# Plot Types

OmeroScreen Plots provides several specialized plot types for high-content screening data analysis. Each plot type is designed for specific analytical needs.


# FeaturePlot

**Purpose**: Compare any measured feature across conditions using box plots, violin plots, or boxen plots.

**Use cases**: - Protein expression levels - Cell morphology parameters - Intensity measurements - Any quantitative feature


## Basic Usage

``` python
from omero_screen_plots.featureplot import FeaturePlot

plot = FeaturePlot(
    data_path="sample_plate_data.csv",
    y_feature="intensity_mean_p21_nucleus",
    conditions=["DMSO", "Nutlin"],
    condition_col="condition",
    plot_type="box"  # Options: "box", "violin", "boxen"
)
```


## Key Parameters

- **y_feature**: The feature to plot on y-axis
- **plot_type**: Visual representation style
- **test_group_pairs**: Pairs for statistical comparisons
- **grouped**: Enable visual grouping of conditions
- **show_points**: Overlay individual data points


## Output

- Box/violin plots with statistical annotations
- Optional scatter overlay
- Automatic or custom statistics


# ClassificationPlot

**Purpose**: Visualize categorical classification results as stacked bars or individual triplicates.

**Use cases**: - Cell morphology classification - Phenotype distribution analysis - Quality control assessment - Drug toxicity evaluation


## Basic Usage

``` python
from omero_screen_plots import classification_plot

fig, ax = classification_plot(
    df=df,
    classes=["normal", "micronuclei", "collapsed"],
    conditions=["control", "treatment1", "treatment2"],
    class_col="classifier",  # Dynamic classification column
    display_mode="stacked"    # or "triplicates"
)
```


## Key Features

- **Dynamic class column**: Adapt to any categorical classification
- **Two display modes**: Stacked bars with error bars or individual triplicates
- **Flexible grouping**: Group conditions for easier comparison
- **Custom colors**: Apply meaningful color schemes to categories


# CellCyclePlot

**Purpose**: Analyze cell cycle distribution based on DNA content (DAPI) and S-phase marker (EdU).

**Use cases**: - Cell cycle profiling - Drug effect on proliferation - p21-based cell cycle arrest analysis


## Basic Usage

``` python
from omero_screen_plots.cellcycleplot import CellCyclePlot

cc_plot = CellCyclePlot(
    data_path="sample_plate_data.csv",
    conditions=["DMSO", "Nutlin"],
    condition_col="condition",
    dapi_col="intensity_integrated_dapi_nucleus",
    edu_col="intensity_mean_edu_nucleus",
    p21_col="intensity_mean_p21_nucleus"  # Optional
)
```


## Key Parameters

- **dapi_col**: Column with integrated DAPI intensity
- **edu_col**: Column with EdU incorporation data
- **p21_col**: Optional p21 expression for arrest analysis
- **plot_type**: "standard", "grouped", or "stacked"
- **normalise**: Normalize to percentage or absolute counts


## Output

- Cell cycle phase distribution (G1, S, G2)
- Optional p21+ arrested cells
- Statistical comparisons between conditions


# CombPlot

**Purpose**: Combined visualizations showing relationships between features with marginal distributions.

**Use cases**: - Feature correlation analysis - Scatter plots with histograms - Distribution comparisons - Multi-dimensional data exploration


## Plot Types Available

1.  **Scatter Plot**

    ``` python
    plot = CombPlot(
        data_path="sample_plate_data.csv",
        plot_type="scatter",
        x_feature="intensity_integrated_dapi_nucleus",
        y_feature="intensity_mean_edu_nucleus"
    )
    ```

2.  **Histogram**

    ``` python
    plot = CombPlot(
        data_path="sample_plate_data.csv",
        plot_type="histogram",
        x_feature="area_nucleus"
    )
    ```

3.  **Hexbin Plot**

    ``` python
    plot = CombPlot(
        data_path="sample_plate_data.csv",
        plot_type="hexbin",
        x_feature="area_nucleus",
        y_feature="intensity_mean_dapi_nucleus"
    )
    ```


## Key Parameters

- **x_feature**, **y_feature**: Features for x and y axes
- **plot_type**: "scatter", "histogram", "hexbin", "kde"
- **show_regression**: Add regression line to scatter
- **marginal_type**: Type of marginal plots


# CountPlot

**Purpose**: Analyze and visualize cell counts per well or condition.

**Use cases**: - Toxicity assessment - Proliferation analysis - Quality control - Well-to-well variation


## Basic Usage

``` python
from omero_screen_plots.countplot import CountPlot

count_plot = CountPlot(
    data_path="sample_plate_data.csv",
    conditions=["DMSO", "Nutlin", "Etop", "Noc"],
    condition_col="condition"
)
```


## Key Parameters

- **normalise_to**: Normalize counts to specific condition
- **show_plate_layout**: Display as plate heatmap
- **aggregate_by**: Group by well, condition, or plate


## Output

- Bar plots of cell counts
- Statistical comparisons
- Optional plate layout visualization


# Specialized Analysis Functions


## Normalise

**Purpose**: Data normalization utilities for immunofluorescence data.

``` python
from omero_screen_plots.normalise import NormalisePlot

norm_plot = NormalisePlot(
    data_path="sample_plate_data.csv",
    feature="intensity_mean_p21_nucleus",
    control_condition="DMSO"
)
```

Features: - Z-score normalization - Percent of control - Robust scaling - Plate-wise normalization


## Stats Module

**Purpose**: Statistical analysis utilities used across all plot types.

``` python
from omero_screen_plots.stats import perform_statistical_tests

# Automatic statistical testing
results = perform_statistical_tests(
    data,
    groups=["DMSO", "Nutlin"],
    test_type="auto"  # Automatically selects appropriate test
)
```


# Choosing the Right Plot Type

| Analysis Goal                            | Recommended Plot Type |
|------------------------------------------|-----------------------|
| Compare single feature across conditions | FeaturePlot           |
| Analyze cell cycle distribution          | CellCyclePlot         |
| Explore feature relationships            | CombPlot (scatter)    |
| Assess cell viability/counts             | CountPlot             |
| Examine distributions                    | CombPlot (histogram)  |
| Normalize data                           | NormalisePlot         |


# Common Workflow

1.  **Start with CountPlot** to check data quality and cell numbers
2.  **Use FeaturePlot** to explore individual features
3.  **Apply CellCyclePlot** for proliferation analysis
4.  **Explore with CombPlot** for feature relationships
5.  **Normalize data** when comparing across plates


# Tips

- Always check cell counts first to identify problematic wells
- Use violin plots for large datasets to see distribution shapes
- Enable `show_points` in FeaturePlot for small sample sizes
- Use grouped layouts when comparing related conditions
- Save plots in vector format (PDF/SVG) for publications