spateo.plotting.static.dotplot
==============================
.. py:module:: spateo.plotting.static.dotplot
.. autoapi-nested-parse::
Dotplot class adapted from https://github.com/scverse/scanpy with modifications for suitability to cell-cell
communication and interaction analyses
Development notes: some of the methods mention dendrograms/other extra plots and there is currently no capability to
generate those- coming in future update...additions that will have to be made: functions for plot_dendrogram,
plot_totals, additional if condition in make_figure()...
Classes
-------
.. autoapisummary::
spateo.plotting.static.dotplot.Dotplot
spateo.plotting.static.dotplot.CCDotplot
Functions
---------
.. autoapisummary::
spateo.plotting.static.dotplot.adata_to_frame
spateo.plotting.static.dotplot.make_grid_spec
spateo.plotting.static.dotplot.dotplot
Module Contents
---------------
.. py:function:: adata_to_frame(adata: anndata.AnnData, var_names: Sequence[str], cat_key: Union[str, Sequence[str]], num_categories: int = 7, layer: Union[None, str] = None, gene_symbols_key: Union[None, str] = None)
For the purposes of dot plotting, converts the information given in AnnData object to a dataframe in which the
row names are categories defined by groups and column names correspond to variable names.
:param adata: class `anndata.AnnData`
:param var_names: Should be a subset of adata.var_names
:param cat_key: The key(s) in .obs of the grouping to consider. Should be a categorical observation; if not,
will be subdivided into 'num_categories'.
:param num_categories: Only used if groupby observation is not categorical. This value determines the number of groups into
which the groupby observation should be subdivided.
:param layer: Key in .layers specifying layer to use. If not given, will use .X.
:param gene_symbols_key: Key in .var containing gene symbols
.. py:function:: make_grid_spec(ax_or_figsize: Union[Tuple[int, int], matplotlib.axes.Axes], nrows: int, ncols: int, wspace: Optional[float] = None, hspace: Optional[float] = None, width_ratios: Optional[Sequence[float]] = None, height_ratios: Optional[Sequence[float]] = None) -> Tuple[matplotlib.figure.Figure, matplotlib.gridspec.GridSpecBase]
Initialize grid layout to place subplots within a figure environment
:param ax_or_figsize: Either already-existing ax object or the width and height to create a figure window
:param nrows: Number of rows in the grid
:param ncols: Number of columns in the grid
:param wspace: The amount of width reserved for space between subplots, expressed as a fraction of the average axis width
:param hspace: The amount of height reserved for space between subplots, expressed as a fraction of the average axis height
:param width_ratios: Defines the relative widths of the columns. Each column gets a relative width of width_ratios[i] / sum(
width_ratios). If not given, all columns will have the same width.
:param height_ratios: Defines the relative heights of the rows. Each row gets a relative width of height_ratios[i] / sum(
height_ratios). If not given, all columns will have the same width.
:returns: Instantiated Figure object
gs: Instantiated gridspec object
:rtype: fig
.. py:class:: Dotplot(adata: anndata.AnnData, var_names: Sequence[str], cat_key: Union[str, Sequence[str]], num_categories: int = 7, prescale_adata: bool = False, categories_order: Union[None, Sequence[str]] = None, title: Union[None, str] = None, figsize: Union[None, Tuple[float, float]] = None, gene_symbols_key: Union[None, str] = None, var_group_positions: Union[None, Sequence[Tuple[int, int]]] = None, var_group_labels: Union[None, Sequence[str]] = None, var_group_rotation: Union[None, float] = None, layer: Union[None, str] = None, expression_cutoff: float = 0.0, mean_only_expressed: bool = False, standard_scale: Literal['var', 'group'] = None, dot_color_df: Optional[pandas.DataFrame] = None, dot_size_df: Optional[pandas.DataFrame] = None, ax: Union[None, matplotlib.axes.Axes] = None, vmin: Union[None, float] = None, vmax: Union[None, float] = None, vcenter: Union[None, float] = None, norm: Optional[matplotlib.colors.Normalize] = None, **kwargs)
Simultaneous visualization of two variates that are encoded by the dot size and the dot color. Size usually
represents the fraction of samples that have non-zero values, and color usually represents the magnitude of the
value.
:param adata: class `anndata.AnnData`
:param var_names: Should be a subset of adata.var_names
:param cat_key: The key(s) in .obs of the grouping to consider. Should be a categorical observation; if not,
will be subdivided into 'num_categories'.
:param num_categories: Only used if groupby observation is not categorical. This value determines the number of
groups into which the groupby observation should be subdivided.
:param categories_order: Sets order of categories given by 'cat_key' along the plotting axis
:param title: Sets title for figure window
:param figsize: The width and height of a figure
:param gene_symbols_key: Key in .var containing gene symbols
:param var_group_positions: Each item in the list should contain the start and end position that the bracket
should cover. Eg. [(0, 4), (5, 8)] means that there are two brackets, one for the var_names in positions 0-4
and other for positions 5-8.
:param var_group_labels: List of group labels for the variable names (e.g. can group var_names in positions 0-4
as being "group A")
:param var_group_rotation: Rotation in degrees of the variable name labels. If not given, small labels (<4
characters) are not rotated, but otherwise labels are rotated 90 degrees.
:param layer: Key in .layers specifying layer to use. If not given, will use .X.
:param expression_cutoff: Used for binarizing feature expression- feature is considered to be expressed only if
the expression value is greater than this threshold
:param mean_only_expressed: If True, gene expression is averaged only over the cells expressing the given features
:param standard_scale: Whether or not to standardize that dimension between 0 and 1, meaning for each variable or
group, subtract the minimum and divide each by its maximum. 'val' or 'group' is used to specify whether this
should be done over variables or groups.
:param dot_color_df: Pre-prepared dataframe with features as indices, categories as columns, and indices
corresponding to color intensities
:param dot_size_df: Pre-prepared dataframe with features as indices, categories as columns, and indices
corresponding to dot sizes
:param ax: Can be used to provide pre-existing plotting axis
:param vmin: The data value that defines 0.0 in the normalization. Defaults to the min value of the dataset.
:param vmax: The data value that defines 1.0 in the normalization. Defaults to the the max value of the dataset.
:param vcenter: The data value that defines 0.5 in the normalization
:param norm: Optional already-initialized normalizing object that scales data, typically into the interval [0, 1],
for the purposes of mapping to color intensities for plotting. Do not pass both 'norm' and
'vmin'/'vmax', etc.
:param \*\*kwargs: Additional arguments passed to `matplotlib.pyplot.scatter()`
.. py:attribute:: default_colormap
:value: 'winter'
.. py:attribute:: default_color_on
:value: 'dot'
.. py:attribute:: default_dot_max
:value: None
.. py:attribute:: default_dot_min
:value: None
.. py:attribute:: default_smallest_dot
:value: 0.0
.. py:attribute:: default_largest_dot
:value: 200.0
.. py:attribute:: default_dot_edgecolor
:value: 'black'
.. py:attribute:: default_dot_edgelw
:value: 0.2
.. py:attribute:: default_size_exponent
:value: 1.5
.. py:attribute:: default_size_legend_title
:value: Multiline-String
.. raw:: html
Show Value
.. code-block:: python
"""Fraction of cells
in group (%)"""
.. raw:: html
Show Value
.. code-block:: python
"""Mean expression
in group"""
.. raw:: html
Show Value
.. code-block:: python
"""Mean expression
in group"""
.. raw:: html
Show Value
.. code-block:: python
"""Fraction of cells
in group (%)"""
.. raw:: html