milopy.plot
Module Contents
Functions
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Visualize DA results on abstracted graph (wrapper around sc.pl.embedding) |
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Visualize cells in a neighbourhood |
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Plot beeswarm plot of logFC against nhood labels |
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Plot boxplot of cell numbers vs condition of interest |
- milopy.plot.plot_nhood_graph(adata: anndata.AnnData, alpha: float = 0.1, min_logFC: float = 0, min_size: int = 10, plot_edges: bool = False, title: str = 'DA log-Fold Change', **kwargs)
Visualize DA results on abstracted graph (wrapper around sc.pl.embedding)
Params:
adata: AnnData object
alpha: significance threshold
min_logFC: minimum absolute log-Fold Change to show results (default: 0, show all significant neighbourhoods)
min_size: minimum size of nodes in visualization (default: 10)
plot_edges: boolean indicating if edges for neighbourhood overlaps whould be plotted (default: False)
title: plot title (default: ‘DA log-Fold Change’)
**kwargs: other arguments to pass to scanpy.pl.embedding
- milopy.plot.plot_nhood(adata, ix, basis='X_umap')
Visualize cells in a neighbourhood
- milopy.plot.plot_DA_beeswarm(adata: anndata.AnnData, anno_col: str = 'nhood_annotation', alpha: float = 0.1, subset_nhoods: List = None)
Plot beeswarm plot of logFC against nhood labels
Params:
adata: AnnData object
anno_col: column in adata.uns[‘nhood_adata’].obs to use as annotation
alpha: significance threshold
subset_nhoods: list of nhoods to plot (default: None, plot all nhoods)
- milopy.plot._get_palette_adata(adata, obs_col)
- milopy.plot.plot_nhood_counts_by_cond(adata: anndata.AnnData, test_var: str, subset_nhoods: List = None, log_counts: bool = False)
Plot boxplot of cell numbers vs condition of interest
Params:
adata: anndata object storing neighbourhood information in adata.uns
test_var: string, name of column in adata.obs storing condition of interest (y-axis for boxplot)
subset_nhoods: list of obs_names for neighbourhoods to include in plot (default: None, plot all nhoods)
log_counts: boolean, whether to plot log1p of cell counts (default: False)