adataviz.plotting module

adataviz.plotting.categorical_scatter(data, ax=None, coord_base='umap', x=None, y=None, hue=None, palette='auto', color=None, text_anno=None, text_kws=None, luminance=None, text_transform=None, dodge_text=False, dodge_kws=None, show_legend=False, legend_kws=None, s='auto', size=None, sizes=None, size_norm=None, size_portion=0.95, axis_format='tiny', max_points=50000, labelsize=4, linewidth=0.5, zoomxy=1.05, outline=None, outline_pad=3, alpha=0.7, outline_kws=None, scatter_kws=None, rasterized='auto', coding=False, id_marker=True, legend_color_text=True, rectangle_marker=False, marker_fontsize=4, marker_pad=0.1)

This function was copied from ALLCools and made some modifications. Plot categorical scatter plot with versatile options.

Parameters:

• rasterized – Whether to rasterize the figure.

• return_fig – Whether to return the figure.

• size_portion – The portion of the figure to be used for the size norm.

• data – Dataframe that contains coordinates and categorical variables

• ax – this function do not generate ax, must provide an ax

• coord_base – coords name, if provided, will automatically search for x and y

• x – x coord name

• y – y coord name

• hue (str) – categorical col name or series for color hue.

• palette (str or dict) – palette for color hue.

• color – specify single color for all the dots

• text_anno – categorical col name or series for text annotation.

• text_kws –

  kwargs pass to plt.text, see: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html including bbox, to see parameter for bbox, go to: https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch commonly used parameters are:

  text_kws=dict(fontsize=5,fontweight='black',
                          color='black', # color could be a dict, keys are text to be annotated
                          bbox=dict(boxstyle='round',edgecolor=(0.5, 0.5, 0.5, 0.2),fill=False,
                                                  facecolor=(0.8, 0.8, 0.8, 0.2), # facecolor could also be a dict
                                                  alpha=1,linewidth=0.5)
                          )
  

• text_transform – transform for text annotation.

• dodge_text – whether to dodge text annotation.

• dodge_kws – kwargs for dodge text annotation.

• show_legend – whether to show legend.

• legend_kws – kwargs for legend.

• s – single size value of all the dots.

• size – mappable size of the dots.

• sizes – mapping size to the sizes value.

• size_norm – normalize size range for mapping.

• axis_format – axis format.

• max_points – maximum number of points to plot.

• labelsize – label size pass to ax.text

• linewidth – line width pass to ax.scatter

• zoomxy – zoom factor for x and y-axis.

• outline – categorical col name or series for outline.

• outline_pad – outline padding.

• outline_kws – kwargs for outline.

• scatter_kws – kwargs for scatter.

Returns:

• if return_fig is True, return the figure and axes.

• else, return None.

adataviz.plotting.continuous_scatter(data, ax=None, coord_base='umap', x=None, y=None, scatter_kws=None, hue=None, hue_norm=None, hue_portion=0.95, color=None, cmap='viridis', colorbar=True, size=None, size_norm=None, size_portion=0.95, sizes=None, sizebar=True, text_anno=None, dodge_text=False, dodge_kws=None, text_kws=None, luminance=0.48, text_transform=None, axis_format='tiny', max_points=50000, s='auto', labelsize=6, ticklabel_size=4, linewidth=0.5, zoomxy=1.05, outline=None, outline_kws=None, outline_pad=2, return_fig=False, rasterized='auto', cbar_kws=None, cbar_width=3)

Plot scatter on given adata.

Parameters:

• data (_type_) – _description_

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

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

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

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

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

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

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

• hue_portion (float, optional) – _description_, by default 0.95

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

• cmap (str, optional) – _description_, by default “viridis”

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

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

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

• size_portion (float, optional) – _description_, by default 0.95

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

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

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

• dodge_text (bool, optional) – _description_, by default False

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

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

• luminance (float, optional) – _description_, by default 0.48

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

• axis_format (str, optional) – _description_, by default “tiny”

• max_points (int, optional) – _description_, by default 50000

• s (str, optional) – _description_, by default “auto”

• labelsize (int, optional) – _description_, by default 6

• ticklabel_size (int, optional) – _description_, by default 4

• linewidth (float, optional) – _description_, by default 0.5

• zoomxy (float, optional) – _description_, by default 1.05

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

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

• outline_pad (int, optional) – _description_, by default 2

• return_fig (bool, optional) – _description_, by default False

• rasterized (str, optional) – _description_, by default “auto”

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

• cbar_width (int, optional) – width of colorbar, by default 3 mm

Returns:

_description_

Return type:

_type_

Raises:

• ValueError – _description_

• TypeError – _description_

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

adataviz.plotting.get_cmap(cmap)

adataviz.plotting.get_genes_mean_frac(adata, obs=None, group_col='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, group_col='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='cell_obs_with_annotation.csv', groupby='Age', outdir='figures', palette_path='~/Projects/mouse_pfc/obs/mpfc_color_palette.xlsx', order=None, explode=0.05)

adataviz.plotting.plot_cluster(adata_path, ax=None, coord_base='tsne', cluster_col='MajorType', palette_path=None, coding=True, id_marker=True, output=None, show=True, figsize=(4, 3.5), sheet_name=None, 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)

Plot cluster.

Parameters:

• adata_path

• ax

• coord_base

• cluster_col

• palette_path

• coding

• output

• show

• figsize

• sheet_name

• ncol

• fontsize

• legend_fontsize (int) – legend fontsize, default 5

• legend_kws (dict) – kwargs passed to ax.legend

• legend_title_fontsize (int) – legend title fontsize, default 5

• marker_fontsize (int) – Marker fontsize, default 3 if id_marker is True, and coding is True. legend marker will be a circle (or rectangle) with code

• linewidth (float) – Line width of the legend marker (circle or rectangle), default 0.5

• kwargs (dict) – set text_anno=None to plot clustering without text annotations, coding=True to plot clustering without code annotations, set show_legend=False to remove the legend

adataviz.plotting.plot_gene(adata_path='~/Projects/BG/adata/BG.gene-CGN.h5ad', obs=None, group_col=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, coord_base='umap', normalize_per_cell=True, stripplot=False, hypo_score=False, ylim=None, clip_norm_value=10, min_cells=3, cmap='parula', figdir='figures')

Plot gene expression in a given region or group on embedding of adata.

Parameters:

• adata_path (str, optional) – _description_, by default “~/Projects/BG/adata/BG.gene-CGN.h5ad”

• group_col (str, optional) – _description_, for example: ‘Region’, by default None

• gene (str, optional) – _description_, by default ‘CADM1’

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

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

• palette_path (str, optional) – _description_, by default “~/Projects/BG/obs/HMBA_color_palette.xlsx”

• obsm (str, optional) – _description_, by default “~/Projects/BG/clustering/100kb/annotated.adata.h5ad”

• coord_base (str, optional) – _description_, by default ‘umap’

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

• stripplot (bool, optional) – _description_, by default False

• clip_norm_value (int, optional) – _description_, by default 10

• min_cells (int, optional) – _description_, by default 3

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

• figdir (str, optional) – _description_, by default “figures”

adataviz.plotting.plot_genes(adata_path='/home/x-wding2/Projects/BICAN/adata/HMBA_v2/HMBA.Group.downsample_1500.h5ad', query_str=None, obs=None, group_col='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')

_summary_

Parameters:

• couldbeint (_type_) – _description_

• median (_type_) – _description_

• meanofp5 (_type_) – _description_

• adata_path (str, optional) – _description_, by default “/home/x-wding2/Projects/BICAN/adata/HMBA_v2/HMBA.Group.downsample_1500.h5ad”

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

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

• group_col (str, optional) – _description_, by default ‘Subclass’

• parent_col (str, optional) – _description_, by default ‘Neighborhood’

• modality (str, optional) – _description_, by default ‘RNA’

• p95andsoongenes (_type_, optional) – _description_, by default None

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

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

• row_cluster (bool, optional) – _description_, by default False

• col_cluster (bool, optional) – _description_, by default False

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

• group_legend (bool, optional) – _description_, by default False

• parent_legend (bool, optional) – _description_, by default False

• title (str, optional) – _description_, by default ‘test’

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

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

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

• clip_norm_value (int, optional) – _description_, by default 10

• hypo_score (bool, optional) – _description_, by default False

• figsize (tuple, optional) – _description_, by default (10, 3.5)

• cmap – _description_, by default ‘Greens_r’

• marker (str, optional) – _description_, by default ‘o’

• plot_kws (dict, optional) – _description_, by default {}

• outname (str, optional) – _description_, by default “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), outdir='figures', rotate=None, ylabel='Pseudotime', palette_path='~/Projects/mouse_pfc/obs/mpfc_color_palette.xlsx')

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='~/Projects/mouse_pfc/obs/mpfc_color_palette.xlsx', 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()