Overview

FlowCyPy is a cutting-edge Python library designed to simulate flow cytometer experiments. By generating realistic Forward Scatter (FSC) and Side Scatter (SSC) signals, FlowCyPy enables detailed modeling of flow cytometry setups, making it ideal for researchers and engineers working with extracellular vesicles (EVs) or other scatterers.

Key Features

• Particle Event Simulation: Create detailed FSC/SSC signals with customizable particle size and refractive index distributions.

• Noise and Signal Modeling: Incorporate realistic noise sources (thermal, shot, dark current) and baseline shifts.

• Detector Configurations: Simulate real-world detector behaviors, including saturation and responsivity.

• Fluorescence Modeling: Simulate fluorescence signals for labeled particles (e.g., EV surface markers).

• Visualization Tools: Generate advanced plots, including density maps and signal traces.

For full documentation and examples, visit the FlowCyPy Documentation.

Installation

Install FlowCyPy via pip or conda`:

pip install FlowCyPy
conda install FlowCyPy --channels MartinPdeS

Requirements: Python 3.11 or higher with dependencies: numpy, pint, tabulate, seaborn, MPSPlots, PyMieSim, pydantic>=2.6.3

Prebuilt wheels are published for Linux x86_64 and macOS ARM64. Other platforms require building from source with FFTW and OpenMP available.

Quick Start

Simulate a simple flow cytometer experiment:

from FlowCyPy.units import ureg
from FlowCyPy.fluidics import (
    Fluidics,
    FlowCell,
    ScattererCollection,
    populations,
    SampleFlowRate,
    SheathFlowRate,
)

# from FlowCyPy.sampling_method import GammaModel, ExplicitModel
from FlowCyPy.fluidics import distributions

flow_cell = FlowCell(
    sample_volume_flow=SampleFlowRate.MEDIUM.value,
    sheath_volume_flow=SheathFlowRate.MEDIUM.value,
    width=400 * ureg.micrometer,
    height=150 * ureg.micrometer,
)

scatterer_collection = ScattererCollection()

medium_refractive_index = distributions.Delta(1.33)

diameter_dist = distributions.RosinRammler(
    scale=200 * ureg.nanometer,
    shape=10,
)

ri_dist = distributions.Normal(
    mean=1.44,
    standard_deviation=0.002,
    low_cutoff=1.33,
)

sampling_method = populations.ExplicitModel()

population_0 = populations.SpherePopulation(
    name="Pop 0",
    medium_refractive_index=medium_refractive_index,
    concentration=1e10 * ureg.particle / ureg.milliliter,
    diameter=diameter_dist,
    refractive_index=ri_dist,
    sampling_method=sampling_method,
)


diameter_dist = distributions.RosinRammler(
    scale=30 * ureg.nanometer,
    shape=50,
)

ri_dist = distributions.Normal(
    mean=1.44,
    standard_deviation=0.002,
    low_cutoff=1.33,
)

population_1 = populations.SpherePopulation(
    name="Pop 1",
    medium_refractive_index=medium_refractive_index,
    concentration=5e11 * ureg.particle / ureg.milliliter,
    diameter=diameter_dist,
    refractive_index=ri_dist,
    sampling_method=populations.GammaModel(number_of_samples=5_000),
)

scatterer_collection.add_population(population_0, population_1)

scatterer_collection.dilute(factor=80)

fluidics = Fluidics(scatterer_collection=scatterer_collection, flow_cell=flow_cell)

# %%
# Step 2: Define Optical Subsystem
# --------------------------------
from FlowCyPy.opto_electronics import (
    Detector,
    Digitizer,
    OptoElectronics,
    Amplifier,
    source,
    circuits,
)

analog_processing = [
    circuits.BaselineRestorationServo(time_constant=100 * ureg.microsecond),
    circuits.BesselLowPass(cutoff_frequency=2 * ureg.megahertz, order=4, gain=2),
]

source = source.Gaussian(
    waist_z=10e-6 * ureg.meter,  # Beam waist along flow direction (z-axis)
    waist_y=60e-6 * ureg.meter,
    wavelength=405 * ureg.nanometer,
    optical_power=200 * ureg.milliwatt,
    rin=-140 * ureg.dB_per_Hz,
    bandwidth=10 * ureg.megahertz,
)

detectors = [
    Detector(
        name="side",
        phi_angle=90 * ureg.degree,
        numerical_aperture=1.1,
        responsivity=1 * ureg.ampere / ureg.watt,
    ),
    Detector(
        name="forward",
        phi_angle=0 * ureg.degree,
        numerical_aperture=0.3,
        cache_numerical_aperture=0.1,
        responsivity=1 * ureg.ampere / ureg.watt,
    ),
]

digitizer = Digitizer(
    sampling_rate=60 * ureg.megahertz,
    bit_depth=14,
    use_auto_range=True,
    channel_range_mode="shared",
)

amplifier = Amplifier(
    gain=10 * ureg.volt / ureg.ampere,
    bandwidth=10 * ureg.megahertz,
    voltage_noise_density=0.0 * ureg.nanovolt / ureg.sqrt_hertz,
    current_noise_density=0.0 * ureg.femtoampere / ureg.sqrt_hertz,
)

opto_electronics = OptoElectronics(
    digitizer=digitizer,
    detectors=detectors,
    source=source,
    amplifier=amplifier,
    analog_processing=analog_processing,
)


# %%
# Step 3: Signal Processing Configuration
# ---------------------------------------
from FlowCyPy.digital_processing import (
    DigitalProcessing,
    peak_locator,
    discriminator,
)

triggering = discriminator.FixedWindow(
    trigger_channel="side",
    threshold="4sigma",
    pre_buffer=40,
    post_buffer=40,
    max_triggers=-1,
)

peak_algo = peak_locator.GlobalPeakLocator()

digital_processing = DigitalProcessing(
    discriminator=triggering,
    peak_algorithm=peak_algo,
)

# %%
# Step 4: Run Simulation
# ----------------------
from FlowCyPy import FlowCytometer

cytometer = FlowCytometer(
    fluidics=fluidics,
    background_power=0.001 * ureg.milliwatt,
)

run_record = cytometer.run(
    opto_electronics=opto_electronics,
    digital_processing=digital_processing,
    run_time=1 * ureg.millisecond,
)

run_record.event_collection.plot(x="Diameter")

run_record.event_collection.plot(x="forward")

run_record.plot_analog()

run_record.plot_digital()


run_record.peaks.plot(x=("forward", "Height"))

_ = run_record.plot_analog(
    figure_size=(12, 8),
    show=False,
    save_as=f"{dir_path}/../images/readme_analog.png",
)

_ = run_record.plot_digital(
    figure_size=(12, 8),
    show=False,
    save_as=f"{dir_path}/../images/readme_digital.png",
)

Explore more examples in the FlowCyPy Examples.

Code structure

Here is the architecture for a standard workflow using FlowCyPy:

Development and Contribution

git clone https://github.com/MartinPdeS/FlowCyPy.git
cd FlowCyPy

pip install -e .[testing,documentation] (on linux system)
pip install -e ".[testing,documentation]" (on macOS system)

pytest

cd docs
make html

Additional Resources

• Documentation: Full guide and API reference at FlowCyPy Documentation

• Examples: Explore use cases in the Examples Section

• GitHub Repository: Access the source code and contribute at FlowCyPy GitHub

Contributions

Contributions are welcome! If you have suggestions, issues, or would like to collaborate, visit the GitHub repository.

Contact

For inquiries or collaboration, contact Martin Poinsinet de Sivry-Houle.