alphapepttools.pl.Plots

alphapepttools.pl.Plots#

class alphapepttools.pl.Plots(config={'axes': {'label_size': 10, 'tick_size': 10, 'title_size': 10}, 'default_font': 'Arial', 'font_family': 'sans-serif', 'font_sizes': {'large': 12, 'medium': 10, 'small': 8}, 'highlight_colors': {'general': '#5ec962', 'high': '#9ecae1', 'low': '#fdae6b'}, 'legend': {'font_size': 10, 'title_size': 10}, 'linewidths': {'large': 1.25, 'medium': 0.5, 'small': 0.25}, 'marker_sizes': {'large': 15, 'medium': 10, 'small': 5}, 'na_color': (0.8274509803921568, 0.8274509803921568, 0.8274509803921568, 1.0), 'na_identifiers': ['nan'], 'preset_sizes': {'0.25': 22.5, '0.5': 45, '1': 89, '1.5': 135, '2': 183}, 'resolution': {'dpi': 300}})#

Class for creating figures with matplotlib

Basic configuration for matplotlib plots is loaded from a YAML file and set to generate consistent plots.

Methods table#

barplot(ax, data[, grouping_column, ...])

Plot a bar chart from a DataFrame or AnnData object

boxplot(ax, data[, grouping_column, ...])

Plot a box plot from a DataFrame or AnnData object

histogram(data, value_column[, ...])

Plot a histogram from a DataFrame or AnnData object

plot_pca(data[, x_column, y_column, color, ...])

Plot the PCs of a PCA analysis using the scatter method

plot_pca_loadings(data, ax[, dim_space, ...])

Plot the gene loadings of a PC using the scatter method

plot_pca_loadings_2d(data, ax[, dim_space, ...])

Plot the gene loadings of a PC using the scatter method

rank_median_plot(data, ax[, layer, color, ...])

Plot the ranked protein median intensities across all samples using the scatter method

scatter(data, x_column, y_column[, color, ...])

Plot a scatterplot from a DataFrame or AnnData object

scree_plot(adata, ax[, n_pcs, dim_space, ...])

Plot the eigenvalues of each of the PCs using the scatter method

violinplot(ax, data[, grouping_column, ...])

Plot a violin plot from a DataFrame or AnnData object

Methods#

classmethod Plots.barplot(ax, data, grouping_column=None, value_column=None, direct_columns=None, color=(0.21299500192233756, 0.5114186851211072, 0.730795847750865, 1.0), color_dict=None)#

Plot a bar chart from a DataFrame or AnnData object

Creates a bar plot showing means with error bars (standard deviation) for grouped data. Each bar represents the mean of values within a group, with error bars showing the standard deviation. Bars have semi-transparent fill with opaque black outlines.

Parameters:

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • data (ad.AnnData | pd.DataFrame) – Data containing grouping and value columns or direct columns to plot.

  • grouping_column (list[str] | None, optional) – Column containing the groups to compare. By default None.

  • value_column (list[str] | None, optional) – Column whose values should be plotted. By default None.

  • direct_columns (list[str] | None, optional) – Overrides grouping_column and value_column. Each column becomes a separate bar group. By default None.

  • color (tuple, optional) – Default color for all bars. By default BaseColors.get(“blue”).

  • color_dict (dict | None, optional) – Dictionary mapping group labels to specific colors. Overrides the color parameter for specified groups. By default None.

Return type:

None

Returns:

None

classmethod Plots.boxplot(ax, data, grouping_column=None, value_column=None, direct_columns=None, color=(0.21299500192233756, 0.5114186851211072, 0.730795847750865, 1.0), color_dict=None)#

Plot a box plot from a DataFrame or AnnData object

Creates a box plot showing the distribution of values for grouped data. Each box shows the median, quartiles, and outliers for values within a group. Boxes have semi-transparent fill with opaque black outlines, medians, whiskers, and caps.

Parameters:

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • data (ad.AnnData | pd.DataFrame) – Data containing grouping and value columns or direct columns to plot.

  • grouping_column (list[str] | None, optional) – Column containing the groups to compare. By default None.

  • value_column (list[str] | None, optional) – Column whose values should be plotted. By default None.

  • direct_columns (list[str] | None, optional) – Overrides grouping_column and value_column. Each column becomes a separate box plot. By default None.

  • color (tuple, optional) – Default color for all boxes. By default BaseColors.get(“blue”).

  • color_dict (dict | None, optional) – Dictionary mapping group labels to specific colors. Overrides the color parameter for specified groups. By default None.

Return type:

None

Returns:

None

classmethod Plots.histogram(data, value_column, color_map_column=None, bins=10, ax=None, color='blue', palette=None, color_dict=None, legend=None, hist_kwargs=None, legend_kwargs=None, xlim=None, ylim=None)#

Plot a histogram from a DataFrame or AnnData object

Parameters:

  • data (pd.DataFrame | ad.AnnData) – Data to plot, must contain the value_column and optionally the color_column.

  • value_column (str) – Column in data to plot as histogram. Must contain numeric data.

  • color_map_column (str, optional) – Column in data to use for color encoding. These values are mapped to the palette or the color_dict (see below). Its values cannot contain NaNs, therefore color_map_column is coerced to string and missing values replaced by a default filler string. Overrides color parameter. By default None.

  • bins (int, optional) – Number of bins to use for the histogram. By default 10.

  • color (str, optional) – Color to use for the histogram. By default “blue”.

  • ax (plt.Axes, optional) – Matplotlib axes object to plot on, if None a new figure is created. By default None.

  • palette (list[tuple], optional) – List of colors to use for color encoding, if None a default palette is used. By default None.

  • color_dict (dict[str, str | tuple], optional) – Supercedes palette, a dictionary mapping levels to colors. By default None. If provided, palette is ignored.

  • legend (str | mpl.legend.Legend, optional) – Legend to add to the plot, by default None. If “auto”, a legend is created from the color_column. By default None.

  • hist_kwargs (dict, optional) – Additional keyword arguments for the matplotlib hist function. By default None.

  • legend_kwargs (dict, optional) – Additional keyword arguments for the matplotlib legend function. By default None.

  • xlim (tuple[float, float], optional) – Limits for the x-axis. By default None.

  • ylim (tuple[float, float], optional) – Limits for the y-axis. By default None.

Return type:

None

Returns:

None

classmethod Plots.plot_pca(data, x_column=1, y_column=2, color='blue', color_map_column=None, color_column=None, dim_space='obs', embeddings_name=None, label=False, label_column=None, ax=None, palette=None, color_dict=None, legend=None, scatter_kwargs=None)#

Plot the PCs of a PCA analysis using the scatter method

Parameters:

  • adata (ad.AnnData) – AnnData to plot.

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • pc_x (int) – The PC principal component index to plot on the x axis, by default 1. Corresponds to the principal component order, the first principal is 1 (1-indexed, i.e. the first PC is 1, not 0).

  • pc_y (int) – The principal component index to plot on the y axis, by default 2. Corresponds to the principal component order, the first principal is 1 (1-indexed, i.e. the first PC is 1, not 0).

  • dim_space (str, optional) – The dimension space used in PCA. Can be either “obs” (default) for sample projection or “var” for feature projection. By default “obs”.

  • embeddings_name (str | None, optional) – The custom embeddings name used in PCA (given as input for pca function in embeddings_name ). If None, uses default naming convention. By default None.

  • label (bool,) – Whether to add labels to the points in the scatter plot. by default False.

  • label_column (str | None = None,) – Column in data.obs to use for labeling the points. If None, and label is True, data.obs.index labels are added. By default None.

  • color (str, optional) – Color to use for the scatterplot. By default “blue”.

  • color_map_column (str, optional) – Column in data to use for color encoding. These values are mapped to the palette or the color_dict (see below). Its values cannot contain NaNs, therefore color_map_column is coerced to string and missing values replaced by a default filler string. Overrides color parameter. By default None.

  • color_column (str, optional) – Column in data to plot the colors. This must contain actual color values (RGBA, hex, etc.). Overrides color and color_map_column parameters. By default None.

  • palette (list[str | tuple], optional) – List of colors to use for color encoding, if None a default palette is used. By default None.

  • color_dict (dict[str, str | tuple], optional) – Supercedes palette, a dictionary mapping levels to colors. By default None. If provided, palette is ignored.

  • legend (str | mpl.legend.Legend, optional) – Legend to add to the plot, by default None. If “auto”, a legend is created from the color_column. By default None.

  • scatter_kwargs (dict, optional) – Additional keyword arguments for the matplotlib scatter function. By default None.

Return type:

None

Returns:

None

classmethod Plots.plot_pca_loadings(data, ax, dim_space='obs', embeddings_name=None, dim=1, nfeatures=20, scatter_kwargs=None)#

Plot the gene loadings of a PC using the scatter method

Parameters:

  • data (ad.AnnData) – AnnData to plot.

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • dim_space (str, optional) – The dimension space used in PCA. Can be either “obs” (default) for sample projection or “var” for feature projection. By default “obs”.

  • embeddings_name (str | None, optional) – The custom embeddings name used in PCA. If None, uses default naming convention. By default None.

  • dim (int) – The PC number from which to get loadings, by default 1 (1-indexed, i.e. the first PC is 1, not 0).

  • nfeatures (int) – The number of top absolute loadings features to plot, by default 20

  • scatter_kwargs (dict, optional) – Additional keyword arguments for the matplotlib scatter function. By default None.

Return type:

None

Returns:

None

classmethod Plots.plot_pca_loadings_2d(data, ax, dim_space='obs', embeddings_name=None, pc_x=1, pc_y=2, nfeatures=20, *, add_labels=True, add_lines=False, scatter_kwargs=None)#

Plot the gene loadings of a PC using the scatter method

Parameters:

  • data (ad.AnnData) – AnnData to plot.

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • dim_space (str, optional) – The dimension space used in PCA. Can be either “obs” (default) for sample projection or “var” for feature projection. By default “obs”.

  • embeddings_name (str | None, optional) – The custom embeddings name used in PCA. If None, uses default naming convention. By default None.

  • pc_x (int) – The PC principal component index to plot on the x axis, by default 1. Corresponds to the principal component order, the first principal is 1 (1-indexed, i.e. the first PC is 1, not 0).

  • pc_y (int) – The principal component index to plot on the y axis, by default 2. Corresponds to the principal component order, the first principal is 1 (1-indexed, i.e. the first PC is 1, not 0).

  • nfeatures (int) – The number of top absolute loadings features to label from each component, by default 20

  • add_labels (bool) – Whether to add feature labels of the top nfeatures loadings. by default True.

  • add_lines (bool) – If True, draw lines connecting the origin (0,0) to the points representing the top nfeatures loadings. Default is False.

  • scatter_kwargs (dict, optional) – Additional keyword arguments for the matplotlib scatter function. By default None.

Return type:

None

Returns:

None

classmethod Plots.rank_median_plot(data, ax, layer='X', color='blue', color_map_column=None, color_column=None, palette=None, color_dict=None, legend=None, scatter_kwargs=None)#

Plot the ranked protein median intensities across all samples using the scatter method

Parameters:

  • data (ad.AnnData) – AnnData to plot.

  • ax (plt.Axes) – Matplotlib axes object to plot on, add labels and logscale the y-axis.

  • layer (str) – The AnnData layer to calculate the median value (intensities) across sample. Default is “X”

  • color (str, optional) – Color to use for the scatterplot. By default “blue”.

  • color_map_column (str, optional) – Column in data to use for color encoding. These values are mapped to the palette or the color_dict (see below). Its values cannot contain NaNs, therefore color_map_column is coerced to string and missing values replaced by a default filler string. Overrides color parameter. By default None.

  • color_column (str, optional) – Column in data to plot the colors. This must contain actual color values (RGBA, hex, etc.). Overrides color and color_map_column parameters. By default None.

  • palette (list[str | tuple], optional) – List of colors to use for color encoding, if None a default palette is used. By default None.

  • color_dict (dict[str, str | tuple], optional) – A dictionary mapping levels to colors. By default None. If provided, palette is ignored.

  • legend (str | mpl.legend.Legend, optional) – Legend to add to the plot, by default None. If “auto”, a legend is created from the color_column. By default None.

  • scatter_kwargs (dict, optional) – Additional keyword arguments for the matplotlib scatter function. By default None.

Return type:

None

Returns:

None

classmethod Plots.scatter(data, x_column, y_column, color=None, color_map_column=None, color_column=None, ax=None, palette=None, color_dict=None, legend=None, scatter_kwargs=None, legend_kwargs=None, xlim=None, ylim=None)#

Plot a scatterplot from a DataFrame or AnnData object

Coloring works in three ways, with the following order of precedence: 1. color_column, 2. color_map_column, 3. color. If a color_column is provided, its values are interpreted directly as colors, i.e. they have to be something matplotlib can understand (e.g. RGBA, hex, etc.). If a color_map_column is provided, its values are mapped to colors in combination with palette or color_dict (see color mapping logic below). If neither color_column nor color_map_column is provided, the color parameter is used to color all points the same (defaults to blue).

Color mapping logic#

  • color_map_column is non-numeric:

    • If color_dict is not None: Use color_dict to assign levels of color_map_column to colors (unmapped levels default to grey).

    • If color_dict is None, and palette is not None: Use palette to automatically assign colors to each level.

    • If color_dict is None and palette is None: Use a repeating default palette to assign colors to each level.

  • color_map_column is numeric:

    • If palette is a matplotlib colormap: Numerically map values to colors using the colormap. This means that e.g. 1 and 3 will be closer in color than 1 and 10.

    • If palette is not a matplotlib colormap: Treat numeric values as categorical and color as described above.

  • Examples:

    • color_column=”my_colors”: Points colored by values in “my_colors” column (must contain valid colors)

    • color_map_column=”cell_type”: Categorical mapping of cell types to colors

    • color_map_column=”expression”, palette=plt.cm.viridis: Continuous gradient based on expression values

type data:

DataFrame | AnnData

param data:

Data to plot, must contain the x_column and y_column and optionally the color_column or color_map_column.

type data:

pd.DataFrame | ad.AnnData

type x_column:

str

param x_column:

Column in data to plot on the x-axis. Must contain numeric data.

type x_column:

str

type y_column:

str

param y_column:

Column in data to plot on the y-axis. Must contain numeric data.

type y_column:

str

type color:

Optional[str] (default: None)

param color:

Color to use for the scatterplot. By default “blue”.

type color:

str, optional

type color_map_column:

Optional[str] (default: None)

param color_map_column:

Column in data to use for color encoding. These values are mapped to the palette or the color_dict (see below). Its values cannot contain NaNs, therefore color_map_column is coerced to string and missing values replaced by a default filler string. Overrides color parameter. By default None.

type color_map_column:

str, optional

type color_column:

Optional[str] (default: None)

param color_column:

Column in data to plot the colors. This must contain actual color values (RGBA, hex, etc.). Overrides color and color_map_column parameters. By default None.

type color_column:

str, optional

type ax:

Optional[Axes] (default: None)

param ax:

Matplotlib axes object to plot on, if None a new figure is created. By default None.

type ax:

plt.Axes, optional

type palette:

Optional[list[str | tuple]] (default: None)

param palette:

List of colors to use for color encoding, if None a default palette is used. By default None.

type palette:

list[str | tuple] | matplotlib.colors.Colormap, optional

type color_dict:

Optional[dict[str, str | tuple]] (default: None)

param color_dict:

Supercedes palette, a dictionary mapping levels to colors. By default None. If provided, palette is ignored.

type color_dict:

dict[str, str | tuple], optional

type legend:

Union[str, Legend, None] (default: None)

param legend:

Legend to add to the plot, by default None. If “auto”, a legend is created from the color_column. By default None.

type legend:

str | mpl.legend.Legend, optional

type scatter_kwargs:

Optional[dict] (default: None)

param scatter_kwargs:

Additional keyword arguments for the matplotlib scatter function. By default None.

type scatter_kwargs:

dict, optional

type legend_kwargs:

Optional[dict] (default: None)

param legend_kwargs:

Additional keyword arguments for the matplotlib legend function. By default None.

type legend_kwargs:

dict, optional

type xlim:

Optional[tuple[float, float]] (default: None)

param xlim:

Limits for the x-axis. By default None.

type xlim:

tuple[float, float], optional

type ylim:

Optional[tuple[float, float]] (default: None)

param ylim:

Limits for the y-axis. By default None.

type ylim:

tuple[float, float], optional

rtype:

None

returns:

None

classmethod Plots.scree_plot(adata, ax, n_pcs=20, dim_space='obs', color='blue', embeddings_name=None, scatter_kwargs=None)#

Plot the eigenvalues of each of the PCs using the scatter method

Parameters:

  • adata (ad.AnnData) – AnnData to plot.

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • n_pcs (int,) – number of PCs to plot, by default 20

  • dim_space (str, optional) – The dimension space used in PCA. Can be either “obs” (default) for sample projection or “var” for feature projection. By default “obs”.

  • color (str, optional) – Color to use for the scatterplot. By default “blue”.

  • embeddings_name (str | None, optional) – The custom embeddings name used in PCA. If None, uses default naming convention. By default None.

  • scatter_kwargs (dict, optional) – Additional keyword arguments for the matplotlib scatter function. By default None.

Return type:

None

Returns:

None

classmethod Plots.violinplot(ax, data, grouping_column=None, value_column=None, direct_columns=None, color=(0.21299500192233756, 0.5114186851211072, 0.730795847750865, 1.0), color_dict=None)#

Plot a violin plot from a DataFrame or AnnData object

Creates a violin plot showing the distribution density of values for grouped data. Each violin shows the kernel density estimation of the distribution, along with medians, quartiles, and min/max whiskers. Violins have semi-transparent fill with opaque black outlines and internal statistical markers.

Parameters:

  • ax (plt.Axes) – Matplotlib axes object to plot on.

  • data (ad.AnnData | pd.DataFrame) – Data containing grouping and value columns or direct columns to plot.

  • grouping_column (list[str] | None, optional) – Column containing the groups to compare. By default None.

  • value_column (list[str] | None, optional) – Column whose values should be plotted. By default None.

  • direct_columns (list[str] | None, optional) – Overrides grouping_column and value_column. Each column becomes a separate violin plot. By default None.

  • color (tuple, optional) – Default color for all violins. By default BaseColors.get(“blue”).

  • color_dict (dict | None, optional) – Dictionary mapping group labels to specific colors. Overrides the color parameter for specified groups. By default None.

Return type:

None

Returns:

None

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