pegasus.scatter

pegasus.scatter(data, attrs=None, basis='umap', matkey=None, restrictions=None, show_background=False, fix_corners=True, alpha=1.0, legend_loc='right margin', legend_ncol=None, palettes=None, cmaps='YlOrRd', vmin=None, vmax=None, nrows=None, ncols=None, panel_size=(4, 4), left=0.2, bottom=0.15, wspace=0.4, hspace=0.15, marker_size=None, scale_factor=None, return_fig=False, dpi=300.0, show_neg_for_sig=False, **kwargs)[source]

Generate scatter plots for different attributes

Parameters

  • data (pegasusio.MultimodalData) – Use current selected modality in data.

  • attrs (str or List[str], default: None) – Color scatter plots by attrs. Each attribute in attrs can be one key in data.obs, data.var_names (e.g. one gene) or data.obsm (attribute has the format of ‘obsm_key@component’, like ‘X_pca@0’). If one attribute is categorical, a palette will be used to color each category separately. Otherwise, a color map will be used. If no attributes are provided, plot the basis for all data.

  • basis (str, optional, default: umap) – Basis to be used to generate scatter plots. Can be either ‘umap’, ‘tsne’, ‘fitsne’, ‘fle’, ‘net_tsne’, ‘net_fitsne’, ‘net_umap’ or ‘net_fle’.

  • matkey (str, optional, default: None) – If matkey is set, select matrix with matkey as keyword in the current modality. Only works for MultimodalData or UnimodalData objects.

  • restrictions (str or List[str], optional, default: None) – A list of restrictions to subset data for plotting. There are two types of restrictions: global restriction and attribute-specific restriction. Global restriction appiles to all attributes in attrs and takes the format of ‘key:value,value…’, or ‘key:~value,value…’. This restriction selects cells with the data.obs[key] values belong to ‘value,value…’ (or not belong to if ‘~’ shows). Attribute-specific restriction takes the format of ‘attr:key:value,value…’, or ‘attr:key:~value,value…’. It only applies to one attribute ‘attr’. If ‘attr’ and ‘key’ are the same, one can use ‘.’ to replace ‘key’ (e.g. cluster_labels:.:value1,value2).

  • show_background (bool, optional, default: False) – Only applicable if restrictions is set. By default, only data points selected are shown. If show_background is True, data points that are not selected will also be shown.

  • fix_corners (bool, optional, default: True) – If True, fix the corners of the plots as defined using all data points.

  • alpha (float or List[float], optional, default: 1.0) – Alpha value for blending, from 0.0 (transparent) to 1.0 (opaque). If this is a list, the length must match attrs, which means we set a separate alpha value for each attribute.

  • legend_loc (str or List[str], optional, default: right margin) – Legend location. Can be either “right margin” or “on data”. If a list is provided, set ‘legend_loc’ for each attribute in ‘attrs’ separately.

  • legend_ncol (str, optional, default: None) – Only applicable if legend_loc == “right margin”. Set number of columns used to show legends.

  • palettes (str or List[str], optional, default: None) – Used for setting colors for every categories in categorical attributes. Each string in palettes takes the format of ‘attr:color1,color2,…,colorn’. ‘attr’ is the categorical attribute and ‘color1’ - ‘colorn’ are the colors for each category in ‘attr’ (e.g. ‘cluster_labels:black,blue,red,…,yellow’). If there is only one categorical attribute in ‘attrs’, palletes can be set as a single string and the ‘attr’ keyword can be omitted (e.g. “blue,yellow,red”).

  • cmaps (str or List[str], optional, default: YlOrRd) – Used for setting colormap for numeric attributes. Each string in cmaps takes the format of ‘colormap’ or ‘attr:colormap’. ‘colormap’ sets the default colormap for all numeric attributes. ‘attr:colormap’ overwrites attribute ‘attr’s colormap as ‘colormap’.

  • vmin (float, optional, default: None) – Minimum value to show on a numeric scatter plot (feature plot).

  • vmax (float, optional, default: None) – Maximum value to show on a numeric scatter plot (feature plot).

  • nrows (int, optional, default: None) – Number of rows in the figure. If not set, pegasus will figure it out automatically.

  • ncols (int, optional, default: None) – Number of columns in the figure. If not set, pegasus will figure it out automatically.

  • panel_size (tuple, optional (default: (6, 4))) – The panel size (width, height) in inches.

  • left (float, optional (default: 0.2)) – This parameter sets the figure’s left margin as a fraction of panel’s width (left * panel_size[0]).

  • bottom (float, optional (default: 0.15)) – This parameter sets the figure’s bottom margin as a fraction of panel’s height (bottom * panel_size[1]).

  • wspace (float, optional (default: 0.4)) – This parameter sets the width between panels and also the figure’s right margin as a fraction of panel’s width (wspace * panel_size[0]).

  • hspace (float, optional (default: 0.15)) – This parameter sets the height between panels and also the figure’s top margin as a fraction of panel’s height (hspace * panel_size[1]).

  • marker_size (float, optional (default: None)) – Manually set the marker size in the plot. If None, automatically adjust the marker size to the plot size.

  • scale_factor (float, optional (default: None)) – Manually set the scale factor in the plot if it’s not None. This is used by generating the spatial plots for 10x Visium data.

  • return_fig (bool, optional, default: False) – Return a Figure object if True; return None otherwise.

  • dpi (float, optional, default: 300.0) – The resolution of the figure in dots-per-inch.

  • show_neg_for_sig (bool, optional, default: False) – For signature scores (i.e. attribute type registered as ‘signature’), if we should show negative scores or show them as zeros. Default is False (i.e. show them as zeros).

Returns

A matplotlib.figure.Figure object containing the dot plot if return_fig == True

Return type

Figure object

Examples

>>> pg.scatter(data, attrs=['louvain_labels', 'Channel'], basis='fitsne')
>>> pg.scatter(data, attrs=['CD14', 'TRAC'], basis='umap')