Mercurial > repos > iuc > scanpy_filter
comparison README.md @ 1:6a76b60e05f5 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
| author | iuc |
|---|---|
| date | Wed, 16 Oct 2019 06:32:33 -0400 |
| parents | 6ea5a05a260a |
| children |
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| 0:6ea5a05a260a | 1:6a76b60e05f5 |
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| 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 |