Mercurial > repos > iuc > scanpy_plot

comparison README.md @ 14:aa8abbf399b3 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 91121b1e72696f17478dae383badaa71e9f96dbb
author iuc
date Sat, 14 Sep 2024 12:39:55 +0000
parents 072b759d0fca
children
comparison
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13:084c74890a1f 14:aa8abbf399b3
23 `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.
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**) 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**)
25 `pp.highly_variable_genes` | Extract highly variable genes 25 `pp.highly_variable_genes` | Extract highly variable genes
26 `pp.subsample` | Subsample to a fraction of the number of observations 26 `pp.subsample` | Subsample to a fraction of the number of observations
27 `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts 27 `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts
28 `pp.scrublet` | Predict doublets
28 29
29 3. Normalize (`normalize.xml`) 30 3. Normalize (`normalize.xml`)
30 31
31 Methods | Description 32 Methods | Description
32 --- | --- 33 --- | ---
33 `pp.normalize_total` | Normalize counts per cell 34 `pp.normalize_total` | Normalize counts per cell
34 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17] 35 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]
35 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17] 36 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]
36 `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15] 37 `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15]
38 `external.pp.magic` | Denoising using Markov Affinity-based Graph Imputation of Cells (MAGIC) API
37 39
38 4. Remove confounders (`remove_confounder.xml`) 40 4. Remove confounders (`remove_confounder.xml`)
39 41
40 Methods | Description 42 Methods | Description
41 --- | --- 43 --- | ---
42 `pp.regress_out` | Regress out unwanted sources of variation 44 `pp.regress_out` | Regress out unwanted sources of variation
43 `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors 45 <!-- `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors -->
44 `pp.combat` | ComBat function for batch effect correction 46 `pp.combat` | ComBat function for batch effect correction
47 `external.pp.bbknn` | Batch effect removal with Batch balanced KNN (BBKNN)
48 `external.pp.harmony_integrate` | Integrate multiple single-cell experiments with Harmony
49 `external.pp.scanorama_integrate` | Integrate multiple single-cell experiments with Scanorama
45 50
46 5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`) 51 5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`)
47 52
48 Methods | Description 53 Methods | Description
49 --- | --- 54 --- | ---
50 `tl.louvain` | Cluster cells into subgroups 55 `tl.louvain` | Cluster cells into subgroups
51 `tl.leiden` | Cluster cells into subgroups 56 `tl.leiden` | Cluster cells into subgroups
52 `tl.pca` | Principal component analysis 57 `pp.pca` | Principal component analysis
53 `pp.pca` | Principal component analysis (appears to be the same func...)
54 `tl.diffmap` | Diffusion Maps 58 `tl.diffmap` | Diffusion Maps
55 `tl.tsne` | t-SNE 59 `tl.tsne` | t-SNE
56 `tl.umap` | Embed the neighborhood graph using UMAP 60 `tl.umap` | Embed the neighborhood graph using UMAP
57 `tl.draw_graph` | Force-directed graph drawing 61 `tl.draw_graph` | Force-directed graph drawing
58 `tl.dpt` | Infer progression of cells through geodesic distance along the graph 62 `tl.dpt` | Infer progression of cells through geodesic distance along the graph
59 `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds 63 `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds
64 `tl.embedding_density` | Calculate the density of cells in an embedding (per condition)
60 65
61 6. Plot (`plot.xml`) 66 6. Plot (`plot.xml`)
62 67
63 1. Generic 68 1. Generic
64 69
65 Methods | Description 70 Methods | Description
66 --- | --- 71 --- | ---
67 `pl.scatter` | Scatter plot along observations or variables axes 72 `pl.scatter` | Scatter plot along observations or variables axes
68 `pl.heatmap` | Heatmap of the expression values of set of genes 73 `pl.heatmap` | Heatmap of the expression values of set of genes
74 `pl.tracksplot` | Tracks plot of the expression values per cell
69 `pl.dotplot` | Makes a dot plot of the expression values 75 `pl.dotplot` | Makes a dot plot of the expression values
70 `pl.violin` | Violin plot 76 `pl.violin` | Violin plot
71 `pl.stacked_violin` | Stacked violin plots 77 `pl.stacked_violin` | Stacked violin plots
72 `pl.matrixplot` | Heatmap of the mean expression values per cluster 78 `pl.matrixplot` | Heatmap of the mean expression values per cluster
73 `pl.clustermap` | Hierarchically-clustered heatmap 79 `pl.clustermap` | Hierarchically-clustered heatmap
74 80
75 2. Preprocessing 81 2. Preprocessing
76 82
77 Methods | Description 83 Methods | Description
78 --- | --- 84 --- | ---
79 `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells 85 `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 `pl.highly_variable_genes` | Plot dispersions versus means for genes
87 `pl.scrublet_score_distribution` | Histogram of doublet scores
81 88
82 3. PCA 89 3. PCA
83 90
84 Methods | Description 91 Methods | Description
85 --- | --- 92 --- | ---
94 --- | --- 101 --- | ---
95 `pl.tsne` | Scatter plot in tSNE basis 102 `pl.tsne` | Scatter plot in tSNE basis
96 `pl.umap` | Scatter plot in UMAP basis 103 `pl.umap` | Scatter plot in UMAP basis
97 `pl.diffmap` | Scatter plot in Diffusion Map basis 104 `pl.diffmap` | Scatter plot in Diffusion Map basis
98 `pl.draw_graph` | Scatter plot in graph-drawing basis 105 `pl.draw_graph` | Scatter plot in graph-drawing basis
106 `pl.embedding_density` | Density of cells in an embedding (per condition)
99 107
100 5. Branching trajectories and pseudotime, clustering 108 5. Branching trajectories and pseudotime, clustering
101 109
102 Methods | Description 110 Methods | Description
103 --- | --- 111 --- | ---
104 `pl.dpt_groups_pseudotime` | Plot groups and pseudotime 112 <!-- `pl.dpt_groups_pseudotime` | Plot groups and pseudotime -->
105 `pl.dpt_timeseries` | Heatmap of pseudotime series 113 `pl.dpt_timeseries` | Heatmap of pseudotime series
106 `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges 114 `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges
107 `pl.paga_compare` | Scatter and PAGA graph side-by-side 115 `pl.paga_compare` | Scatter and PAGA graph side-by-side
108 `pl.paga_path` | Gene expression and annotation changes along paths 116 `pl.paga_path` | Gene expression and annotation changes along paths
109 117
111 119
112 Methods | Description 120 Methods | Description
113 --- | --- 121 --- | ---
114 `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot 122 `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 123 `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons
124 `pl.rank_genes_groups_stacked_violin` | Plot ranking of genes as stacked violin plot
125 `pl.rank_genes_groups_heatmap` | Plot ranking of genes as heatmap plot
126 `pl.rank_genes_groups_dotplot` | Plot ranking of genes as dotplot plot
127 `pl.rank_genes_groups_matrixplot` | Plot ranking of genes as matrixplot plot
128 `pl.rank_genes_groups_tracksplot` | Plot ranking of genes as tracksplot plot