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

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

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

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

    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

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

  3. Hexbin Plot

    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

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.

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.

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