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comparison README.md @ 1:e4c0f5ee8e17 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
author iuc
date Wed, 16 Oct 2019 06:28:57 -0400
parents 397d2c97af05
children 2dfb2227a16c
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0:397d2c97af05 1:e4c0f5ee8e17
1 Scanpy 1 Scanpy
2 ====== 2 ======
3 3
4 ## Classification of methods into steps 4 1. Inspect & Manipulate (`inspect.xml`)
5 5
6 Steps: 6 Methods | Description
7 --- | ---
8 `pp.calculate_qc_metrics` | Calculate quality control metrics
9 `pp.neighbors` | Compute a neighborhood graph of observations
10 `tl.score_genes` | Score a set of genes
11 `tl.score_genes_cell_cycle` | Score cell cycle gene
12 `tl.rank_genes_groups` | Rank genes for characterizing groups
13 `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**)
14 `pp.log1p` | Logarithmize the data matrix.
15 `pp.scale` | Scale data to unit variance and zero mean
16 `pp.sqrt` | Square root the data matrix
7 17
8 1. Filtering 18 2. Filter (`filter.xml`)
9 19
10 Methods | Description 20 Methods | Description
11 --- | --- 21 --- | ---
12 `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed. 22 `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. 23 `pp.filter_genes` | Filter genes based on number of cells or counts.
14 `pp.filter_genes_dispersion` | Extract highly variable genes 24 `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**)
15 `pp.highly_variable_genes` | Extract highly variable genes 25 `pp.highly_variable_genes` | Extract highly variable genes
16 `pp.subsample` | Subsample to a fraction of the number of observations 26 `pp.subsample` | Subsample to a fraction of the number of observations
17 `queries.gene_coordinates` | (Could not find...) 27 `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts
18 `queries.mitochondrial_genes` | Retrieves Mitochondrial gene symbols for specific organism through BioMart for filtering
19 28
20 2. Quality Plots 29 3. Normalize (`normalize.xml`)
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 30
32 Methods | Description 31 Methods | Description
33 --- | --- 32 --- | ---
34 `pp.normalize_per_cell` | Normalize total counts per cell 33 `pp.normalize_total` | Normalize counts per cell
35 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17] 34 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]
36 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17] 35 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]
37 `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15] 36 `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 37
43 4. Conf. removal 38 4. Remove confounders (`remove_confounder.xml`)
44 39
45 Methods | Description 40 Methods | Description
46 --- | --- 41 --- | ---
47 `pp.regress_out` | Regress out unwanted sources of variation 42 `pp.regress_out` | Regress out unwanted sources of variation
48 `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors 43 `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors
49 `pp.dca` | Deep count autoencoder to denoise the data 44 `pp.combat` | ComBat function for batch effect correction
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 45
58 5. Clustering and Heatmaps 46 5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`)
59 47
60 Methods | Description 48 Methods | Description
61 --- | --- 49 --- | ---
62 `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15] 50 `tl.louvain` | Cluster cells into subgroups
63 `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17] 51 `tl.leiden` | Cluster cells into subgroups
64 `tl.pca` | Principal component analysis 52 `tl.pca` | Principal component analysis
65 `pp.pca` | Principal component analysis (appears to be the same func...) 53 `pp.pca` | Principal component analysis (appears to be the same func...)
66 `tl.diffmap` | Diffusion Maps 54 `tl.diffmap` | Diffusion Maps
67 `tl.tsne` | t-SNE 55 `tl.tsne` | t-SNE
68 `tl.umap` | Embed the neighborhood graph using UMAP 56 `tl.umap` | Embed the neighborhood graph using UMAP
69 `tl.phate` | PHATE 57 `tl.draw_graph` | Force-directed graph drawing
70 `pp.neighbors` | Compute a neighborhood graph of observations 58 `tl.dpt` | Infer progression of cells through geodesic distance along the graph
71 `tl.rank_genes_groups` | Rank genes for characterizing groups 59 `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds
72 `pl.rank_genes_groups` | 60
73 `pl.rank_genes_groups_dotplot` | 61 6. Plot (`plot.xml`)
74 `pl.rank_genes_groups_heatmap` | 62
75 `pl.rank_genes_groups_matrixplot` | 63 1. Generic
76 `pl.rank_genes_groups_stacked_violin` | 64
77 `pl.rank_genes_groups_violin` | 65 Methods | Description
78 `pl.matrix_plot` | 66 --- | ---
79 `pl.heatmap` | 67 `pl.scatter` | Scatter plot along observations or variables axes
80 `pl.highest_expr_genes` | 68 `pl.heatmap` | Heatmap of the expression values of set of genes
81 `pl.diffmap` | 69 `pl.dotplot` | Makes a dot plot of the expression values
70 `pl.violin` | Violin plot
71 `pl.stacked_violin` | Stacked violin plots
72 `pl.matrixplot` | Heatmap of the mean expression values per cluster
73 `pl.clustermap` | Hierarchically-clustered heatmap
82 74
83 6. Cluster Inspection and plotting 75 2. Preprocessing
84 76
85 Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related. 77 Methods | Description
78 --- | ---
79 `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells
80 `pl.highly_variable_genes` | Plot dispersions versus means for genes
86 81
87 Methods | Description 82 3. PCA
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 83
107 7. Branch/Between-Cluster Inspection 84 Methods | Description
85 --- | ---
86 `pl.pca` | Scatter plot in PCA coordinates
87 `pl.pca_loadings` | Rank genes according to contributions to PCs
88 `pl.pca_variance_ratio` | Scatter plot in PCA coordinates
89 `pl.pca_overview` | Plot PCA results
108 90
109 Pseudotime analysis, relies on initial clustering. 91 4. Embeddings
110 92
111 Methods | Description 93 Methods | Description
112 --- | --- 94 --- | ---
113 `tl.dpt` | Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i] 95 `pl.tsne` | Scatter plot in tSNE basis
114 `pl.dpt_groups_pseudotime` | 96 `pl.umap` | Scatter plot in UMAP basis
115 `pl.dpt_timeseries` | 97 `pl.diffmap` | Scatter plot in Diffusion Map basis
116 `tl.paga_compare_paths` | 98 `pl.draw_graph` | Scatter plot in graph-drawing basis
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 99
100 5. Branching trajectories and pseudotime, clustering
128 101
129 Methods to sort | Description 102 Methods | Description
130 --- | --- 103 --- | ---
131 `tl.ROC_AUC_analysis` | (could not find in API) 104 `pl.dpt_groups_pseudotime` | Plot groups and pseudotime
132 `tl.correlation_matrix` | (could not find in API) 105 `pl.dpt_timeseries` | Heatmap of pseudotime series
133 `rtools.mnn_concatenate` | (could not find in API) 106 `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges
134 `utils.compute_association_matrix_of_groups` | (could not find in API) 107 `pl.paga_compare` | Scatter and PAGA graph side-by-side
135 `utils.cross_entropy_neighbors_in_rep` | (could not find in API) 108 `pl.paga_path` | Gene expression and annotation changes along paths
136 `utils.merge_groups` | (could not find in API) 109
137 `utils.plot_category_association` | (could not find in API) 110 6. Marker genes
138 `utils.select_groups` | (could not find in API) 111
112 Methods | Description
113 --- | ---
114 `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot
115 `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons