pl.highest_expr_genes'
+#else if $method.method == 'pl.highest_expr_genes'
sc.pl.highest_expr_genes(
@CMD_param_plot_inputs@
n_top=$method.n_top,
@@ -174,7 +184,16 @@
#end if
saturation=$method.setseaborn_boxplot.saturation)
-#elif $method.method == 'pl.pca'
+#else if $method.method == 'pl.highly_variable_genes'
+sc.pl.highly_variable_genes(
+ adata_or_result=adata,
+ log=$method.log,
+ save='.$format',
+ show=False,
+ highly_variable_genes=$method.highly_variable_genes
+ )
+
+#else if $method.method == 'pl.pca'
sc.pl.pca(
@CMD_param_plot_inputs@
@CMD_param_color@
@@ -185,19 +204,19 @@
@CMD_pl_attribute_section@
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.pca_loadings'
+#else if $method.method == 'pl.pca_loadings'
sc.pl.pca_loadings(
@CMD_param_plot_inputs@
#set $components = ([int(x.strip()) for x in str($method.components).split(',')])
components=$components)
-#elif $method.method == 'pl.pca_variance_ratio'
+#else if $method.method == 'pl.pca_variance_ratio'
sc.pl.pca_variance_ratio(
@CMD_param_plot_inputs@
n_pcs=$method.n_pcs,
log=$method.log)
-#elif $method.method == 'pl.pca_overview'
+#else if $method.method == 'pl.pca_overview'
sc.pl.pca_overview(
@CMD_param_plot_inputs@
@CMD_param_color@
@@ -208,7 +227,7 @@
@CMD_pl_attribute_section@
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.tsne'
+#else if $method.method == 'pl.tsne'
sc.pl.tsne(
@CMD_param_plot_inputs@
@CMD_param_color@
@@ -221,7 +240,7 @@
@CMD_pl_attribute_section@
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.umap'
+#else if $method.method == 'pl.umap'
sc.pl.umap(
@CMD_param_plot_inputs@
@CMD_param_color@
@@ -234,7 +253,7 @@
@CMD_pl_attribute_section@
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.diffmap'
+#else if $method.method == 'pl.diffmap'
sc.pl.diffmap(
@CMD_param_plot_inputs@
@CMD_param_color@
@@ -245,12 +264,35 @@
@CMD_pl_attribute_section@
@CMD_params_matplotlib_pyplot_scatter@)
-#elif $method.method == 'pl.dpt_groups_pseudotime'
+#else if $method.method == 'pl.draw_graph'
+sc.pl.draw_graph(
+ @CMD_param_plot_inputs@
+ @CMD_param_color@
+ #if str($method.gene_symbols) != ''
+ gene_symbols='$method.gene_symbols',
+ #end if
+ use_raw=$method.use_raw,
+ #if str($method.layout) != 'None'
+ layout='$method.layout',
+ #end if
+ edges=$method.edges.edges,
+ #if str($method.edges.edges) == 'True'
+ edges_width=$method.edges.edges_width,
+ edges_color='$method.edges.edges_color',
+ #end if
+ arrows=$method.arrows,
+ sort_order=$method.sort_order,
+ @CMD_params_pl_groups@
+ @CMD_params_pl_components@
+ @CMD_pl_attribute_section@
+ @CMD_params_matplotlib_pyplot_scatter@)
+
+#else if $method.method == 'pl.dpt_groups_pseudotime'
sc.pl.dpt_groups_pseudotime(
@CMD_param_plot_inputs@
color_map='$method.color_map')
-#elif $method.method == 'pl.dpt_timeseries'
+#else if $method.method == 'pl.dpt_timeseries'
sc.pl.dpt_timeseries(
@CMD_param_plot_inputs@
#if $method.heatmap.as_heatmap == "True"
@@ -258,10 +300,73 @@
#end if
as_heatmap=$method.heatmap.as_heatmap)
-#elif $method.method == 'pl.paga'
+#else if $method.method == 'pl.paga'
sc.pl.paga(
@CMD_param_plot_inputs@
@CMD_params_pl_paga@)
+
+#else if $method.method == 'pl.paga_compare'
+sc.pl.paga_compare(
+ @CMD_param_plot_inputs@
+ @CMD_params_pl_paga@)
+
+#else if $method.method == 'pl.paga_path'
+sc.pl.paga_path(
+ @CMD_param_plot_inputs@
+ #set $nodes = ([int(x.strip()) for x in str($method.nodes).split(',')])
+ nodes=$nodes,
+ #set $keys=([x.strip() for x in str($method.keys).split(',')])
+ keys=$keys,
+ use_raw=$method.use_raw,
+ #if str($method.annotations) != ''
+ #set $annotations=([x.strip() for x in str($method.annotations).split(',')])
+ annotations=$annotations,
+ #end if
+ color_map='$method.color_map',
+ n_avg=$method.n_avg,
+ #if str($method.annotations) != ''
+ groups_key='$method.groups_key',
+ #end if
+ as_heatmap=$method.as_heatmap,
+ show_node_names=$method.show_node_names,
+ show_colorbar=$method.show_colorbar,
+ show_yticks=$method.show_yticks,
+ normalize_to_zero_one=$method.normalize_to_zero_one)
+
+#else if $method.method == 'pl.rank_genes_groups'
+sc.pl.rank_genes_groups(
+ @CMD_param_plot_inputs@
+ @CMD_params_pl_groups@
+ n_genes=$method.n_genes,
+ #if str($method.gene_symbols) != ''
+ gene_symbols='$method.gene_symbols',
+ #end if
+ fontsize=$method.fontsize,
+ ncols=$method.ncols,
+ sharey=$method.sharey)
+
+#else if $method.method == 'pl.rank_genes_groups_violin'
+sc.pl.rank_genes_groups_violin(
+ @CMD_param_plot_inputs@
+ @CMD_params_pl_groups@
+ #if $method.genes.select == 'n_genes'
+ n_genes=$method.genes.n_genes,
+ #else
+ #set $gene_names = ([x.strip() for x in str($method.genes.gene_names).split(',')])
+ gene_names=$gene_names,
+ #end if
+ gene_symbols='$method.gene_symbols',
+ use_raw=$method.use_raw,
+ split=$method.split,
+ strip=$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
+ scale='$method.violin_plot.scale')
+
#end if
]]>
@@ -274,13 +379,13 @@
-
+
-
+
@@ -289,18 +394,18 @@
-
+
-
-
-
+
+
+
-
+
@@ -317,9 +422,18 @@
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
@@ -355,7 +469,7 @@
-
+
@@ -396,7 +510,7 @@
-
+
@@ -407,11 +521,43 @@
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -427,7 +573,7 @@
-
+
+
+
+
+
@@ -518,6 +668,20 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -536,35 +700,92 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
- format == 'png'
+
+ format == 'png' and method['method'] != 'pl.rank_genes_groups_violin'
+
+
+
+ format == 'png' and method['method'] == 'pl.rank_genes_groups_violin'
+
+
+ format == 'pdf' and method['method'] != pl.rank_genes_groups_violin
-
- format == 'pdf'
+
+
+ format == 'pdf' and method['method'] == 'pl.rank_genes_groups_violin'
+
+
+ format == 'svg' and method['method'] != pl.rank_genes_groups_violin
-
- format == 'svg'
-
+
+
+ format == 'svg' and method['method'] == 'pl.rank_genes_groups_violin'
+
-
-
-
-
-
+
+
-
-
+
+
-
+
-
-
-
+
+
+
-
-
@@ -594,14 +813,11 @@
-
+
-
-
-
-
-
+
+
@@ -609,9 +825,9 @@
-
-
-
+
+
+
@@ -630,11 +846,8 @@
-
-
-
@@ -648,11 +861,8 @@
-
-
-
-
-
+
+
@@ -673,7 +883,7 @@
@@ -685,23 +895,17 @@
-
-
-
-
-
-
-
-
+
+
@@ -714,10 +918,6 @@
-
-
-
-
@@ -728,6 +928,9 @@
+
+
+
@@ -743,7 +946,7 @@
-
+
@@ -755,11 +958,8 @@
-
-
-
-
-
+
+
@@ -795,10 +995,11 @@
-
+
+
@@ -810,11 +1011,65 @@
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -844,8 +1099,6 @@
-
-
@@ -854,19 +1107,15 @@
-
-
-
-
-
+
+
-
-
+
@@ -876,23 +1125,17 @@
-
-
-
-
+
-
-
-
-
-
+
+
@@ -913,11 +1156,25 @@
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -955,7 +1212,6 @@
-
@@ -973,11 +1229,8 @@
-
-
-
-
-
+
+
@@ -987,14 +1240,11 @@
-
+
-
-
-
-
-
+
+
@@ -1006,22 +1256,18 @@
-
+
-
-
-
-
-
+
+
-
-
+
@@ -1032,22 +1278,12 @@
-
-
-
-
-
+
@@ -1057,19 +1293,12 @@
-
-
-
-
-
-
-
-
-
+
+
@@ -1098,13 +1327,10 @@
-
-
-
@@ -1121,11 +1347,8 @@
-
-
-
-
-
+
+
@@ -1163,8 +1386,6 @@
-
-
@@ -1181,11 +1402,8 @@
-
-
-
-
-
+
+
@@ -1214,8 +1432,6 @@
-
-
@@ -1231,12 +1447,59 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
-
-
-
-
+
+
@@ -1271,10 +1531,7 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
`__
+
+Generic: Stacked violin plots (`pl.stacked_violin`)
+===================================================
+
+Makes a compact image composed of individual violin plots (from `seaborn.violinplot`)
+stacked on top of each other. Useful to visualize gene expression per cluster.
+
+More details on the `scanpy documentation
`__
Generic: Heatmap of the mean expression values per cluster (`pl.matrixplot`)
@@ -1353,7 +1684,7 @@
category.
More details on the `scanpy documentation
-`__
+`__
Generic: Hierarchically-clustered heatmap (`pl.clustermap`)
===========================================================
@@ -1371,7 +1702,7 @@
Column indices, use: clustergrid.dendrogram_col.reordered_ind
More details on the `scanpy documentation
-`__
+`__
Preprocessing: Plot the fraction of counts assigned to each gene over all cells (`pl.highest_expr_genes`)
=========================================================================================================
@@ -1380,30 +1711,36 @@
a cell. The `n_top` genes with the highest mean fraction over all cells are
plotted as boxplots.
-This plot is similar to the `scater` package function `plotHighestExprs(type= "highest-expression")`, see `here
-`__.
--- Davis McCarthy and Aaron Lun
+This plot is similar to the `scater` package function `plotHighestExprs(type= "highest-expression")`
More details on the `scanpy documentation
-`__
+`__
+
+Preprocessing: Plot dispersions versus means for genes (`pl.highly_variable_genes`)
+===================================================================================
+
+It produces Supp. Fig. 5c of Zheng et al. (2017) and MeanVarPlot() of Seurat.
+
+More details on the `scanpy documentation
+`__
PCA: Scatter plot in PCA coordinates (`pl.pca`)
===============================================
More details on the `scanpy documentation
-`__
+`__
PCA: Rank genes according to contributions to PCs (`pl.pca_loadings`)
=====================================================================
More details on the `scanpy documentation
-`__
+`__
PCA: Plot the variance ratio (`pl.pca_variance_ratio`)
======================================================
More details on the `scanpy documentation
-`__
+`__
PCA: Plot PCA results (`pl.pca_overview`)
=========================================
@@ -1412,37 +1749,37 @@
if you want to change the default settings.
More details on the `scanpy documentation
-`__
+`__
Embedding: Scatter plot in tSNE basis (`pl.tsne`)
=================================================
More details on the `scanpy documentation
-`__
+`__
Embeddings: Scatter plot in UMAP basis (`pl.umap`)
==================================================
More details on the `scanpy documentation
-`__
+`__
Embeddings: Scatter plot in Diffusion Map basis (`pl.diffmap`)
==============================================================
More details on the `scanpy documentation
-`__
+`__
Branching trajectories and pseudotime, clustering: Plot groups and pseudotime (`pl.dpt_groups_pseudotime`)
===========================================================================================================
More details on the `scanpy documentation
-`__
+`__
Branching trajectories and pseudotime, clustering: Heatmap of pseudotime series (`pl.dpt_timeseries`)
=====================================================================================================
More details on the `scanpy documentation
-`__
+`__
Branching trajectories and pseudotime, clustering: Plot the abstracted graph through thresholding low-connectivity edges (`pl.paga`)
@@ -1455,9 +1792,32 @@
`maxiter` parameter by 1 if the layout is flipped.
More details on the `scanpy documentation
-`__
+`__
+Branching trajectories and pseudotime, clustering: Scatter and PAGA graph side-by-side (`pl.paga_compare`)
+==========================================================================================================
+
+More details on the `scanpy documentation
+`__
+
+Branching trajectories and pseudotime, clustering: Gene expression and annotation changes along paths (`pl.paga_path`)
+======================================================================================================================
+
+More details on the `scanpy documentation
+`__
+
+Marker genes: Plot ranking of genes using dotplot plot (`pl.rank_genes_groups`)
+===============================================================================
+
+More details on the `scanpy documentation
+`__
+
+Marker genes: Plot ranking of genes for all tested comparisons (`pl.rank_genes_groups_violin`)
+==============================================================================================
+
+More details on the `scanpy documentation
+`__
]]>
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diff -r 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,641 +0,0 @@
- cell_subset number_per_cell
-0 True 9
-1 True 9
-2 True 9
-3 True 8
-4 True 8
-5 True 8
-6 True 8
-7 True 7
-8 True 8
-9 True 8
-10 True 7
-11 True 7
-12 True 7
-13 True 7
-14 True 8
-15 True 10
-16 True 10
-17 True 10
-18 True 11
-19 True 11
-20 True 11
-21 True 11
-22 True 11
-23 True 11
-24 True 11
-25 True 11
-26 True 11
-27 True 11
-28 True 11
-29 True 11
-30 True 11
-31 True 11
-32 True 11
-33 True 11
-34 True 11
-35 True 11
-36 True 11
-37 True 11
-38 True 11
-39 True 11
-40 True 11
-41 True 11
-42 True 11
-43 True 11
-44 True 11
-45 True 11
-46 True 11
-47 True 11
-48 True 10
-49 True 10
-50 True 10
-51 True 10
-52 True 10
-53 True 10
-54 True 10
-55 True 10
-56 True 11
-57 True 11
-58 True 11
-59 True 10
-60 True 10
-61 True 11
-62 True 10
-63 True 11
-64 True 10
-65 True 10
-66 True 11
-67 True 11
-68 True 11
-69 True 10
-70 True 10
-71 True 10
-72 True 10
-73 True 10
-74 True 11
-75 True 10
-76 True 10
-77 True 10
-78 True 9
-79 True 10
-80 True 9
-81 True 9
-82 True 10
-83 True 9
-84 True 9
-85 True 9
-86 True 9
-87 True 9
-88 True 9
-89 True 9
-90 True 9
-91 True 10
-92 True 10
-93 True 9
-94 True 9
-95 True 9
-96 True 9
-97 True 7
-98 True 7
-99 True 6
-100 True 6
-101 True 7
-102 True 8
-103 True 8
-104 True 8
-105 True 8
-106 True 9
-107 True 9
-108 True 9
-109 True 8
-110 True 8
-111 True 10
-112 True 9
-113 True 8
-114 True 9
-115 True 10
-116 True 9
-117 True 8
-118 True 7
-119 True 7
-120 True 7
-121 True 7
-122 True 9
-123 True 9
-124 True 9
-125 True 8
-126 True 7
-127 True 6
-128 True 6
-129 True 8
-130 True 8
-131 True 8
-132 True 8
-133 True 10
-134 True 10
-135 True 8
-136 True 6
-137 True 6
-138 True 8
-139 True 9
-140 True 8
-141 True 7
-142 True 7
-143 True 8
-144 True 7
-145 True 7
-146 True 7
-147 True 5
-148 True 6
-149 True 8
-150 True 9
-151 True 6
-152 True 6
-153 True 6
-154 True 7
-155 True 8
-156 True 7
-157 True 7
-158 True 7
-159 True 8
-160 True 9
-161 True 8
-162 True 8
-163 True 9
-164 True 9
-165 True 9
-166 True 8
-167 True 8
-168 True 9
-169 True 9
-170 True 8
-171 True 9
-172 True 9
-173 True 10
-174 True 10
-175 True 10
-176 True 10
-177 True 10
-178 True 10
-179 True 10
-180 True 10
-181 True 10
-182 True 10
-183 True 10
-184 True 10
-185 True 10
-186 True 10
-187 True 10
-188 True 11
-189 True 11
-190 True 11
-191 True 11
-192 True 11
-193 True 11
-194 True 11
-195 True 11
-196 True 11
-197 True 11
-198 True 11
-199 True 11
-200 True 11
-201 True 11
-202 True 11
-203 True 11
-204 True 11
-205 True 11
-206 True 11
-207 True 11
-208 True 11
-209 True 11
-210 True 11
-211 True 11
-212 True 11
-213 True 11
-214 True 11
-215 True 11
-216 True 11
-217 True 11
-218 True 11
-219 True 11
-220 True 11
-221 True 11
-222 True 11
-223 True 11
-224 True 11
-225 True 11
-226 True 11
-227 True 11
-228 True 11
-229 True 11
-230 True 11
-231 True 11
-232 True 11
-233 True 11
-234 True 11
-235 True 11
-236 True 11
-237 True 11
-238 True 11
-239 True 11
-240 True 11
-241 True 11
-242 True 11
-243 True 11
-244 True 11
-245 True 11
-246 True 11
-247 True 11
-248 True 11
-249 True 11
-250 True 11
-251 True 11
-252 True 11
-253 True 11
-254 True 11
-255 True 11
-256 True 11
-257 True 11
-258 True 11
-259 True 11
-260 True 11
-261 True 11
-262 True 11
-263 True 11
-264 True 11
-265 True 11
-266 True 11
-267 True 11
-268 True 11
-269 True 11
-270 True 11
-271 True 11
-272 True 10
-273 True 11
-274 True 11
-275 True 11
-276 True 11
-277 True 11
-278 True 11
-279 True 10
-280 True 11
-281 True 9
-282 True 9
-283 True 8
-284 True 9
-285 True 9
-286 True 10
-287 True 9
-288 True 9
-289 True 9
-290 True 9
-291 True 9
-292 True 9
-293 True 10
-294 True 10
-295 True 10
-296 True 10
-297 True 9
-298 True 10
-299 True 9
-300 True 8
-301 True 8
-302 True 8
-303 True 8
-304 True 8
-305 True 9
-306 True 8
-307 True 8
-308 True 8
-309 True 8
-310 True 9
-311 True 8
-312 True 9
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-314 True 10
-315 True 10
-316 True 10
-317 True 10
-318 True 10
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-320 True 4
-321 True 8
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-329 True 9
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-331 True 9
-332 True 8
-333 True 8
-334 True 8
-335 True 10
-336 True 10
-337 True 9
-338 True 10
-339 True 10
-340 True 10
-341 True 10
-342 True 10
-343 True 10
-344 True 10
-345 True 11
-346 True 11
-347 True 11
-348 True 11
-349 True 11
-350 True 11
-351 True 11
-352 True 11
-353 True 11
-354 True 11
-355 True 11
-356 True 11
-357 True 11
-358 True 11
-359 True 11
-360 True 11
-361 True 11
-362 True 11
-363 True 11
-364 True 11
-365 True 11
-366 True 11
-367 True 11
-368 True 11
-369 True 11
-370 True 11
-371 True 11
-372 True 11
-373 True 11
-374 True 11
-375 True 11
-376 True 11
-377 True 11
-378 True 11
-379 True 11
-380 True 11
-381 True 11
-382 True 11
-383 True 11
-384 True 11
-385 True 11
-386 True 11
-387 True 11
-388 True 11
-389 True 11
-390 True 11
-391 True 11
-392 True 11
-393 True 11
-394 True 11
-395 True 11
-396 True 11
-397 True 11
-398 True 11
-399 True 11
-400 True 11
-401 True 11
-402 True 11
-403 True 11
-404 True 11
-405 True 11
-406 True 11
-407 True 11
-408 True 11
-409 True 11
-410 True 11
-411 True 11
-412 True 11
-413 True 11
-414 True 11
-415 True 11
-416 True 11
-417 True 11
-418 True 11
-419 True 11
-420 True 11
-421 True 11
-422 True 11
-423 True 11
-424 True 11
-425 True 11
-426 True 11
-427 True 11
-428 True 11
-429 True 11
-430 True 11
-431 True 11
-432 True 11
-433 True 11
-434 True 11
-435 True 11
-436 True 11
-437 True 11
-438 True 11
-439 True 11
-440 True 11
-441 True 11
-442 True 11
-443 True 11
-444 True 11
-445 True 11
-446 True 10
-447 True 10
-448 True 10
-449 True 10
-450 True 10
-451 True 11
-452 True 11
-453 True 11
-454 True 10
-455 True 10
-456 True 11
-457 True 10
-458 True 10
-459 True 11
-460 True 11
-461 True 10
-462 True 9
-463 True 10
-464 True 10
-465 True 10
-466 True 10
-467 True 9
-468 True 10
-469 True 10
-470 True 11
-471 True 11
-472 True 9
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-474 True 9
-475 True 9
-476 True 9
-477 True 10
-478 True 10
-479 True 9
-480 True 8
-481 True 10
-482 True 10
-483 True 10
-484 True 8
-485 True 8
-486 True 9
-487 True 8
-488 True 9
-489 True 10
-490 True 11
-491 True 11
-492 True 11
-493 True 9
-494 True 10
-495 True 10
-496 True 10
-497 True 10
-498 True 11
-499 True 11
-500 True 11
-501 True 11
-502 True 11
-503 True 11
-504 True 11
-505 True 11
-506 True 11
-507 True 11
-508 True 11
-509 True 11
-510 True 11
-511 True 11
-512 True 11
-513 True 11
-514 True 11
-515 True 11
-516 True 11
-517 True 11
-518 True 11
-519 True 11
-520 True 11
-521 True 11
-522 True 11
-523 True 11
-524 True 11
-525 True 11
-526 True 11
-527 True 11
-528 True 11
-529 True 11
-530 True 11
-531 True 11
-532 True 11
-533 True 11
-534 True 11
-535 True 11
-536 True 10
-537 True 10
-538 True 10
-539 True 10
-540 True 10
-541 True 10
-542 True 11
-543 True 11
-544 True 11
-545 True 11
-546 True 11
-547 True 10
-548 True 9
-549 True 9
-550 True 10
-551 True 11
-552 True 10
-553 True 9
-554 True 9
-555 True 9
-556 True 8
-557 True 9
-558 True 7
-559 True 8
-560 True 8
-561 True 10
-562 True 9
-563 True 8
-564 True 8
-565 True 8
-566 True 8
-567 True 8
-568 True 6
-569 True 6
-570 True 6
-571 True 6
-572 True 8
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-574 True 7
-575 True 9
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-577 True 7
-578 True 8
-579 True 8
-580 True 6
-581 True 7
-582 True 7
-583 True 8
-584 True 6
-585 True 5
-586 True 5
-587 True 5
-588 True 6
-589 True 7
-590 True 6
-591 True 8
-592 True 7
-593 True 7
-594 True 8
-595 True 7
-596 True 7
-597 True 8
-598 True 5
-599 True 4
-600 True 5
-601 True 6
-602 True 5
-603 True 6
-604 True 7
-605 True 7
-606 True 9
-607 True 10
-608 True 8
-609 True 8
-610 True 10
-611 True 10
-612 True 9
-613 True 8
-614 True 8
-615 True 8
-616 True 7
-617 True 8
-618 True 7
-619 True 6
-620 True 6
-621 True 7
-622 True 7
-623 True 7
-624 True 8
-625 True 6
-626 True 7
-627 True 7
-628 True 7
-629 True 6
-630 True 5
-631 True 7
-632 True 6
-633 True 6
-634 True 7
-635 True 6
-636 True 8
-637 True 8
-638 True 6
-639 True 8
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_genes.krumsiek11-min_counts.h5ad
Binary file test-data/pp.filter_genes.krumsiek11-min_counts.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-index n_counts
-Gata2 163.95355
-Gata1 203.95117
-Fog1 83.94181
-EKLF 70.69286
-Fli1 57.56072
-SCL 202.67444
-Cebpa 469.87094
-Pu.1 250.78569
-cJun 188.10158
-EgrNab 164.99693
-Gfi1 159.99155
diff -r 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,222 +0,0 @@
- gene_subset number_per_gene
-0 True 34
-1 True 123
-2 True 281
-3 True 54
-4 True 253
-5 True 63
-6 True 9
-7 True 266
-8 True 101
-9 True 233
-10 True 267
-11 True 285
-12 True 332
-13 True 197
-14 True 158
-15 True 64
-16 True 285
-17 True 229
-18 True 43
-19 True 199
-20 True 271
-21 True 318
-22 True 132
-23 True 83
-24 True 88
-25 True 87
-26 True 71
-27 True 258
-28 True 58
-29 True 348
-30 True 280
-31 True 150
-32 True 121
-33 True 237
-34 True 29
-35 True 220
-36 True 103
-37 True 87
-38 True 115
-39 True 100
-40 True 139
-41 True 23
-42 True 162
-43 True 76
-44 True 180
-45 True 51
-46 True 244
-47 True 132
-48 True 244
-49 True 82
-50 True 172
-51 True 27
-52 True 100
-53 True 327
-54 True 277
-55 True 282
-56 True 245
-57 True 21
-58 True 52
-59 True 19
-60 True 227
-61 True 288
-62 True 274
-63 True 301
-64 True 316
-65 True 314
-66 True 271
-67 True 270
-68 True 283
-69 True 245
-70 True 263
-71 True 312
-72 True 285
-73 True 228
-74 True 170
-75 True 11
-76 True 228
-77 True 192
-78 True 140
-79 True 15
-80 True 22
-81 True 10
-82 True 233
-83 True 129
-84 True 12
-85 True 297
-86 True 295
-87 True 127
-88 True 208
-89 True 281
-90 True 265
-91 True 254
-92 True 122
-93 True 76
-94 True 237
-95 True 74
-96 True 65
-97 True 45
-98 True 90
-99 True 147
-100 True 189
-101 True 170
-102 True 207
-103 True 14
-104 True 307
-105 True 267
-106 True 111
-107 True 94
-108 True 306
-109 True 126
-110 True 269
-111 True 116
-112 True 140
-113 True 260
-114 True 201
-115 True 198
-116 True 155
-117 True 256
-118 True 214
-119 True 70
-120 True 304
-121 True 336
-122 True 201
-123 True 305
-124 True 301
-125 True 301
-126 True 338
-127 True 81
-128 True 256
-129 True 277
-130 True 237
-131 True 173
-132 True 228
-133 True 64
-134 True 52
-135 True 34
-136 True 333
-137 True 285
-138 True 132
-139 True 32
-140 True 275
-141 True 31
-142 True 244
-143 True 15
-144 True 54
-145 True 289
-146 True 186
-147 True 283
-148 True 333
-149 True 53
-150 True 26
-151 True 173
-152 True 19
-153 True 109
-154 True 138
-155 True 264
-156 True 293
-157 True 225
-158 True 150
-159 True 62
-160 True 350
-161 True 13
-162 True 341
-163 True 223
-164 True 177
-165 True 15
-166 True 202
-167 True 101
-168 True 203
-169 True 271
-170 True 305
-171 True 45
-172 True 322
-173 True 164
-174 True 213
-175 True 55
-176 True 143
-177 True 112
-178 True 266
-179 True 168
-180 True 9
-181 True 300
-182 True 249
-183 True 101
-184 True 55
-185 True 312
-186 True 181
-187 True 256
-188 True 27
-189 True 242
-190 True 210
-191 True 12
-192 True 203
-193 True 41
-194 True 205
-195 True 315
-196 True 94
-197 True 262
-198 True 316
-199 True 13
-200 True 94
-201 True 204
-202 True 245
-203 True 11
-204 True 238
-205 True 301
-206 True 219
-207 True 106
-208 True 253
-209 True 134
-210 True 262
-211 True 222
-212 True 82
-213 True 153
-214 True 122
-215 True 211
-216 True 49
-217 True 211
-218 True 176
-219 True 329
-220 True 8
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad
Binary file test-data/pp.filter_genes_dispersion.krumsiek11-seurat.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 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 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 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 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.filter_rank_genes_groups.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.highly_variable_genes.seurat.blobs.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.log1p.krumsiek11.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/pp.normalize_per_cell.obs.krumsiek11.tabular
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:14:25 2019 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,641 +0,0 @@
-index cell_type
-0 progenitor
-1 progenitor
-2 progenitor
-3 progenitor
-4 progenitor
-5 progenitor
-6 progenitor
-7 progenitor
-8 progenitor
-9 progenitor
-10 progenitor
-11 progenitor
-12 progenitor
-13 progenitor
-14 progenitor
-15 progenitor
-16 progenitor
-17 progenitor
-18 progenitor
-19 progenitor
-20 progenitor
-21 progenitor
-22 progenitor
-23 progenitor
-24 progenitor
-25 progenitor
-26 progenitor
-27 progenitor
-28 progenitor
-29 progenitor
-30 progenitor
-31 progenitor
-32 progenitor
-33 progenitor
-34 progenitor
-35 progenitor
-36 progenitor
-37 progenitor
-38 progenitor
-39 progenitor
-40 progenitor
-41 progenitor
-42 progenitor
-43 progenitor
-44 progenitor
-45 progenitor
-46 progenitor
-47 progenitor
-48 progenitor
-49 progenitor
-50 progenitor
-51 progenitor
-52 progenitor
-53 progenitor
-54 progenitor
-55 progenitor
-56 progenitor
-57 progenitor
-58 progenitor
-59 progenitor
-60 progenitor
-61 progenitor
-62 progenitor
-63 progenitor
-64 progenitor
-65 progenitor
-66 progenitor
-67 progenitor
-68 progenitor
-69 progenitor
-70 progenitor
-71 progenitor
-72 progenitor
-73 progenitor
-74 progenitor
-75 progenitor
-76 progenitor
-77 progenitor
-78 progenitor
-79 progenitor
-80 Mo
-81 Mo
-82 Mo
-83 Mo
-84 Mo
-85 Mo
-86 Mo
-87 Mo
-88 Mo
-89 Mo
-90 Mo
-91 Mo
-92 Mo
-93 Mo
-94 Mo
-95 Mo
-96 Mo
-97 Mo
-98 Mo
-99 Mo
-100 Mo
-101 Mo
-102 Mo
-103 Mo
-104 Mo
-105 Mo
-106 Mo
-107 Mo
-108 Mo
-109 Mo
-110 Mo
-111 Mo
-112 Mo
-113 Mo
-114 Mo
-115 Mo
-116 Mo
-117 Mo
-118 Mo
-119 Mo
-120 Mo
-121 Mo
-122 Mo
-123 Mo
-124 Mo
-125 Mo
-126 Mo
-127 Mo
-128 Mo
-129 Mo
-130 Mo
-131 Mo
-132 Mo
-133 Mo
-134 Mo
-135 Mo
-136 Mo
-137 Mo
-138 Mo
-139 Mo
-140 Mo
-141 Mo
-142 Mo
-143 Mo
-144 Mo
-145 Mo
-146 Mo
-147 Mo
-148 Mo
-149 Mo
-150 Mo
-151 Mo
-152 Mo
-153 Mo
-154 Mo
-155 Mo
-156 Mo
-157 Mo
-158 Mo
-159 Mo
-0 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
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diff -r 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 2019 -0500
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diff -r 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 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 10:14:25 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
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-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 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.pca.krumsiek11.h5ad
Binary file test-data/tl.pca.krumsiek11.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.rank_genes_groups.krumsiek11.h5ad
Binary file test-data/tl.rank_genes_groups.krumsiek11.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad
Binary file test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 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 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes.krumsiek11.h5ad
Binary file test-data/tl.score_genes.krumsiek11.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:14:25 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 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad
Binary file test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad has changed
diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular
--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:14:25 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
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.tsne.krumsiek11.h5ad
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diff -r 397d2c97af05 -r e4c0f5ee8e17 test-data/tl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad
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