### Install easy_vitessce with pip

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Install the easy_vitessce package. Requires Python 3.10 or greater.

```sh
pip install easy_vitessce
```

--------------------------------

### Create a heatmap with scanpy

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Loads the pbmc68k_reduced dataset and generates a heatmap using sc.pl.heatmap with groupby='bulk_labels' and a list of marker genes. This example is intended to be used with EasyVitessce to add interactivity.

```python
adata = sc.datasets.pbmc68k_reduced()

sc.pl.heatmap(adata, groupby="bulk_labels", var_names=['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ'])
```

--------------------------------

### SpatialData object file structure (no Labels folder)

Source: https://vitessce.github.io/easy_vitessce/index.html

File structure of the SpatialData example above. It has Images, Points, Shapes, and Tables folders but no Labels folder, so pl.render_labels() will not display any data.

```text
SpatialData object, with associated Zarr store:
├── Images
│     └── 'rasterized': DataArray[cyx] (1, 522, 575)
├── Points
│     └── 'single_molecule': DataFrame with shape: (<Delayed>, 3) (2D points)
├── Shapes
│     ├── 'anatomical': GeoDataFrame shape: (6, 1) (2D shapes)
│     └── 'cells': GeoDataFrame shape: (2389, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2389, 268)

```

--------------------------------

### Spatial plot with SpatialData-plot

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Create a spatial plot using SpatialData's mouse brain MERFISH dataset. spatialdata_filepath should lead to a .zarr file containing spatial data with an Images folder. Since the example data has no Labels folder, calling pl.render_labels() will not display any data.

```py
sdata = sd.read_zarr(spatialdata_filepath)
sdata.pl.render_images(element="rasterized").pl.render_shapes(element="cells", color="Acta2").pl.show()
```

--------------------------------

### Pretty-print current configuration with config.pprint

Source: https://vitessce.github.io/easy_vitessce/customization.html

Import the Donfig-based `config` variable and call `pprint()` to display the current configuration in a readable format.

```python
import easy_vitessce as ev

# Pretty-print the current configuration
ev.config.pprint()

```

--------------------------------

### Register on-disk AnnData path with register_data_path

Source: https://vitessce.github.io/easy_vitessce/customization.html

Tell `easy_vitessce` that an AnnData object is already stored on-disk to avoid redundant writes. Provide the path to the `.zarr` or `.h5ad` file.

```python
import easy_vitessce as ev

ev.register_data_path(adata, '/path/to/my_object.adata.zarr')
# or
ev.register_data_path(adata, '/path/to/my_object.h5ad')

```

--------------------------------

### SpatialData object file structure

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

File structure of the SpatialData object used in the spatial plot example. It shows the Images, Points, Shapes, and Tables folders, and notes the absence of a Labels folder.

```text
SpatialData object, with associated Zarr store:
├── Images
│     └── 'rasterized': DataArray[cyx] (1, 522, 575)
├── Points
│     └── 'single_molecule': DataFrame with shape: (<Delayed>, 3) (2D points)
├── Shapes
│     ├── 'anatomical': GeoDataFrame shape: (6, 1) (2D shapes)
│     └── 'cells': GeoDataFrame shape: (2389, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2389, 268)
```

--------------------------------

### Configure scatterplot views with color, size, and linking (easy_vitessce.configure_plots)

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

The first block handles multiple colors (or a list of titles/sizes) by creating one scatterplot per color, linking each to an OBS_SETS or FEATURE_LIST view when the color is in adata.obs or adata.var, then linking zoom/pan, embedding type, radius, colormap range, and colormap across all scatterplots, and finally laying them out in a grid using hconcat/vconcat. The second block is the else branch for a single color or color=None: it creates one scatterplot, optionally adds OBS_SETS or FEATURE_LIST control views based on has_color_in_obs/has_color_in_var, and links the embedding type.

```python
for color_i, color_val in enumerate(color):
    scatterplot_props = {}
    if type(title) == str and color_i == 0:
        scatterplot_props['title'] = title
    elif type(title) == list and len(color) == len(title):
        scatterplot_props['title'] = title[color_i]
    
    scatterplot_view = vc.add_view(vt.SCATTERPLOT, dataset=dataset).set_props(**scatterplot_props)
    scatterplot_views.append(scatterplot_view)

    if type(size) is list and len(size) == len(color):
        vc.link_views(scatterplot_views,
            ["embeddingObsRadiusMode", "embeddingObsRadius"],
            ["manual", size[color_i]]
        )
    
    # Check if color[i] is in obs or var.
    if color_val in adata.obs.columns:
        if obs_sets_view is None:
            obs_sets_view = vc.add_view(vt.OBS_SETS, dataset=dataset)
        
        # TODO: simplify this once https://github.com/vitessce/vitessce/issues/2254 is addressed.
        group_name = color_val.capitalize()
        obs_set_categories = adata.obs[color_val].unique().tolist()
        obs_set_paths = [
            [group_name, str(cat)] for cat in obs_set_categories
        ]

        vc.link_views([scatterplot_view, obs_sets_view],
            ["obsColorEncoding", "obsSetSelection", "obsSetExpansion"],
            ["cellSetSelection", obs_set_paths, [[group_name]]]
        )
    elif color_val in adata.var.index:
        if feature_list_view is None:
            feature_list_view = vc.add_view(vt.FEATURE_LIST, dataset=dataset)
        vc.link_views([scatterplot_view, feature_list_view],
            ["featureSelection", "obsColorEncoding"],
            [[color_val], "geneSelection"]
        )

# Link the zoom/pan interactions across all scatterplot views.
vc.link_views(scatterplot_views,
    ["embeddingZoom", "embeddingTargetX", "embeddingTargetY"],
    [None, None, None]
)

# Other linkings that are common across all scatterplot views.
vc.link_views(scatterplot_views,
    ["embeddingType"],
    [basis_name]
)

if size is not None and type(size) is not list:
    vc.link_views(scatterplot_views,
        ["embeddingObsRadiusMode", "embeddingObsRadius"],
        ["manual", size]
    )

if vmin is not None or vmax is not None:
    vc.link_views(scatterplot_views,
        ["featureValueColormapRange"],
        [
            [
                vmin if vmin is not None else 0.0,
                vmax if vmax is not None else 1.0,
            ]
        ]
    )

if color_map is not None and color_map in ["viridis", "plasma", "jet", "greys"]:
    vc.link_views(scatterplot_views,
        ["featureValueColormap"],
        [color_map]
    )

# Layout the views.
all_views = (
    scatterplot_views
    + ([obs_sets_view] if obs_sets_view is not None else [])
    + ([feature_list_view] if feature_list_view is not None else [])
)
rows_of_views = []

for view_i, view in enumerate(all_views):
    if view_i % ncols == 0:
        # Start a new row.
        rows_of_views.append([view])
    else:
        rows_of_views[-1].append(view)

# Combine rows.
row_layouts = [hconcat(*row) for row in rows_of_views]
vc.layout(vconcat(*row_layouts))
```

```python
else:
    # Single color or color=None: single scatterplot view.
    scatterplot_props = {} if title is None else { 'title': title }
    scatterplot_view = vc.add_view(vt.SCATTERPLOT, dataset=dataset).set_props(**scatterplot_props)
    control_views = []
    if has_color_in_obs:
        obs_sets_view = vc.add_view(vt.OBS_SETS, dataset=dataset)
        control_views.append(obs_sets_view)
    if has_color_in_var:
        feature_list_view = vc.add_view(vt.FEATURE_LIST, dataset=dataset)
        control_views.append(feature_list_view)
    
    # Link views.
    vc.link_views([scatterplot_view],
        ["embeddingType"],
        [basis_name]
    )
```

--------------------------------

### Enable or disable plots with configure_plots

Source: https://vitessce.github.io/easy_vitessce/_sources/customization.md

Use `configure_plots` to enable or disable specific plotting functions. Alternatively, use `enable_plots` or `disable_plots` for simpler toggling. The example shows enabling 'embedding' and disabling 'violin' and 'heatmap'.

```python
import easy_vitessce as ev

# Enable or disable particular plotting functions
ev.configure_plots(enable_plots=["embedding"], disable_plots=["violin", "heatmap"])
# or
ev.enable_plots(["embedding"])
# or
ev.disable_plots(["violin", "heatmap"])
```

--------------------------------

### Choose widget display function with config.update

Source: https://vitessce.github.io/easy_vitessce/customization.html

Set `widget_function` to `'display'` to use `VitessceConfig.display` instead of the default `VitessceConfig.widget` for showing the widget.

```python
import easy_vitessce as ev

ev.config.update({ 'widget_function': 'display' })

```

--------------------------------

### Heatmap with sc.pl.heatmap

Source: https://vitessce.github.io/easy_vitessce/index.html

Create a heatmap using Easy Vitessce's heatmap function.

```python
adata = sc.datasets.pbmc68k_reduced()

sc.pl.heatmap(adata, groupby="bulk_labels", var_names=['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ'])

```

--------------------------------

### Configure widget parameters with config.update or config.set

Source: https://vitessce.github.io/easy_vitessce/customization.html

Update the `widget` configuration to set parameters passed to `VitessceConfig.widget`. Use `config.update` or `config.set` with the same effect.

```python
import easy_vitessce as ev

ev.config.update({ 'widget': { 'js_dev_mode': True } })
# or
ev.config.set({ 'widget': { 'js_dev_mode': True } })

```

--------------------------------

### Configure scatterplot view linking and layout

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

Configures view linking and layout for a scatterplot with optional controls. Applies size, colormap range, color encoding, and layout based on conditions. Requires variables like size, vmin, vmax, has_color_in_obs, has_color_in_var, color_map, and control_views to be defined.

```python
if size is not None:
    vc.link_views([scatterplot_view],
        ["embeddingObsRadiusMode", "embeddingObsRadius"],
        ["manual", size]
    )

if vmin is not None or vmax is not None:
    vc.link_views([scatterplot_view],
        ["featureValueColormapRange"],
        [
            [
                vmin if vmin is not None else 0.0,
                vmax if vmax is not None else 1.0,
            ]
        ]
    )

if has_color_in_obs:
    vc.link_views([scatterplot_view, *control_views],
        ["obsColorEncoding"],
        ["cellSetSelection"]
    )
    vc.link_views([obs_sets_view],
        ["obsSetExpansion"],
        [[[obs_set_names[0]]]]
    )

if has_color_in_var:
    vc.link_views([scatterplot_view, *control_views],
        ["featureSelection", "obsColorEncoding"],
        [[color[0]], "geneSelection"]
    )

if color_map is not None and color_map in ["viridis", "plasma", "jet", "greys"]:
    vc.link_views([scatterplot_view],
        ["featureValueColormap"],
        [color_map]
    )

# Layout views.
if len(control_views) > 0:
    vc.layout(hconcat(scatterplot_view, vconcat(*control_views), split=[2, 1]))
else:
    vc.layout(scatterplot_view)
```

--------------------------------

### Access VitessceConfig from widget

Source: https://vitessce.github.io/easy_vitessce/advanced.html

Shows how to access the VitessceConfig instance from the `vw` variable, including the static config, its dictionary representation, and the live config.

```python
# Access the configuration powering this widget instance.
vw.config
# or
vw.config.to_dict(base_url="")
# or, for "live" dynamically-updated config
vw._config

```

--------------------------------

### Register existing data path for AnnData

Source: https://vitessce.github.io/easy_vitessce/_sources/customization.md

Avoid redundant writing of AnnData objects by registering an existing on-disk path. Use `register_data_path` with either a `.zarr` or `.h5ad` path.

```python
import easy_vitessce as ev

ev.register_data_path(adata, '/path/to/my_object.adata.zarr')
# or
ev.register_data_path(adata, '/path/to/my_object.h5ad')
```

--------------------------------

### heatmap(adata, var_names, groupby, *, gene_symbols=None, layer=None, swap_axes=False, vmin=None, vmax=None, **kwargs)

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

Creates an interactive heatmap using Vitessce. It takes an AnnData object and parameters to configure the heatmap display.

```APIDOC
## heatmap(adata, var_names, groupby, *, gene_symbols=None, layer=None, swap_axes=False, vmin=None, vmax=None, **kwargs)

### Description
Creates an interactive heatmap using Vitessce. It takes an AnnData object and parameters to configure the heatmap display.

### Parameters
- **adata** (AnnData) - Required - AnnData object.
- **var_names** (list[str]) - Required - List of genes.
- **groupby** (str) - Required - Category group, a column in adata.obs.
- **gene_symbols** (str) - Optional - Key in adata.var to use for gene symbols, if different from adata.var.index.
- **layer** (str) - Optional - Layer in AnnData to use for expression values. Defaults to None, which uses adata.X.
- **swap_axes** (bool) - Optional - If True, transpose the heatmap. Defaults to False.
- **vmin** (float) - Optional - Minimum value for color map scaling.
- **vmax** (float) - Optional - Maximum value for color map scaling.
- **color_map** (str) - Optional - Color map (viridis, plasma, jet). Defaults to viridis.

### Returns
- Vitessce widget. Documentation can be found `here. <https://python-docs.vitessce.io/api_config.html#vitessce-widget>`_
```

--------------------------------

### Spatial plot with Scanpy

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Create a spatial plot using Scanpy's Visium dataset. Uses easy_vitessce's spatial function with Scanpy syntax.

```py
adata = sc.datasets.visium_sge(sample_id="Targeted_Visium_Human_Glioblastoma_Pan_Cancer", include_hires_tiff=True)

sc.pl.spatial(adata, color = "log1p_n_genes_by_counts")
```

--------------------------------

### Configure spatial plots with VitessceConfig and AnnDataWrapper

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

Configures AnnData and image wrappers for spatial data, handling gene vs categorical color inputs, and adds views for feature list, histogram, spatial, and layer controller. Includes commented-out code for multiple colors and spot layer configuration.

```python
    if (type(color) == list and color[0] in adata.var.index) or (type(color) == str and color in adata.var.index): # gene
        # genes = kwargs["color"]
        # adata.var["genes"] = list(adata.var.index)
        # adata.var["in_color"] = adata.var["genes"].apply(lambda gene: True if gene in color else False)
        
        path = {"obs_feature_matrix_path": "X"}
        #path = {"feature_filter_path": ["var/in_color"]}
        new_coord_vals = {"featureType": 'gene', "featureValueType": 'expression'}
        # new_coord_vals = {"featureType": 'qualityMetric', "featureValueType": 'value'}
        adata_wrapper_dict.update(path)
        adata_wrapper_dict["coordination_values"].update(new_coord_vals)
        print(adata_wrapper_dict)
        
    elif (type(color) == list and color[0] in adata.obs.columns) or (type(color) == str and color in adata.obs.columns): # categorical
        adata.obs[color] = adata.obs[color].astype("float32")
        
        path = {"obs_feature_column_paths":[f"obs/{color}"]}
        new_coord_vals = {"obsType": 'cell', "featureType": 'qualityMetric', "featureValueType": 'value'}
        adata_wrapper_dict.update(path)
        adata_wrapper_dict["coordination_values"].update(new_coord_vals)
        print(adata_wrapper_dict)
    
    adata.write_zarr(output_adata, chunks=[adata.shape[0], VAR_CHUNK_SIZE])

    # obs_feature_column_paths=[f"obs/{color}"],
    # feature_filter_path=[f"obs/{color}"],
        
        
    vc = VitessceConfig(schema_version="1.0.18", name="sc.pl.spatial (deprecated)")
    dataset = vc.add_dataset(name='spatial data').add_object(
        AnnDataWrapper(
            adata_path=output_adata,
            obs_spots_path = "obsm/spatial", 
            obs_feature_matrix_path = "X"
            # obs_set_paths = ["obs/log1p_n_genes_by_counts"]
        )
    ).add_object(
        ImageOmeZarrWrapper(
            img_path=output_img,
            coordinate_transformations = [
                {
                    "type": 'translation',
                    "translation": [0, 0, 0],
                },
                {
                    "type": 'scale',
                    "scale": [1, x, y],
                    # [color, x, y]
                },
            ]
        )
    ).add_object(
        AnnDataWrapper(**adata_wrapper_dict)
    )
        # adata_path=output_adata,
        # obs_feature_column_paths=[f"obs/{color}"], # for numerical data
        # # obs_feature_matrix_path = []
        # coordination_values={
        #     "obsType": 'cell',
        #     "featureType": 'qualityMetric',
        #     "featureValueType": 'value',
        # }
    link_views_dict = {
        "obsType": 'cell',
        "featureSelection": [color],
        "obsColorEncoding": "geneSelection" # ??
    }

    if color in adata.var.index: # gene
        genes = vc.add_view(vt.FEATURE_LIST, dataset=dataset) #assumes featureType = gene
        
    if color in adata.obs.columns:
        histogram = vc.add_view(vt.FEATURE_VALUE_HISTOGRAM, dataset=dataset)

    # if type(color) == list and len(color) > 1:
    #     for i in range(0, len(color), 2):
    #         adata.obs[color[i]] = adata.obs[color[i]].astype("float32")
    #         path = {"obs_feature_column_paths":[f"obs/{color[i]}"]}
    #         new_coord_vals = {"obsType": 'cell', "featureType": 'qualityMetric', "featureValueType": 'value'}
    #         adata_wrapper_dict.update(path)
    #         adata_wrapper_dict["coordination_values"].update(new_coord_vals)
    #         # print(adata_wrapper_dict)
    #         spotLayer = {
    #         "spotLayer": CL([
    #             {
    #                 "obsType": "cell",
    #                 "spatialSpotRadius": 45, #might have to depend on scale factor as well
    #                 "featureValueColormap": color_map
    #             },
    #         ]) }
    #         spatial_view_1 = vc.add_view("spatialBeta", dataset=dataset)
    #         spatial_view_2 = vc.add_view("spatialBeta", dataset=dataset)
    
    #         vc.link_views_by_dict([spatial_view_1], **spotLayer, **link_views_dict)
    #         vc.link_views_by_dict([spatial_view_2],  **spotLayer, **link_views_dict)
            
    #         vc.layout(spatial_view_1 | spatial_view_2)
    
    #     vw = _to_widget(vc)
    #     return vw

    
    spatial_view = vc.add_view("spatialBeta", dataset=dataset)
    lc_view = vc.add_view("layerControllerBeta", dataset=dataset)

    
    if color in adata.obs.columns: # categorical
        new_vals = {"featureType": 'qualityMetric', "featureValueType": 'value'}
        link_views_dict.update(new_vals)
        print(link_views_dict)
```

--------------------------------

### Set custom output directory with data.out_dir

Source: https://vitessce.github.io/easy_vitessce/customization.html

Specify an alternative output directory for data files. The default is `data`.

```python
import easy_vitessce as ev

ev.config.update({ 'data.out_dir': '/path/to/some/dir' })

```

--------------------------------

### Create interactive heatmap with VitessceConfig

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

Creates an interactive heatmap using VitessceConfig. It copies the AnnData object, adds a boolean mask for initial feature filtering, configures an OBS_SETS view and a HEATMAP view, links views for color map and range, and lays them out horizontally.

```python
def heatmap(
        adata,
        var_names,
        groupby,
        *,
        gene_symbols=None,
        layer=None,
        swap_axes=False,
        vmin=None,
        vmax=None,
        **kwargs
    ):
    """
    Creates interactive heatmap.

    :param AnnData adata: AnnData object.
    :param list[str] var_names: List of genes.
    :param str groupby: Category group, a column in adata.obs.
    :param str gene_symbols: Key in adata.var to use for gene symbols, if different from adata.var.index.
    :param str layer: Layer in AnnData to use for expression values. Defaults to None, which uses adata.X.
    :param bool swap_axes: If True, transpose the heatmap. Defaults to False.
    :param float vmin: Minimum value for color map scaling.
    :param float vmax: Maximum value for color map scaling.
    :param str color_map: Color map (viridis, plasma, jet). Defaults to viridis.
    :returns: Vitessce widget. Documentation can be found `here. <https://python-docs.vitessce.io/api_config.html#vitessce-widget>`_ 
    """

    # TODO: throw errors/warnings for unsupported parameters?

    vc =  VitessceConfig(schema_version="1.0.18", name='sc.pl.heatmap')

    # We need to make a copy since we will be modifying adata.
    # TODO: this will mean the object will always be re-written to disk
    # (and redundantly if the function is called multiple times),
    # and breaking the functionality of register_data_path() if the user had specified an existing on-disk location.
    # Workarounds for future work (TODO: create github issues):
    # - Create a new AnnData object (or CSV file) to store var/__ev_initial_feature_filter__ column only.
    # - In JS, implement a way to specify a list of features directly rather than via a path in the AnnData object.
    adata = adata.copy()

    # We need to create a boolean mask in adata.var to indicate which genes to show initially.
    # We name it using double underscores to reduce the chance of name collisions.
    adata.var["__ev_initial_feature_filter__"] = adata.var_names.to_series().apply(lambda gene: True if gene in var_names else False)

    group_name = groupby.capitalize()

    wrapper_params = {
        "obs_set_paths": [f"obs/{groupby}"],
        "obs_set_names": [group_name],
        "obs_feature_matrix_path": "X" if layer is None else f"layers/{layer}",
        "initial_feature_filter_path": "var/__ev_initial_feature_filter__",
    }

    if gene_symbols is not None:
        wrapper_params["feature_labels_path"] = f"var/{gene_symbols}"

    dataset = vc.add_dataset(name='heatmap data').add_object(AnnDataWrapper(
        **_get_adata_wrapper_params(adata),
        **wrapper_params,
    ))

    obs_sets_view = vc.add_view(vt.OBS_SETS, dataset=dataset)
    heatmap_view = vc.add_view(vt.HEATMAP, dataset=dataset).set_props(transpose=(not swap_axes))

    if "color_map" in kwargs:
        # TODO: color_map is not listed as a parameter for sc.pl.heatmap in Scanpy docs.
        # Should we still keep it here?
        color_map = kwargs["color_map"]
        vc.link_views([heatmap_view, obs_sets_view], 
            ["featureValueColormap"],
            [color_map]
        )
    
    vc.link_views([obs_sets_view], 
        ['obsSetExpansion'],
        [[[group_name]]]
    )

    if vmin is not None or vmax is not None:
        vc.link_views(
            [heatmap_view, obs_sets_view], 
            ["featureValueColormapRange"],
            [
                [
                    vmin if vmin is not None else 0.0,
                    vmax if vmax is not None else 1.0,
                ]
            ]
        )

    vc.layout(hconcat(heatmap_view, obs_sets_view, split=[2, 1]))
    vw = _to_widget(vc)
    return vw
```

--------------------------------

### render_labels

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/spatialdata_plot.html

Renders label data with options for coloring, grouping, and alpha values. Returns self for chaining.

```APIDOC
## render_labels

### Description
Renders label data. This method allows users to visualize label elements from spatial data, with options for coloring by observation columns, grouping, and adjusting transparency.

### Method
render_labels

### Signature
render_labels(element=None, color=None, groups=None, palette=None, cmap=None, norm=None, outline_alpha=0.0, fill_alpha=0.4, table_name=None, table_layer=None, **kwargs)

### Parameters
- **element** (str) - Optional - Name of the labels element.
- **color** (str or None) - Optional - Name of an obs column, var index value, or color-like string.
- **groups** (list[str] or str or None) - Optional - List of obs group names to select.
- **palette** (list[str] or str or None) - Optional - Palette to color labels. If list, the number of colors should be equal to the number of groups.
- **cmap** (str or None) - Optional - Quantitative colormap name, such as "viridis".
- **norm** (matplotlib.colors.Normalize or None) - Optional - Normalization for quantitative colormap.
- **outline_alpha** (float or int or None) - Optional - Alpha value for label outlines, between 0.0 and 1.0. Default is 0.0.
- **fill_alpha** (float or int or None) - Optional - Alpha value for filling labels, between 0.0 and 1.0. Default is 0.4.
- **table_name** (str or None) - Optional - Name of an annotating table to use for coloring.
- **table_layer** (str or None) - Optional - Name of the layer in the annotating table to use for coloring.
- **kwargs** - Optional - Additional keyword arguments passed to the underlying spatialdata-plot method.

### Returns
Self, allowing for chaining.

### Example
```python
plot_accessor.render_labels(element='labels', color='cell_type', groups=['A', 'B'], palette=['red', 'blue'])
```
```

--------------------------------

### Dotplot with sc.pl.dotplot

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Create a dotplot. To select/deselect multiple genes, hold SHIFT while clicking on genes in the Gene List.

```py
adata = sc.datasets.pbmc68k_reduced()

sc.pl.dotplot(adata, var_names=["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"], groupby="bulk_labels")
```

--------------------------------

### Set AnnData output format to h5ad

Source: https://vitessce.github.io/easy_vitessce/customization.html

Change the default AnnData write format from `AnnData.write_zarr` to `AnnData.write_h5ad` by setting `data.anndata_format` to `'h5ad'`.

```python
import easy_vitessce as ev

ev.config.update({ 'data.anndata_format': 'h5ad' })

```

--------------------------------

### Import easy_vitessce as ev

Source: https://vitessce.github.io/easy_vitessce/_sources/index.md

Import the easy_vitessce package. By default, interactive plots are enabled via running this import statement.

```py
import easy_vitessce as ev
```

--------------------------------

### VitesscePlotAccessor.show()

Source: https://vitessce.github.io/easy_vitessce/easy_vitessce.html

Displays the spatial plot as a Vitessce widget.

```APIDOC
## VitesscePlotAccessor.show()

### Description
Displays the spatial plot as a Vitessce widget.

### Method
show

### Parameters
- **coordinate_systems** (_optional_) - Optional - Coordinate systems to display.
- **kwargs** - Optional - Additional keyword arguments.

### Returns
Vitessce widget. Learn more at the vitessce-python docs.
```

--------------------------------

### dotplot

Source: https://vitessce.github.io/easy_vitessce/_modules/easy_vitessce/configure_plots.html

Creates an interactive dotplot from an AnnData object. It configures a Vitessce widget with linked views for observation sets, feature list, and dot plot, and returns the widget.

```APIDOC
## dotplot

### Description
Creates an interactive dotplot from an AnnData object. It configures a Vitessce widget with linked views for observation sets, feature list, and dot plot, and returns the widget.

### Method
Function

### Signature
```python
def dotplot(
    adata,
    var_names,
    groupby,
    *,
    expression_cutoff=None,
    title=None,
    gene_symbols=None,
    layer=None,
    cmap=None,
    swap_axes=False,
    **kwargs,
)
```

### Parameters
- **adata** (AnnData) - Required - AnnData object.
- **var_names** (list[str]) - Required - List of gene IDs from adata.var.index.
- **groupby** (str) - Required - Category group, a column in adata.obs.
- **expression_cutoff** (float) - Optional - Expression cutoff for dot display.
- **title** (str) - Optional - Title of the plot.
- **gene_symbols** (str) - Optional - Key in adata.var to use for gene symbols, if different from adata.var.index.
- **layer** (str) - Optional - Layer in AnnData to use for expression values. Defaults to None, which uses adata.X.
- **cmap** (str) - Optional - Color map (viridis, plasma, jet). Defaults to viridis.
- **swap_axes** (bool) - Optional - If True, transpose the dotplot. Defaults to False.

### Returns
- **Vitessce widget** - Documentation can be found [here](https://python-docs.vitessce.io/api_config.html#vitessce-widget).
```

--------------------------------

### enable_plots(plots=None)

Source: https://vitessce.github.io/easy_vitessce/easy_vitessce.html

Activates interactive Vitessce plots. If no plots are specified, enables all plots.

```APIDOC
## enable_plots(plots=None)

### Description
Activates interactive Vitessce plots.

### Method
Function call

### Endpoint
easy_vitessce.configure_plots.enable_plots

### Parameters
#### Parameters
- **plots** (list[str]) - Optional - List of plots to enable. If None, enables all plots.

### Returns
None
```