diff README.md @ 0:0e212e42ef88 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6

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+Scanpy
+======
+
+## Classification of methods into steps
+
+Steps:
+
+1. Filtering
+
+    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
+    `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.
+
+    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
+
+    Methods | Description
+    --- | ---
+    `pp.normalize_per_cell` | Normalize total 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
+
+    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]
+
+5. Clustering and Heatmaps
+
+    Methods | Description
+    --- | ---
+    `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15]
+    `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17]
+    `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` | 
+    
+6. Cluster Inspection and plotting
+
+    Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related.
+
+    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` | 
+
+7. Branch/Between-Cluster Inspection
+
+    Pseudotime analysis, relies on initial clustering.
+
+    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` | 
+
+
+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)
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