adataviz.plotting module

adataviz.plotting.get_boxplot_data(adata, variable, gene, obs=None)

adataviz.plotting.get_genes_mean_frac(adata, obs=None, groupby='Subclass', modality='RNA', layer='mean', use_raw=False, expression_cutoff='p5', genes=None, normalize_per_cell=True, clip_norm_value=10, hypo_score=False)

adataviz.plotting.has_stats(adata)

adataviz.plotting.interactive_boxplot(adata, variable, gene, obs=None, palette_path=None, title=None, width=1100, height=700, show=True, renderer='notebook')

adataviz.plotting.interactive_dotHeatmap(adata=None, obs=None, genes=None, groupby='Subclass', modality='RNA', title=None, use_raw=False, expression_cutoff='p5', normalize_per_cell=True, clip_norm_value=10, width=900, height=700, gene_order=None, colorscale='greens', vmin='p1', vmax='p99', show=True, reversescale=False, size_min=5, size_max=30, renderer='notebook')

adataviz.plotting.interactive_embedding(adata=None, obs=None, variable=None, gene=None, coord='umap', vmin='p1', vmax='p99', cmap='jet', title=None, width=900, height=750, colors=None, palette_path=None, size=None, show=True, downsample=None, target_fill=0.05, normalize_per_cell=True, clip_norm_value=10, renderer='notebook')

Plot interactive embedding plot with plotly for a given AnnData object or path of .h5ad.

Parameters:

• adata (_type_) – _description_

• obs (_type_, optional) – _description_, by default None

• variable (_type_, optional) – _description_, by default None

• gene (_type_, optional) – _description_, by default None

• coord (str, optional) – _description_, by default “umap”

• vmin (str, optional) – _description_, by default ‘p1’

• vmax (str, optional) – _description_, by default ‘p99’

• cmap (str, optional) – _description_, by default ‘jet’

• title (_type_, optional) – _description_, by default None

• width (int, optional) – _description_, by default 1000

• height (int, optional) – _description_, by default 800

• colors (_type_, optional) – _description_, by default None

• palette_path (_type_, optional) – _description_, by default None

• size (_type_, optional) – _description_, by default None

• target_fill (float, optional) – _description_, by default 0.05

• show (bool, optional) – _description_, by default True

• renderer (str, optional) – _description_, by default “notebook” Available renderers:

• ['plotly_mimetype' – ‘notebook’, ‘notebook_connected’, ‘kaggle’, ‘azure’, ‘colab’, ‘cocalc’, ‘databricks’, ‘json’, ‘png’, ‘jpeg’, ‘jpg’, ‘svg’, ‘pdf’, ‘browser’, ‘firefox’, ‘chrome’, ‘chromium’, ‘iframe’, ‘iframe_connected’, ‘sphinx_gallery’, ‘sphinx_gallery_png’]

• 'jupyterlab' – ‘notebook’, ‘notebook_connected’, ‘kaggle’, ‘azure’, ‘colab’, ‘cocalc’, ‘databricks’, ‘json’, ‘png’, ‘jpeg’, ‘jpg’, ‘svg’, ‘pdf’, ‘browser’, ‘firefox’, ‘chrome’, ‘chromium’, ‘iframe’, ‘iframe_connected’, ‘sphinx_gallery’, ‘sphinx_gallery_png’]

• 'nteract' – ‘notebook’, ‘notebook_connected’, ‘kaggle’, ‘azure’, ‘colab’, ‘cocalc’, ‘databricks’, ‘json’, ‘png’, ‘jpeg’, ‘jpg’, ‘svg’, ‘pdf’, ‘browser’, ‘firefox’, ‘chrome’, ‘chromium’, ‘iframe’, ‘iframe_connected’, ‘sphinx_gallery’, ‘sphinx_gallery_png’]

• 'vscode' – ‘notebook’, ‘notebook_connected’, ‘kaggle’, ‘azure’, ‘colab’, ‘cocalc’, ‘databricks’, ‘json’, ‘png’, ‘jpeg’, ‘jpg’, ‘svg’, ‘pdf’, ‘browser’, ‘firefox’, ‘chrome’, ‘chromium’, ‘iframe’, ‘iframe_connected’, ‘sphinx_gallery’, ‘sphinx_gallery_png’]

:param‘notebook’, ‘notebook_connected’, ‘kaggle’, ‘azure’, ‘colab’,

‘cocalc’, ‘databricks’, ‘json’, ‘png’, ‘jpeg’, ‘jpg’, ‘svg’, ‘pdf’, ‘browser’, ‘firefox’, ‘chrome’, ‘chromium’, ‘iframe’, ‘iframe_connected’, ‘sphinx_gallery’, ‘sphinx_gallery_png’]

Returns:

_description_

Return type:

_type_

adataviz.plotting.pieplot(obs, groupby='Age', palette_path=None, order=None, save=None, explode=0.05)

adataviz.plotting.plot_categorical(adata, ax=None, basis='umap', groupby='MajorType', coding=True, coded_marker=True, save=None, palette_path=None, sheet_name=None, show=True, figsize=(4, 3.5), ncol=None, fontsize=5, legend_fontsize=5, legend_kws=None, legend_title_fontsize=5, marker_fontsize=4, marker_pad=0.1, linewidth=0.5, axis_format='tiny', alpha=0.7, text_kws=None, **kwargs)

adataviz.plotting.plot_gene(adata, obs=None, groupby=None, gene='CADM1', query_str=None, title=None, palette_path=None, hue_norm=None, cbar_kws={'extendfrac': 0.1}, axis_format='tiny', scatter_kws={}, obsm=None, basis='umap', normalize_per_cell=True, stripplot=False, hypo_score=False, ylim=None, clip_norm_value=10, min_cells=3, cmap='parula', prefix=None)

adataviz.plotting.plot_genes(adata='/home/x-wding2/Projects/BICAN/adata/HMBA_v2/HMBA.Group.downsample_1500.h5ad', query_str=None, obs=None, groupby='Subclass', parent_col=None, modality='RNA', use_raw=True, expression_cutoff='p5', genes=None, cell_type_order=None, gene_order=None, row_cluster=False, col_cluster=False, cmap='Greens_r', group_legend=False, parent_legend=False, title=None, palette_path=None, legend_kws={'extend': 'both', 'extendfrac': 0.1, 'label': 'Mean mCG'}, normalize_per_cell=True, clip_norm_value=10, hypo_score=False, figsize=(10, 3.5), marker='o', plot_kws={}, transpose=False, outname='test.pdf')

adataviz.plotting.plot_interacrive_boxplot_from_stats(adata, variable, gene, palette_path=None, title=None, width=1100, height=700)

adataviz.plotting.plot_interactive_boxlot_from_data(adata, obs, variable, gene, palette_path=None, width=1100, height=700, title=None)

adataviz.plotting.plot_pseudotime(pseudotime='dpt_pseudotime.tsv', groupby='Age', y='dpt_pseudotime', hue=None, figsize=(5, 3.5), save=None, rotate=None, ylabel='Pseudotime', palette_path=None)

Plot pseudotime. plot_pseudotime(figsize=(6,3.5),groupby=’MajorType’,

rotate=-45);

plot_pseudotime(figsize=(3.5,3),groupby=’CellClass’) plot_pseudotime(figsize=(3.5,3),groupby=’Age’)

Parameters:

• pseudotime

• groupby

• y

• hue

• figsize

• outdir

• rotate

• palette_path

adataviz.plotting.show_fig(fig, filename='plot')

adataviz.plotting.stacked_barplot(obs='cell_obs_with_annotation.csv', groupby='Age', column='CellClass', x_order=None, y_order=None, linewidth=0.1, palette_path=None, width=None, height=None, xticklabels_kws=None, save=False, lgd_kws=None, gap=0.05, sort_by=None)

Plot stacked barplto to show the cell type composition in each groupby (

such as Age, brain regions and so on.) For example: stacked_barplot(column=’MajorType’,width=3.5,height=6)

stacked_barplot(column=’CellClass’,width=3.5,height=3)

adataviz.plotting.stacked_violinplot(adata, use_genes=None, groupby='Age', cell_groups=None, parent=None, figsize=(6, 4), cmap='viridis')

adataviz.plotting.use_scientific_style()