## feature_norm_plot()


Create a normalized feature plot with threshold-based stacked bars.


Usage

``` python
feature_norm_plot(
    df,
    feature,
    conditions,
    axes=None,
    x_label=True,
    condition_col="condition",
    selector_col="cell_line",
    selector_val="",
    title="",
    color_scheme="green",
    legend=False,
    fig_size=(8, 6),
    size_units="cm",
    normalize_by_plate=True,
    threshold=1.5,
    save_norm_qc=False,
    show_triplicates=False,
    show_error_bars=True,
    show_boxes=True,
    group_size=1,
    within_group_spacing=0.2,
    between_group_gap=0.5,
    paired=True,
    show_ns=True,
    scatter_size=7.0,
    min_effect=None,
    save=True,
    save_stats=False,
    path=None,
    tight_layout=False,
    file_format="pdf",
    dpi=300
)
```


This plot normalizes the feature data by mode (peak=1.0) and displays the proportion of cells above/below a threshold as stacked bars. Optionally shows individual triplicate data with boxes.


df : pd.DataFrame The dataframe containing single-cell measurements. feature : str The feature column to normalize and plot. conditions : list\[str\] The conditions to plot. axes : Optional\[Axes\], default=None The axis to plot on. x_label : bool, default=True Whether to show the x-label. condition_col : str, default="condition" The column name for the conditions. selector_col : Optional\[str\], default="cell_line" The column name for the selector. selector_val : Optional\[str\], default="" The value of the selector. title : Optional\[str\], default="" The title of the plot. color_scheme : str, default="green" Color scheme for positive/negative categories. Options: - "green": Olive (positive) and Light Green (negative) - "blue": Blue (positive) and Light Blue (negative) - "purple": Purple (positive) and Lavender (negative) Invalid values default to "green". legend : bool, default=False Whether to show the default plate legend. Default False since norm plots have their own threshold legend. fig_size : tuple\[float, float\], default=(8, 6) The size of the figure. size_units : str, default="cm" The units of the figure size. normalize_by_plate : bool, default=True Whether to normalize within each plate. threshold : float, default=1.5 The threshold value (times mode). save_norm_qc : bool, default=False Whether to save normalization QC plots. QC plots show the intensity distributions before/after normalization to document the process. Saved to the same path as the main plot with suffix "\_norm_qc". show_triplicates : bool, default=False Whether to show individual triplicate bars. show_error_bars : bool, default=True Whether to show error bars on summary bars. show_boxes : bool, default=True Whether to draw boxes around triplicates. group_size : int, default=1 The number of conditions to group. paired : bool, default=True Use a paired t-test (ttest_rel, matched by plate_id) for significance; set False for the unpaired ttest_ind. within_group_spacing : float, default=0.2 The spacing between conditions within a group. between_group_gap : float, default=0.5 The gap between groups. save : bool, default=True Whether to save the plot. save_stats : bool, default=False Write {title}\_stats.csv and {title}\_medians.csv to `path` (independent of `save`; works when plotting onto a provided/composed `axes`). path : Optional\[Path\], default=None The path to save the plot. tight_layout : bool, default=False Whether to use tight layout. file_format : str, default="pdf" The format of the saved figure. dpi : int, default=300 The resolution of the saved figure.


tuple\[Figure, Axes\] The figure and axes objects.


``` python
>>> # Basic normalized feature plot with default green scheme
>>> fig, ax = feature_norm_plot(
...     df=df,
...     feature="intensity_mean_p21_nucleus",
...     conditions=["control", "treatment1", "treatment2"],
... )
```

``` python
>>> # Blue color scheme with individual triplicates
>>> fig, ax = feature_norm_plot(
...     df=df,
...     feature="intensity_mean_p21_nucleus",
...     conditions=["control", "treatment1", "treatment2"],
...     color_scheme="blue",
...     show_triplicates=True,
...     threshold=2.0,  # Custom threshold at 2x mode
... )
```

``` python
>>> # Purple color scheme for publication
>>> fig, ax = feature_norm_plot(
...     df=df,
...     feature="intensity_mean_p21_nucleus",
...     conditions=["control", "treatment1", "treatment2"],
...     color_scheme="purple",
... )
```