diff --git a/docs/api_examples/scatter_plot.ipynb b/docs/api_examples/scatter_plot.ipynb
index bc8f871..1f5906e 100644
--- a/docs/api_examples/scatter_plot.ipynb
+++ b/docs/api_examples/scatter_plot.ipynb
@@ -303,16 +303,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ade445fe",
+   "id": "dbb5c1e3",
    "metadata": {},
    "source": [
-    "## 1. Basic Scatter Plot"
+    "### 0.3. Create sample data\n",
+    "We create a synthetic dataset that contains simulated gene expression values, p-values, regulation status, and significance scores for 8 genes across two cell types."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "19f0277d",
+   "id": "82dfaf45",
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +450,6 @@
     }
    ],
    "source": [
-    "# Created sample data\n",
     "sample_df = pd.DataFrame(\n",
     "    {\n",
     "        \"gene_expression\": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],\n",
@@ -473,6 +473,15 @@
     "sample_df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ade445fe",
+   "metadata": {},
+   "source": [
+    "## 1. Basic Scatter Plot\n",
+    "A basic scatter plot can be created by providing the `x` and `y` columns from the DataFrame, along with style options like `title`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -483,7 +492,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[VueCore] Plot saved to ./outputs/scatter_basic.pdf\n"
+      "[VueCore] Plot saved to ./outputs/scatter_basic.png\n"
      ]
     },
     {
@@ -4381,9 +4390,9 @@
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Gene Expression Scatter Plot\"},\"height\":600,\"width\":800,\"hovermode\":\"closest\"},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('f6922920-e7a5-4502-a284-ef60b31d646b');\n",
+       "var gd = document.getElementById('a55bfe8d-a745-4cb1-aeba-838261d3099d');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4414,7 +4423,7 @@
    ],
    "source": [
     "# Define output path\n",
-    "file_path_png = os.path.join(output_dir, \"scatter_basic.pdf\")\n",
+    "file_path_png = os.path.join(output_dir, \"scatter_basic.png\")\n",
     "\n",
     "# Generate basic plot\n",
     "fig = create_scatter_plot(\n",
@@ -4433,12 +4442,13 @@
    "id": "f5e16637",
    "metadata": {},
    "source": [
-    "## 2. Advanced Scatter Plot"
+    "## 2. Advanced Scatter Plot\n",
+    "An example of an advanced scatter plot that includes grouping by a categorical variable, color mapping, text annotations, and adding several styling options."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "f9307e85",
    "metadata": {},
    "outputs": [
@@ -4452,9 +4462,9 @@
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Fold Change\"},\"type\":\"linear\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Log P-value\"},\"type\":\"linear\"},\"legend\":{\"title\":{\"text\":\"Regulation Status\"},\"tracegroupgap\":0,\"itemsizing\":\"constant\"},\"margin\":{\"t\":60},\"title\":{\"subtitle\":{\"text\":\"Visualizing Gene Expression with Regulation and Significance\"},\"text\":\"Advanced Gene Expression Scatter Plot\"},\"height\":600,\"width\":900,\"hovermode\":\"closest\"},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('518f2589-134c-47c8-b4ff-3ac49a508b42');\n",
+       "var gd = document.getElementById('bb32b2ba-1265-4971-9c7b-55c4612224ae');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4492,14 +4502,20 @@
     "    data=sample_df,\n",
     "    x=\"gene_expression\",\n",
     "    y=\"log_p_value\",\n",
-    "    group=\"regulation\",\n",
+    "    color=\"regulation\",\n",
     "    size=\"significance_score\",\n",
     "    text=\"gene_name\",\n",
     "    title=\"Advanced Gene Expression Scatter Plot\",\n",
-    "    x_title=\"Log2 Fold Change\",\n",
-    "    y_title=\"-Log10(P-value)\",\n",
-    "    colors={\"Up\": \"#FF5733\", \"Down\": \"#3380FF\", \"None\": \"#33FF57\"},\n",
-    "    marker_opacity=0.8,\n",
+    "    subtitle=\"Visualizing Gene Expression with Regulation and Significance\",\n",
+    "    labels={\n",
+    "        \"gene_expression\": \"Log2 Fold Change\",\n",
+    "        \"log_p_value\": \"Log P-value\",\n",
+    "        \"regulation\": \"Regulation Status\",\n",
+    "        \"significance_score\": \"Significance Score\",\n",
+    "        \"gene_name\": \"Gene Name\",\n",
+    "    },\n",
+    "    color_discrete_map={\"Up\": \"#508AA8\", \"Down\": \"#A8505E\", \"None\": \"#838383\"},\n",
+    "    opacity=0.8,\n",
     "    marker_line_width=1,\n",
     "    marker_line_color=\"darkgray\",\n",
     "    width=900,\n",
diff --git a/docs/api_examples/scatter_plot.py b/docs/api_examples/scatter_plot.py
index 2f8d7f9..f9cc861 100644
--- a/docs/api_examples/scatter_plot.py
+++ b/docs/api_examples/scatter_plot.py
@@ -84,10 +84,10 @@
 pio.renderers.default = "notebook"
 
 # %% [markdown]
-# ## 1. Basic Scatter Plot
+# ### 0.3. Create sample data
+# We create a synthetic dataset that contains simulated gene expression values, p-values, regulation status, and significance scores for 8 genes across two cell types.
 
 # %%
-# Created sample data
 sample_df = pd.DataFrame(
     {
         "gene_expression": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],
@@ -110,6 +110,10 @@
 
 sample_df
 
+# %% [markdown]
+# ## 1. Basic Scatter Plot
+# A basic scatter plot can be created by providing the `x` and `y` columns from the DataFrame, along with style options like `title`.
+
 # %%
 # Define output path
 file_path_png = os.path.join(output_dir, "scatter_basic.png")
@@ -127,6 +131,7 @@
 
 # %% [markdown]
 # ## 2. Advanced Scatter Plot
+# An example of an advanced scatter plot that includes grouping by a categorical variable, color mapping, text annotations, and adding several styling options.
 
 # %%
 # Define output path
@@ -137,14 +142,20 @@
     data=sample_df,
     x="gene_expression",
     y="log_p_value",
-    group="regulation",
+    color="regulation",
     size="significance_score",
     text="gene_name",
     title="Advanced Gene Expression Scatter Plot",
-    x_title="Log2 Fold Change",
-    y_title="-Log10(P-value)",
-    colors={"Up": "#FF5733", "Down": "#3380FF", "None": "#33FF57"},
-    marker_opacity=0.8,
+    subtitle="Visualizing Gene Expression with Regulation and Significance",
+    labels={
+        "gene_expression": "Log2 Fold Change",
+        "log_p_value": "Log P-value",
+        "regulation": "Regulation Status",
+        "significance_score": "Significance Score",
+        "gene_name": "Gene Name",
+    },
+    color_discrete_map={"Up": "#508AA8", "Down": "#A8505E", "None": "#838383"},
+    opacity=0.8,
     marker_line_width=1,
     marker_line_color="darkgray",
     width=900,
diff --git a/src/vuecore/engines/plotly/scatter.py b/src/vuecore/engines/plotly/scatter.py
index e90c36d..9af6bdb 100644
--- a/src/vuecore/engines/plotly/scatter.py
+++ b/src/vuecore/engines/plotly/scatter.py
@@ -10,46 +10,73 @@
 
 def build(data: pd.DataFrame, config: ScatterConfig) -> go.Figure:
     """
-    Creates a Plotly scatter plot figure from a DataFrame and configuration.
+    Creates a Plotly scatter plot from a DataFrame and a Pydantic configuration.
 
     This function acts as a bridge between the abstract plot definition and the
-    Plotly Express implementation. It translates the validated configuration
-    into the arguments for `plotly.express.scatter`.
+    Plotly Express implementation. It translates the validated `ScattereConfig`
+    into the arguments for `plotly.express.scatter` and also forwards any
+    additional, unvalidated keyword arguments from plotly. The resulting figure
+    is then customized with layout and theme settings using `plotly.graph_objects`.
+    (https://plotly.com/python-api-reference/generated/plotly.express.scatter.html).
 
     Parameters
     ----------
     data : pd.DataFrame
         The DataFrame containing the plot data.
     config : ScatterConfig
-        The validated Pydantic model object containing all plot configurations.
+        The validated Pydantic model object with all plot configurations.
 
     Returns
     -------
     go.Figure
         A `plotly.graph_objects.Figure` object representing the scatter plot.
     """
+    # Get all parameters from the config model, including extras
+    all_config_params = config.model_dump()
+
+    # Define parameters handled by the theme script
+    theming_params = [
+        "opacity",
+        "log_x",
+        "log_y",
+        "range_x",
+        "range_y",
+        "title",
+        "subtitle",
+        "x_title",
+        "y_title",
+        "template",
+        "width",
+        "height",
+        "marker_line_width",
+        "marker_line_color",
+        "color_by_density",
+    ]
+
+    # Create the dictionary of arguments for px.scatter
     plot_args = {
-        "x": config.x,
-        "y": config.y,
-        "size": config.size,
-        "symbol": config.symbol,
-        "text": config.text,
-        "hover_data": config.hover_cols,
-        "trendline": config.trendline,
+        k: v
+        for k, v in all_config_params.items()
+        if k not in theming_params and v is not None
     }
 
+    # Handle density coloring separately
     if config.color_by_density:
         # Calculate density and pass it to the 'color' argument
         density_values = get_density(data[config.x].values, data[config.y].values)
         plot_args["color"] = density_values
+
+        # Remove discrete color mapping for density plots
+        if "color_discrete_map" in plot_args:
+            del plot_args["color_discrete_map"]
     else:
         # Use standard group-based coloring
-        plot_args["color"] = config.group
-        plot_args["color_discrete_map"] = config.colors
+        plot_args["color"] = config.color
 
+    # Create the base figure using only the arguments for px.scatter
     fig = px.scatter(data, **plot_args)
 
-    # Apply theme
+    # Apply theme and additional styling
     fig = apply_scatter_theme(fig, config)
 
     return fig
diff --git a/src/vuecore/engines/plotly/theming.py b/src/vuecore/engines/plotly/theming.py
index 86a0f8c..95d3b0a 100644
--- a/src/vuecore/engines/plotly/theming.py
+++ b/src/vuecore/engines/plotly/theming.py
@@ -5,40 +5,55 @@
 
 def apply_scatter_theme(fig: go.Figure, config: ScatterConfig) -> go.Figure:
     """
-    Applies a consistent layout and theme to a Plotly figure.
+    Applies a consistent layout and theme to a Plotly scatter plot.
 
-    This function separates styling from plot creation, allowing for a consistent
-    look and feel across different plot types. It updates traces and layout
-    properties based on the provided configuration.
+    This function handles all styling and layout adjustments, such as titles,
+    dimensions, templates, and trace properties, separating these concerns
+    from the initial data mapping.
 
     Parameters
     ----------
     fig : go.Figure
         The Plotly figure object to be styled.
     config : ScatterConfig
-        The configuration object containing styling info like titles and dimensions.
+        The configuration object containing all styling and layout info.
 
     Returns
     -------
     go.Figure
         The styled Plotly figure object.
     """
+    # Apply trace-specific updates
     fig.update_traces(
         marker=dict(
-            opacity=config.marker_opacity,
+            opacity=config.opacity,
             line=dict(width=config.marker_line_width, color=config.marker_line_color),
         ),
         selector=dict(mode="markers"),
     )
 
+    # Use the labels dictionary to set axis titles, falling back to defaults
+    x_title = config.x_title or (
+        config.labels.get(config.x) if config.labels else None or config.x.title()
+    )
+    y_title = config.y_title or (
+        config.labels.get(config.y) if config.labels else None or config.y.title()
+    )
+
+    # Apply layout updates
     fig.update_layout(
         title_text=config.title,
-        xaxis_title=config.x_title or config.x.title(),
-        yaxis_title=config.y_title or config.y.title(),
+        title_subtitle_text=config.subtitle,
+        xaxis_title=x_title,
+        yaxis_title=y_title,
         height=config.height,
         width=config.width,
-        template="plotly_white",
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        template=config.template,
+        xaxis_type="log" if config.log_x else "linear",
+        yaxis_type="log" if config.log_y else "linear",
+        xaxis_range=config.range_x,
+        yaxis_range=config.range_y,
+        # legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
         hovermode="closest",
     )
     return fig
diff --git a/src/vuecore/plots/basic/scatter.py b/src/vuecore/plots/basic/scatter.py
index 0c691f6..85f63f4 100644
--- a/src/vuecore/plots/basic/scatter.py
+++ b/src/vuecore/plots/basic/scatter.py
@@ -41,38 +41,20 @@ def create_scatter_plot(
     Raises
     ------
     pydantic.ValidationError
-        If the provided kwargs do not match the `ScatterConfig` schema.
+        If the provided keyword arguments do not conform to the `ScatterConfig` schema,
+        e.g. a required parameter is missing or a value has an incorrect type.
     ValueError
-        If columns specified in the configuration do not exist in the DataFrame.
+        Raised by the plotting engine if a column specified in the configuration is not
+        found in the provided DataFrame.
 
     Examples
     --------
-    >>> import pandas as pd
-    >>> sample_df = pd.DataFrame({
-    ...     'gene_expression': [1.2, 2.5, 3.1, 4.5, 5.2, 6.8],
-    ...     'log_p_value': [0.5, 1.5, 2.0, 3.5, 4.0, 5.5],
-    ...     'regulation': ['Up', 'Up', 'None', 'Down', 'Down', 'Down'],
-    ...     'significance_score': [10, 20, 5, 40, 55, 80],
-    ...     'gene_name': ['GENE_A', 'GENE_B', 'GENE_C', 'GENE_D', 'GENE_E', 'GENE_F']
-    ... })
-    >>>
-    >>> # Create a simple scatter plot and save it to HTML
-    >>> fig = create_scatter_plot(
-    ...     data=sample_df,
-    ...     x='gene_expression',
-    ...     y='log_p_value',
-    ...     group='regulation',
-    ...     size='significance_score',
-    ...     text='gene_name',
-    ...     title="Gene Expression vs. Significance",
-    ...     x_title="Log2 Fold Change",
-    ...     y_title="-Log10(P-value)",
-    ...     colors={'Up': '#d62728', 'Down': '#1f77b4', 'None': '#7f7f7f'},
-    ...     file_path="my_scatter_plot.html"
-    ... )
-    >>>
-    >>> # The returned `fig` object can be displayed in a notebook or further modified
-    >>> # fig.show()
+    For detailed examples and usage, please refer to the documentation:
+
+    * **Jupyter Notebook:** `docs/api_examples/scatter_plot.ipynb` -
+    https://vuecore.readthedocs.io/en/latest/api_examples/scatter_plot.html
+    * **Python Script:** `docs/api_examples/scatter_plot.py` -
+    https://github.com/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/scatter_plot.py
     """
     # 1. Validate configuration using Pydantic
     config = ScatterConfig(**kwargs)
diff --git a/src/vuecore/schemas/basic/scatter.py b/src/vuecore/schemas/basic/scatter.py
index 12d8d6b..19d67b1 100644
--- a/src/vuecore/schemas/basic/scatter.py
+++ b/src/vuecore/schemas/basic/scatter.py
@@ -1,105 +1,174 @@
-from vuecore import PlotType
 from typing import Dict, List, Optional
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field, ConfigDict, model_validator
 
 
 class ScatterConfig(BaseModel):
     """
     Pydantic model for validating and managing scatter plot configurations.
 
-    This model defines all the possible parameters that can be used to customize
-    a scatter plot, from data mapping to styling and layout. It ensures that
-    user-provided configurations are type-safe and adhere to the expected structure.
+    This model serves as a curated API for the most relevant parameters
+    for scatter plots, closely aligned with the `plotly.express.scatter` API
+    (https://plotly.com/python-api-reference/generated/plotly.express.scatter.html).
+
+    This model includes the most relevant parameters for data mapping, styling,
+    and layout. It ensures that user-provided configurations are type-safe and
+    adhere to the expected structure. The plotting function handles parameters
+    defined here, and also accepts additional Plotly keyword arguments,
+    forwarding them to the appropriate `plotly.express.scatter` or
+    `plotly.graph_objects.Figure` call.
 
     Attributes
     ---------
+    -----Data Mapping-----
     x : str
-        Column name for the x-axis.
+        Column name for x-axis values.
     y : str
-        Column name for the y-axis.
-    type : Optional[PlotType]
-        The type of plot. Defaults to `SCATTER`.
-    group : Optional[str]
-        Column for grouping data, typically used for coloring markers.
-    size : Optional[str]
-        Column to determine marker size, enabling a third dimension of data.
+        Column name for y-axis values.
+    color : Optional[str]
+        Column to assign color to markers.
     symbol : Optional[str]
-        Column to determine the shape of markers.
+        Column to assign marker symbols.
+    size : Optional[str]
+        Column to determine marker size.
+    hover_name : Optional[str]
+        Column for bold text in hover tooltip.
+    hover_data : List[str]
+        Additional columns for hover tooltip.
     text : Optional[str]
-        Column for adding text labels directly onto markers.
-    hover_cols : List[str]
-        Additional data columns to display in the hover tooltip.
+        Column for text labels on markers.
+    facet_row : Optional[str]
+        Column for vertical facetting.
+    facet_col : Optional[str]
+        Column for horizontal facetting.
+    error_x : Optional[str]
+        Column for x-axis error bars.
+    error_y : Optional[str]
+        Column for y-axis error bars.
+    labels : Optional[Dict[str, str]]
+        Column name overrides for display.
+    color_discrete_map : Optional[Dict[str, str]]
+        Specific color mappings for color column values.
+    symbol_map : Optional[Dict[str, str]]
+        Specific symbol mappings for symbol column values.
+    size_max : int
+        Maximum marker size.
+    -----Styling and Layout-----
+    opacity : float
+        Marker opacity (0-1).
+    trendline : Optional[str]
+        Trendline type (ols/lowess/rolling/expanding/ewm).
+    trendline_options : Optional[Dict]
+        Advanced options for trendlines.
+    log_x : bool
+        Enable logarithmic x-axis.
+    log_y : bool
+        Enable logarithmic y-axis.
+    range_x : Optional[List[float]]
+        Manual x-axis range [min, max].
+    range_y : Optional[List[float]]
+        Manual y-axis range [min, max].
     title : str
-        The main title of the plot.
+        Main plot title.
     x_title : Optional[str]
-        Custom title for the x-axis. If None, defaults to the `x` column name.
+        Custom title for the x-axis.
     y_title : Optional[str]
-        Custom title for the y-axis. If None, defaults to the `y` column name.
-    height : int
-        Height of the plot in pixels.
+        Custom title for the y-axis.
+    subtitle : Optional[str]
+        Plot subtitle.
+    template : str
+        Plotly visual theme/template.
     width : int
-        Width of the plot in pixels.
-    colors : Optional[Dict[str, str]]
-        A dictionary mapping group names from the `group` column to specific colors.
-    trendline : Optional[str]
-        If specified, adds a trendline to the plot (e.g., 'ols', 'lowess').
+        Plot width in pixels.
+    height : int
+        Plot height in pixels.
+    color_by_density : bool
+        Color points by density instead of category.
+    marker_line_width : float
+        Width of marker border lines.
+    marker_line_color : str
+        Color of marker border lines.
     """
 
+    # General Configuration
+    # Allow extra parameters to pass through to Plotly
+    model_config = ConfigDict(extra="allow")
+
     # Data mapping
-    x: str = Field(..., description="Column name for the x-axis.")
-    y: str = Field(..., description="Column name for the y-axis.")
-    type: Optional[PlotType] = Field(
-        PlotType.SCATTER, description="Type of plot, defaults to SCATTER."
+    x: str = Field(..., description="Column for x-axis values.")
+    y: str = Field(..., description="Column for y-axis values.")
+    color: Optional[str] = Field(
+        None, description="Column for color assignment (replaces 'group')."
     )
-    group: Optional[str] = Field(
-        None, description="Column for grouping data, often used for color."
+    symbol: Optional[str] = Field(
+        None, description="Column for marker symbol assignment."
     )
     size: Optional[str] = Field(None, description="Column to determine marker size.")
-    symbol: Optional[str] = Field(
-        None, description="Column to determine marker symbol."
+    hover_name: Optional[str] = Field(
+        None, description="Column for bold text in hover tooltip."
+    )
+    hover_data: List[str] = Field(
+        [], description="Additional columns for hover tooltip."
     )
     text: Optional[str] = Field(None, description="Column for text labels on markers.")
-    hover_cols: List[str] = Field(
-        [], description="Additional columns to show on hover."
+    facet_row: Optional[str] = Field(None, description="Column for vertical facetting.")
+    facet_col: Optional[str] = Field(
+        None, description="Column for horizontal facetting."
+    )
+    error_x: Optional[str] = Field(None, description="Column for x-axis error bars.")
+    error_y: Optional[str] = Field(None, description="Column for y-axis error bars.")
+    labels: Optional[Dict[str, str]] = Field(
+        None, description="Column name overrides for display purposes."
+    )
+    color_discrete_map: Optional[Dict[str, str]] = Field(
+        None, description="Specific color mappings for color column values."
+    )
+    symbol_map: Optional[Dict[str, str]] = Field(
+        None, description="Specific symbol mappings for symbol column values."
     )
+    size_max: int = Field(20, description="Maximum size for markers.")
 
     # Styling and Layout
-    title: str = Field("Scatter Plot", description="The main title of the plot.")
-    x_title: Optional[str] = Field(
-        None, description="Title for the x-axis. Defaults to x column name."
+    opacity: float = Field(0.8, ge=0, le=1, description="Overall opacity of markers.")
+    trendline: Optional[str] = Field(
+        None, description="Trendline type (ols/lowess/rolling/expanding/ewm)."
+    )
+    trendline_options: Optional[Dict] = Field(
+        None, description="Advanced options for trendline configuration."
     )
-    y_title: Optional[str] = Field(
-        None, description="Title for the y-axis. Defaults to y column name."
+    log_x: bool = Field(False, description="Enable logarithmic x-axis scale.")
+    log_y: bool = Field(False, description="Enable logarithmic y-axis scale.")
+    range_x: Optional[List[float]] = Field(
+        None, description="Manual x-axis range [min, max]."
     )
-    height: int = Field(600, description="Height of the plot in pixels.")
-    width: int = Field(800, description="Width of the plot in pixels.")
-    colors: Optional[Dict[str, str]] = Field(
-        None, description="Mapping of group names to specific colors."
+    range_y: Optional[List[float]] = Field(
+        None, description="Manual y-axis range [min, max]."
     )
-    marker_opacity: float = Field(
-        0.8, ge=0, le=1, description="Opacity of the markers."
+    title: str = Field("Scatter Plot", description="Main title of the plot.")
+    subtitle: Optional[str] = Field(
+        None, description="Subtitle displayed below main title."
     )
+    x_title: Optional[str] = Field(None, description="Custom title for the x-axis.")
+    y_title: Optional[str] = Field(None, description="Custom title for the y-axis.")
+    template: str = Field("plotly_white", description="Plotly visual theme/template.")
+    width: int = Field(800, description="Plot width in pixels.")
+    height: int = Field(600, description="Plot height in pixels.")
     marker_line_width: float = Field(
-        0.5, ge=0, description="Width of the line surrounding each marker."
+        0.5, ge=0, description="Width of marker border lines."
     )
     marker_line_color: str = Field(
-        "DarkSlateGrey", description="Color of the line surrounding each marker."
+        "DarkSlateGrey", description="Color of marker border lines."
     )
 
     # Special features
-    trendline: Optional[str] = Field(
-        None, description="Adds a trendline. E.g., 'ols' for Ordinary Least Squares."
-    )
     color_by_density: bool = Field(
-        False, description="If True, color points by density instead of group."
+        False, description="Color points by density instead of category."
     )
 
     @model_validator(mode="after")
-    def check_exclusive_coloring(self) -> "ScatterConfig":
-        """Ensure that coloring by group and by density are mutually exclusive."""
-        if self.color_by_density and self.group:
+    def validate_exclusive_color_options(self) -> "ScatterConfig":
+        if self.color_by_density and self.color:
             raise ValueError(
-                "Cannot set 'group' when 'color_by_density' is True. "
-                "Coloring is mutually exclusive."
+                "Cannot use both 'color' and 'color_by_density'. "
+                "These options are mutually exclusive."
             )
         return self
diff --git a/src/vuecore/schemas/distribution/__init__.py b/src/vuecore/schemas/distribution/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/test_scatter.py b/tests/test_scatter.py
index e7a0114..a4f463f 100644
--- a/tests/test_scatter.py
+++ b/tests/test_scatter.py
@@ -1,6 +1,6 @@
 import pandas as pd
 import pytest
-from vuecore.plots.distribution.scatter import create_scatter_plot
+from vuecore.plots.basic.scatter import create_scatter_plot
 
 
 @pytest.fixture
@@ -52,19 +52,20 @@ def test_advanced_scatter_plot(sample_df, tmp_path, ext):
         data=sample_df,
         x="gene_expression",
         y="log_p_value",
-        group="regulation",
+        color="regulation",
         size="significance_score",
         text="gene_name",
-        title="Advanced Gene Expression Plot",
+        title="Advanced Gene Expression Scatter Plot",
+        subtitle="Visualizing Gene Expression with Regulation and Significance",
         x_title="Log2 Fold Change",
         y_title="-Log10(P-value)",
-        colors={"Up": "#FF5733", "Down": "#3380FF", "None": "#33FF57"},
-        marker_opacity=0.8,
+        color_discrete_map={"Up": "#508AA8", "Down": "#A8505E", "None": "#838383"},
+        opacity=0.8,
         marker_line_width=1,
         marker_line_color="darkgray",
         width=900,
         height=600,
-        file_path=str(output_path),
+        file_path=output_path,
     )
 
     assert fig is not None