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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
author iuc
date Mon, 04 Mar 2019 10:15:38 -0500
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1 Scanpy
2 ======
3
4 ## Classification of methods into steps
5
6 Steps:
7
8 1. Filtering
9
10 Methods | Description
11 --- | ---
12 `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed.
13 `pp.filter_genes` | Filter genes based on number of cells or counts.
14 `pp.filter_genes_dispersion` | Extract highly variable genes
15 `pp.highly_variable_genes` | Extract highly variable genes
16 `pp.subsample` | Subsample to a fraction of the number of observations
17 `queries.gene_coordinates` | (Could not find...)
18 `queries.mitochondrial_genes` | Retrieves Mitochondrial gene symbols for specific organism through BioMart for filtering
19
20 2. Quality Plots
21
22 These are in-between stages used to measure the effectiveness of a Filtering/Normalisation/Conf.Removal stage either after processing or prior to.
23
24 Methods | Description | Notes
25 --- | --- | ---
26 `pp.calculate_qc_metrics` | Calculate quality control metrics
27 `pl.violin` | violin plot of features, lib. size, or subsets of.
28 `pl.stacked_violin` | Same as above but for multiple series of features or cells
29
30 3. Normalization
31
32 Methods | Description
33 --- | ---
34 `pp.normalize_per_cell` | Normalize total counts per cell
35 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]
36 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]
37 `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15]
38 `pp.log1p` | Logarithmize the data matrix.
39 `pp.scale` | Scale data to unit variance and zero mean
40 `pp.sqrt` |
41 `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts
42
43 4. Conf. removal
44
45 Methods | Description
46 --- | ---
47 `pp.regress_out` | Regress out unwanted sources of variation
48 `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors
49 `pp.dca` | Deep count autoencoder to denoise the data
50 `pp.magic` | Markov Affinity-based Graph Imputation of Cells (MAGIC) API to denoise
51 `tl.sim` | Simulate dynamic gene expression data [Wittman09]
52 `pp.calculate_qc_metrics` | Calculate quality control metrics
53 `tl.score_genes` | Score a set of genes
54 `tl.score_genes_cell_cycle` | Score cell cycle genes
55 `tl.cyclone` | Assigns scores and predicted class to observations based on cell-cycle genes [Scialdone15]
56 `tl.sandbag` | Calculates pairs of genes serving as markers for each cell-cycle phase [Scialdone15]
57
58 5. Clustering and Heatmaps
59
60 Methods | Description
61 --- | ---
62 `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15]
63 `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17]
64 `tl.pca` | Principal component analysis
65 `pp.pca` | Principal component analysis (appears to be the same func...)
66 `tl.diffmap` | Diffusion Maps
67 `tl.tsne` | t-SNE
68 `tl.umap` | Embed the neighborhood graph using UMAP
69 `tl.phate` | PHATE
70 `pp.neighbors` | Compute a neighborhood graph of observations
71 `tl.rank_genes_groups` | Rank genes for characterizing groups
72 `pl.rank_genes_groups` |
73 `pl.rank_genes_groups_dotplot` |
74 `pl.rank_genes_groups_heatmap` |
75 `pl.rank_genes_groups_matrixplot` |
76 `pl.rank_genes_groups_stacked_violin` |
77 `pl.rank_genes_groups_violin` |
78 `pl.matrix_plot` |
79 `pl.heatmap` |
80 `pl.highest_expr_genes` |
81 `pl.diffmap` |
82
83 6. Cluster Inspection and plotting
84
85 Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related.
86
87 Methods | Description
88 --- | ---
89 `pl.clustermap` |
90 `pl.phate` |
91 `pl.dotplot` |
92 `pl.draw_graph` | (really general purpose, would not implement directly)
93 `pl.filter_genes_dispersion` | (depreciated for 'highly_variable_genes')
94 `pl.matrix` | (could not find in API)
95 `pl.pca` |
96 `pl.pca_loadings` |
97 `pl.pca_overview` |
98 `pl.pca_variance_ratio` |
99 `pl.ranking` | (not sure what this does...)
100 `pl.scatter` | ([very general purpose](https://icb-scanpy.readthedocs-hosted.com/en/latest/api/scanpy.api.pl.scatter.html), would not implement directly)
101 `pl.set_rcParams_defaults` |
102 `pl.set_rcParams_scanpy` |
103 `pl.sim` |
104 `pl.tsne` |
105 `pl.umap` |
106
107 7. Branch/Between-Cluster Inspection
108
109 Pseudotime analysis, relies on initial clustering.
110
111 Methods | Description
112 --- | ---
113 `tl.dpt` | Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i]
114 `pl.dpt_groups_pseudotime` |
115 `pl.dpt_timeseries` |
116 `tl.paga_compare_paths` |
117 `tl.paga_degrees` |
118 `tl.paga_expression_entropies` |
119 `tl.paga` | Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i]
120 `pl.paga` |
121 `pl.paga_adjacency` |
122 `pl.paga_compare` |
123 `pl.paga_path` |
124 `pl.timeseries` |
125 `pl.timeseries_as_heatmap` |
126 `pl.timeseries_subplot` |
127
128
129 Methods to sort | Description
130 --- | ---
131 `tl.ROC_AUC_analysis` | (could not find in API)
132 `tl.correlation_matrix` | (could not find in API)
133 `rtools.mnn_concatenate` | (could not find in API)
134 `utils.compute_association_matrix_of_groups` | (could not find in API)
135 `utils.cross_entropy_neighbors_in_rep` | (could not find in API)
136 `utils.merge_groups` | (could not find in API)
137 `utils.plot_category_association` | (could not find in API)
138 `utils.select_groups` | (could not find in API)