# Scatter plots

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The scatter plot provides intelligent visualization for exploring relationships between two quantitative features with automatic context detection for cell cycle analysis and extensive customization options.

See the [API reference](../reference/index.md) for full signatures and parameter documentation.


# Examples


## Basic Feature Correlation

Create a standard scatter plot to explore relationships between two features:

``` python
from omero_screen_plots import scatter_plot
import pandas as pd

df = pd.read_csv("data.csv")
fig, ax = scatter_plot(
    df=df,
    x_feature="area_cell",
    y_feature="intensity_mean_p21_nucleus",
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    title="Cell Area vs p21 Intensity",
    save=True,
    file_format="svg"
)
```


<figure class="figure">
<p><img src="../assets/scatter_plot_basic.svg" class="img-fluid figure-img" /></p>
<figcaption>Basic Feature Correlation</figcaption>
</figure>


## Multiple Condition Comparison

Compare feature relationships across different experimental conditions:

``` python
fig, ax = scatter_plot(
    df=df,
    x_feature="area_cell",
    y_feature="intensity_mean_p21_nucleus",
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    title="Multi-Condition Feature Analysis"
)
```


<figure class="figure">
<p><img src="../assets/scatter_plot_multiple.svg" class="img-fluid figure-img" /></p>
<figcaption>Multiple Condition Comparison</figcaption>
</figure>


## Cell Cycle Context Detection

Automatic cell cycle visualization with DNA content and EdU incorporation:

``` python
fig, ax = scatter_plot(
    df=df,
    x_feature="integrated_int_DAPI_norm",  # Auto-detected as DNA content
    y_feature="intensity_mean_EdU_nucleus_norm",  # Auto-detected as EdU
    conditions=['control', 'cond01', 'cond02'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    title="Cell Cycle Analysis"
)
```


<figure class="figure">
<p><img src="../assets/scatter_plot_phases.svg" class="img-fluid figure-img" /></p>
<figcaption>Cell Cycle Context Detection</figcaption>
</figure>


## Threshold Analysis

Apply threshold-based classification with custom coloring:

``` python
fig, ax = scatter_plot(
    df=df,
    x_feature="area_cell",
    y_feature="intensity_mean_p21_nucleus",
    conditions=['control', 'treatment'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    threshold=5000,  # Threshold value for y-axis
    title="Threshold-Based Classification"
)
```


<figure class="figure">
<p><img src="../assets/scatter_plot_threshold.svg" class="img-fluid figure-img" /></p>
<figcaption>Threshold Analysis</figcaption>
</figure>


## KDE Density Overlay

Add kernel density estimation for distribution visualization:

``` python
fig, ax = scatter_plot(
    df=df,
    x_feature="integrated_int_DAPI_norm",
    y_feature="intensity_mean_EdU_nucleus_norm",
    conditions=['control'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    kde_overlay=True,
    title="Density Distribution Analysis"
)
```


<figure class="figure">
<p><img src="../assets/scatter_plot_kde.svg" class="img-fluid figure-img" /></p>
<figcaption>KDE Density Overlay</figcaption>
</figure>