Mercurial > repos > iuc > scanpy_remove_confounders

comparison README.rst @ 0:9ca360dde8e3 draft

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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 10:16:47 -0500
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1 The different methods from Scanpy have been grouped by themes:
2
3 1. Filter in `filter.xml`
4 - Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`
5 - Filter genes based on number of cells or counts, using `pp.filter_genes`
6 - Extract highly variable genes, using `pp.filter_genes_dispersion`
7 - `tl.highly_variable_genes` (need to be added)
8 - Subsample to a fraction of the number of observations, using `pp.subsample`
9 - `queries.gene_coordinates` (need to be added)
10 - `queries.mitochondrial_genes` (need to be added)
11
12 2. Normalize in `normalize.xml`
13 - Normalize total counts per cell, using `pp.normalize_per_cell`
14 - Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`
15 - Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`
16 - Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`
17 - Logarithmize the data matrix, using `pp.log1p`
18 - Scale data to unit variance and zero mean, using `pp.scale`
19 - Square root the data matrix, using `pp.sqrt`
20 - Downsample counts, using `pp.downsample_counts`
21
22 3. Remove confounder in `remove_confounders.xml`
23 - Regress out unwanted sources of variation, using `pp.regress_out`
24 - `pp.mnn_correct` (need to be added)
25 - `pp.mnn_correct` (need to be added)
26 - `pp.magic` (need to be added)
27 - `tl.sim` (need to be added)
28 - `pp.calculate_qc_metrics` (need to be added)
29 - Score a set of genes, using `tl.score_genes`
30 - Score cell cycle genes, using `tl.score_genes_cell_cycle`
31 - `tl.cyclone` (need to be added)
32 - `tl.andbag` (need to be added)
33
34 4. Cluster and reduce dimension in `cluster_reduce_dimension.xml`
35 - `tl.leiden` (need to be added)
36 - Cluster cells into subgroups, using `tl.louvain`
37 - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
38 - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
39 - Diffusion Maps, using `tl.diffmap`
40 - t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
41 - Embed the neighborhood graph using UMAP, using `tl.umap`
42 - `tl.phate` (need to be added)
43 - Compute a neighborhood graph of observations, using `pp.neighbors`
44 - Rank genes for characterizing groups, using `tl.rank_genes_groups`
45
46 4. Inspect
47 - `tl.paga_compare_paths` (need to be added)
48 - `tl.paga_degrees` (need to be added)
49 - `tl.paga_expression_entropies` (need to be added)
50 - Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`
51 - Infer progression of cells through geodesic distance along the graph, using `tl.dpt`
52
53 5. Plot
54 1. Generic
55 - Scatter plot along observations or variables axes, using `pl.scatter`
56 - Heatmap of the expression values of set of genes, using `pl.heatmap`
57 - Makes a dot plot of the expression values, using `pl.dotplot`
58 - Violin plot, using `pl.violin`
59 - `pl.stacked_violin` (need to be added)
60 - Heatmap of the mean expression values per cluster, using `pl.matrixplot`
61 - Hierarchically-clustered heatmap, using `pl.clustermap`
62 - `pl.ranking`
63
64 2. Preprocessing
65 - Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes`
66 - Plot dispersions versus means for genes, using `pl.filter_genes_dispersion`
67 - `pl.highly_variable_genes` (need to be added)
68 - `pl.calculate_qc_metrics` (need to be added)
69
70 3. PCA
71 - Scatter plot in PCA coordinates, using `pl.pca`
72 - Rank genes according to contributions to PCs, using `pl.pca_loadings`
73 - Scatter plot in PCA coordinates, using `pl.pca_variance_ratio`
74 - Plot PCA results, using `pl.pca_overview`
75
76 4. Embeddings
77 - Scatter plot in tSNE basis, using `pl.tsne`
78 - Scatter plot in UMAP basis, using `pl.umap`
79 - Scatter plot in Diffusion Map basis, using `pl.diffmap`
80 - `pl.draw_graph` (need to be added)
81
82 5. Branching trajectories and pseudotime, clustering
83 - Plot groups and pseudotime, using `pl.dpt_groups_pseudotime`
84 - Heatmap of pseudotime series, using `pl.dpt_timeseries`
85 - Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga`
86 - `pl.paga_compare` (need to be added)
87 - `pl.paga_path` (need to be added)
88
89 6. Marker genes:
90 - Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups`
91 - `pl.rank_genes_groups_dotplot` (need to be added)
92 - `pl.rank_genes_groups_heatmap` (need to be added)
93 - `pl.rank_genes_groups_matrixplot` (need to be added)
94 - `pl.rank_genes_groups_stacked_violin` (need to be added)
95 - `pl.rank_genes_groups_violin` (need to be added)
96
97 7. Misc
98 - `pl.phate` (need to be added)
99 - `pl.matrix` (need to be added)
100 - `pl.paga_adjacency` (need to be added)
101 - `pl.timeseries` (need to be added)
102 - `pl.timeseries_as_heatmap` (need to be added)
103 - `pl.timeseries_subplot` (need to be added)
104
105