# Cell cycle stacked plots

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The cell cycle stacked plot provides unified visualization of cell cycle phase distributions using stacked bar charts with options for summary statistics or individual biological replicates.

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


# Examples


## Basic Stacked Distribution

Create a standard stacked bar plot showing cell cycle phase proportions with error bars:

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

df = pd.read_csv("data.csv")
fig, ax = cellcycle_stacked(
    df=df,
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    title="Cell Cycle Phase Distribution",
    save=True,
    file_format="svg"
)
```


<figure class="figure">
<p><img src="../assets/cellcycle_stacked_default.svg" class="img-fluid figure-img" /></p>
<figcaption>Basic Stacked Distribution</figcaption>
</figure>


## Individual Triplicates Display

Show individual biological replicates to assess experimental consistency:

``` python
fig, ax = cellcycle_stacked(
    df=df,
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    selector_val="MCF10A",
    show_triplicates=True,
    group_size=1,
    title="Cell Cycle - Individual Replicates"
)
```


<figure class="figure">
<p><img src="../assets/cellcycle_stacked_triplicates.svg" class="img-fluid figure-img" /></p>
<figcaption>Individual Triplicates Display</figcaption>
</figure>


## DNA Content Focus

Emphasize DNA content measurements with custom phase selection:

``` python
fig, ax = cellcycle_stacked(
    df=df,
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    selector_val="MCF10A",
    phase_order=['SubG1', 'G1', 'S', 'G2/M'],
    title="DNA Content Distribution"
)
```


<figure class="figure">
<p><img src="../assets/cellcycle_stacked_DNA_content.svg" class="img-fluid figure-img" /></p>
<figcaption>DNA Content Focus</figcaption>
</figure>


## Custom Phase Colors

Apply custom color schemes for publication figures:

``` python
from omero_screen_plots.colors import COLOR

phase_colors = [COLOR.LIGHT_BLUE.value, COLOR.BLUE.value,
                COLOR.GREY.value, COLOR.DARK_GREY.value]

fig, ax = cellcycle_stacked(
    df=df,
    conditions=['control', 'cond01', 'cond02', 'cond03'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    colors=phase_colors,
    show_legend=True,
    title="Custom Cell Cycle Colors"
)
```


<figure class="figure">
<p><img src="../assets/cellcycle_stacked_custom_colors.svg" class="img-fluid figure-img" /></p>
<figcaption>Custom Phase Colors</figcaption>
</figure>


## Grouped Condition Analysis

Group related conditions for treatment comparison:

``` python
fig, ax = cellcycle_stacked(
    df=df,
    conditions=['control', 'low_dose', 'med_dose', 'high_dose'],
    condition_col="condition",
    selector_col="cell_line",
    selector_val="MCF10A",
    show_triplicates=True,
    group_size=2,
    within_group_spacing=0.2,
    between_group_gap=0.5,
    title="Dose Response Analysis"
)
```


<figure class="figure">
<p><img src="../assets/cellcycle_stacked_advanced_grouping.svg" class="img-fluid figure-img" /></p>
<figcaption>Grouped Condition Analysis</figcaption>
</figure>