Mercurial > repos > iuc > scanpy_remove_confounders

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 0ef475df22ba51263bb3d1db9f8797c723db35b0
author iuc
date Tue, 20 Aug 2024 09:51:42 +0000
parents a89ee42625ad
children b70219da4a96
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9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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1 Scanpy
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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2 ======
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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4 1. Inspect & Manipulate (`inspect.xml`)
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6 Methods | Description
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7 --- | ---
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8 `pp.calculate_qc_metrics` | Calculate quality control metrics
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9 `pp.neighbors` | Compute a neighborhood graph of observations
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10 `tl.score_genes` | Score a set of genes
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11 `tl.score_genes_cell_cycle` | Score cell cycle gene
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12 `tl.rank_genes_groups` | Rank genes for characterizing groups
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13 `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**)
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14 `pp.log1p` | Logarithmize the data matrix.
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15 `pp.scale` | Scale data to unit variance and zero mean
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16 `pp.sqrt` | Square root the data matrix
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18 2. Filter (`filter.xml`)
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20 Methods | Description
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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21 --- | ---
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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22 `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed.
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23 `pp.filter_genes` | Filter genes based on number of cells or counts.
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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**)
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25 `pp.highly_variable_genes` | Extract highly variable genes
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26 `pp.subsample` | Subsample to a fraction of the number of observations
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27 `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts
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29 3. Normalize (`normalize.xml`)
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31 Methods | Description
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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32 --- | ---
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33 `pp.normalize_total` | Normalize counts per cell
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34 `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17]
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35 `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17]
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36 `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15]
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38 4. Remove confounders (`remove_confounder.xml`)
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40 Methods | Description
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41 --- | ---
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42 `pp.regress_out` | Regress out unwanted sources of variation
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43 `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors
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44 `pp.combat` | ComBat function for batch effect correction
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46 5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`)
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48 Methods | Description
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49 --- | ---
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50 `tl.louvain` | Cluster cells into subgroups
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51 `tl.leiden` | Cluster cells into subgroups
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52 `tl.pca` | Principal component analysis
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53 `pp.pca` | Principal component analysis (appears to be the same func...)
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54 `tl.diffmap` | Diffusion Maps
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55 `tl.tsne` | t-SNE
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56 `tl.umap` | Embed the neighborhood graph using UMAP
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57 `tl.draw_graph` | Force-directed graph drawing
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58 `tl.dpt` | Infer progression of cells through geodesic distance along the graph
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59 `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds
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61 6. Plot (`plot.xml`)
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63 1. Generic
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65 Methods | Description
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66 --- | ---
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67 `pl.scatter` | Scatter plot along observations or variables axes
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68 `pl.heatmap` | Heatmap of the expression values of set of genes
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69 `pl.dotplot` | Makes a dot plot of the expression values
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70 `pl.violin` | Violin plot
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71 `pl.stacked_violin` | Stacked violin plots
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72 `pl.matrixplot` | Heatmap of the mean expression values per cluster
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73 `pl.clustermap` | Hierarchically-clustered heatmap
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75 2. Preprocessing
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77 Methods | Description
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78 --- | ---
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79 `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells
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80 `pl.highly_variable_genes` | Plot dispersions versus means for genes
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82 3. PCA
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84 Methods | Description
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86 `pl.pca` | Scatter plot in PCA coordinates
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87 `pl.pca_loadings` | Rank genes according to contributions to PCs
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88 `pl.pca_variance_ratio` | Scatter plot in PCA coordinates
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89 `pl.pca_overview` | Plot PCA results
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90
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91 4. Embeddings
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92
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93 Methods | Description
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94 --- | ---
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95 `pl.tsne` | Scatter plot in tSNE basis
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96 `pl.umap` | Scatter plot in UMAP basis
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97 `pl.diffmap` | Scatter plot in Diffusion Map basis
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98 `pl.draw_graph` | Scatter plot in graph-drawing basis
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99
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100 5. Branching trajectories and pseudotime, clustering
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101
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102 Methods | Description
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103 --- | ---
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104 `pl.dpt_groups_pseudotime` | Plot groups and pseudotime
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105 `pl.dpt_timeseries` | Heatmap of pseudotime series
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106 `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges
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107 `pl.paga_compare` | Scatter and PAGA graph side-by-side
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108 `pl.paga_path` | Gene expression and annotation changes along paths
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109
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110 6. Marker genes
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111
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112 Methods | Description
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113 --- | ---
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114 `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot
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115 `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons