| The different methods from Scanpy have been grouped by themes:
1. Filter in `filter.xml`
- Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`
- Filter genes based on number of cells or counts, using `pp.filter_genes`
- Extract highly variable genes, using `pp.filter_genes_dispersion`
- `tl.highly_variable_genes` (need to be added)
- Subsample to a fraction of the number of observations, using `pp.subsample`
- `queries.gene_coordinates` (need to be added)
- `queries.mitochondrial_genes` (need to be added)
2. Normalize in `normalize.xml`
- Normalize total counts per cell, using `pp.normalize_per_cell`
- Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`
- Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`
- Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`
- Logarithmize the data matrix, using `pp.log1p`
- Scale data to unit variance and zero mean, using `pp.scale`
- Square root the data matrix, using `pp.sqrt`
- Downsample counts, using `pp.downsample_counts`
3. Remove confounder in `remove_confounders.xml`
- Regress out unwanted sources of variation, using `pp.regress_out`
- `pp.mnn_correct` (need to be added)
- `pp.mnn_correct` (need to be added)
- `pp.magic` (need to be added)
- `tl.sim` (need to be added)
- `pp.calculate_qc_metrics` (need to be added)
- Score a set of genes, using `tl.score_genes`
- Score cell cycle genes, using `tl.score_genes_cell_cycle`
- `tl.cyclone` (need to be added)
- `tl.andbag` (need to be added)
4. Cluster and reduce dimension in `cluster_reduce_dimension.xml`
- `tl.leiden` (need to be added)
- Cluster cells into subgroups, using `tl.louvain`
- Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
- Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
- Diffusion Maps, using `tl.diffmap`
- t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
- Embed the neighborhood graph using UMAP, using `tl.umap`
- `tl.phate` (need to be added)
- Compute a neighborhood graph of observations, using `pp.neighbors`
- Rank genes for characterizing groups, using `tl.rank_genes_groups`
4. Inspect
- `tl.paga_compare_paths` (need to be added)
- `tl.paga_degrees` (need to be added)
- `tl.paga_expression_entropies` (need to be added)
- Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga`
- Infer progression of cells through geodesic distance along the graph, using `tl.dpt`
5. Plot
1. Generic
- Scatter plot along observations or variables axes, using `pl.scatter`
- Heatmap of the expression values of set of genes, using `pl.heatmap`
- Makes a dot plot of the expression values, using `pl.dotplot`
- Violin plot, using `pl.violin`
- `pl.stacked_violin` (need to be added)
- Heatmap of the mean expression values per cluster, using `pl.matrixplot`
- Hierarchically-clustered heatmap, using `pl.clustermap`
- `pl.ranking`
2. Preprocessing
- Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes`
- Plot dispersions versus means for genes, using `pl.filter_genes_dispersion`
- `pl.highly_variable_genes` (need to be added)
- `pl.calculate_qc_metrics` (need to be added)
3. PCA
- Scatter plot in PCA coordinates, using `pl.pca`
- Rank genes according to contributions to PCs, using `pl.pca_loadings`
- Scatter plot in PCA coordinates, using `pl.pca_variance_ratio`
- Plot PCA results, using `pl.pca_overview`
4. Embeddings
- Scatter plot in tSNE basis, using `pl.tsne`
- Scatter plot in UMAP basis, using `pl.umap`
- Scatter plot in Diffusion Map basis, using `pl.diffmap`
- `pl.draw_graph` (need to be added)
5. Branching trajectories and pseudotime, clustering
- Plot groups and pseudotime, using `pl.dpt_groups_pseudotime`
- Heatmap of pseudotime series, using `pl.dpt_timeseries`
- Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga`
- `pl.paga_compare` (need to be added)
- `pl.paga_path` (need to be added)
6. Marker genes:
- Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups`
- `pl.rank_genes_groups_dotplot` (need to be added)
- `pl.rank_genes_groups_heatmap` (need to be added)
- `pl.rank_genes_groups_matrixplot` (need to be added)
- `pl.rank_genes_groups_stacked_violin` (need to be added)
- `pl.rank_genes_groups_violin` (need to be added)
7. Misc
- `pl.phate` (need to be added)
- `pl.matrix` (need to be added)
- `pl.paga_adjacency` (need to be added)
- `pl.timeseries` (need to be added)
- `pl.timeseries_as_heatmap` (need to be added)
- `pl.timeseries_subplot` (need to be added)
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