PyFLASH

A Python package for processing and analyzing immunofluorescence (IF) confocal microscopy data exported from the FLASH ImageJ Plugin.

Documentation

What it does

Takes CSV exports from ImageJ's 3D Object Counter and other plugins, processes them into structured experiment/batch objects, performs statistical analysis, generates publication-quality plots, and exports formatted Excel summaries.

Installation

pip install PyFLASH-analysis

Typical installation time

On a normal desktop or laptop (at least 4 CPU cores, 8 GB RAM, an SSD, and a stable broadband connection), allow approximately 2-5 minutes to install PyFLASH and its required dependencies into a fresh Python environment. Python must already be installed. This is a planning estimate; download speed, existing packages and availability of prebuilt dependencies affect the time. Installing Python itself or optional extras takes additional time.

For reference, an uncached installation of PyFLASH-analysis 0.2.0 from PyPI into a fresh environment took 4 minutes 4 seconds on 30 September 2026 (Windows 11, Python 3.12.10, AMD Ryzen 7 7730U, 32 GB RAM, SSD). This includes required dependency downloads and installation; it excludes installing Python and creating the environment. See demo/verification.json.

License

PyFLASH is distributed under the BSD 3-Clause License. See LICENSE.

Requires: Python ≥ 3.10

Required dependencies (minimum versions): reprofig 0.5.1, pandas 1.5, numpy 1.23, matplotlib 3.6, seaborn 0.12, scipy 1.11, statsmodels 0.14, scikit-posthocs 0.9, scikit-learn 1.2, openpyxl 3.0, read-roi 1.6, and Pillow 9.0. pip installs these automatically; the authoritative requirements are in pyproject.toml.

Hardware and operating systems: no GPU or other non-standard hardware is required for the demo. The package uses cross-platform Python dependencies. The demo was verified on Windows 11 with Python 3.12.10 and PyFLASH-analysis 0.2.0; macOS and Linux were not tested in this verification run.

The PyPI distribution is PyFLASH-analysis; the Python import package is PyFLASH. For local development, install from the repository with pip install -e .. For local notebook testing, start Jupyter from this repository and run pip install -e .; the editable PyFLASH-analysis install points at the local PyFLASH/ source files while imports stay as import PyFLASH.

Reproducible installation

The current tagged release is PyFLASH 0.3.1. For a fixed installation of that release:

python -m pip install "PyFLASH-analysis==0.3.1"

The default branch and unpinned installation can change over time. Record the software version, dependencies and analysis settings with your results. Source for the release is available at the v0.3.1 tag.

Small simulated demo

The demo/ folder includes a ready-to-use CSV table of twelve artificial subjects (six per group), under 1 KB, and a runnable script. Download or clone this repository and run from its root:

python demo/run_demo.py

Expected output is Demo passed, an imported subject table, a group-statistics CSV, a saved and reloaded PyFLASH experiment, and a timing/version report under demo/output/. Group mean signal intensities are approximately 40.89737 and 51.88691 arbitrary units. The script checks its numerical results against the supplied expected result.

Expected running time on a normal desktop or laptop is under 1 minute, including Python imports and output writing, after installation. All data and the built-in group difference are simulated and have no biological meaning. See the demo guide for outputs, rerunning, and adapting the example to your own tabular data.

The verified demo took 10.3 seconds on the laptop described above, using the published PyPI package in a fresh environment. Exact tested dependency versions are in demo/tested-dependencies.txt.

Manuscript analysis scripts

The Soteras et al. rerun guide provides command-line scripts using public PyFLASH APIs for human regression/correlation analyses, mouse batch preparation, and explicitly configured panel statistics. Each run records its inputs, settings, versions and sample counts. The guide identifies the original exclusions and settings still required before claiming an exact match to the final paper results.

Quick start

from PyFLASH import *
from PyFLASH.plotting import plot_mean_bars, plot_matrices, plot_location
from PyFLASH.utils import get_columns

set_pyflash_style()

# Define experimental conditions (fluent builder)
conditions = (
    ConditionBuilder("Genotype")
    .add("WT", short="WT", color="blue")        # color names or hex
    .add("KO", short="KO", color="red")
    .compare("WT", "KO")                         # named, not '1-2'
    .explain("Wild-type vs knockout mice")
    .build()
)

# Or the classic API (still works):
# WT = condition('WT', 'WT', Config.COLORS['blue'], 'Genotype', 'Wild-type mice')
# KO = condition('KO', 'KO', Config.COLORS['red'], 'Genotype', 'Knockout mice')
# conditions = conditionList([WT, KO], comparisons=['1-2'])

# Create or load a batch
batch = create_batch(
    "My Experiment",
    conditions,
    batch_path="path/to/output",
    experiments={"Cohort1": "path/to/data1", "Cohort2": "path/to/data2"},
    pickle_path="path/to/cache",
)

# Analyse
cols = get_columns(batch.summary, column_strings=['Count', 'Volume'], exclude='NonColoc')
plot_mean_bars(batch, cols, factor='Genotype')
plot_matrices(batch, cols)

# Export
batch.export_all_excel()
save_state(batch, "my_batch.pkl")

Self-describing ReproFig figures

Every PyFLASH figure now carries a compressed figure record: the exact plotted comma-separated values (CSV) table, exact statistics and sample-size definitions, PyFLASH and Python versions, creating function, run request, reproduction script, and source-file fingerprints. The default master is self-contained; companion CSV files are optional.

Choose a distribution profile at the shared save point:

from PyFLASH.utils import save_fig

save_fig(
    fig,
    output_dir,
    "Figure 1",
    figure_profile="master",
    figure_formats=("svg", "pdf", "png", "jpg", "tif", "webp", "avif", "heif"),
    dpi=300,
)
save_fig(
    fig,
    output_dir,
    "Figure 1 public",
    figure_profile="public",
    figure_safe_columns=["group", "metric", "value"],
    write_companion_csv=True,
)

All direct formats above share one figure identity. For existing PowerPoint, Word, Excel, HTML, netCDF-4, HDF5, FITS and ZIP/RO-Crate files, use PyFLASH.publication.embed_file.

For publication, derive new files from masters without changing them:

from PyFLASH import publish_artifacts

publish_artifacts(
    ["Figure 1.svg"],
    output_dir="Publication Figures",
    figure_profile="minimal_public",
    safe_columns=["group", "metric", "value"],
)

The resulting flat folder contains privacy-validated figure files, public source data CSV files, exact statistics CSV files, a hashed manifest, and a validation report. Command-line equivalents start with pyflash-figure, for example pyflash-figure inspect Figure.svg and pyflash-figure publish Figure.svg --profile public --safe-columns group,value --output-dir Publication.

Plot styling

Use set_pyflash_style() as the single front door for plot styling. It applies Matplotlib-level style such as fonts, tick widths, spines, titles, labels, and legends, plus PyFLASH semantics such as condition hatch cycles, scatter marker defaults, significance stars, matrix label orientation, colormaps, and overview status colours:

from PyFLASH import set_pyflash_style, pyflash_style_context

set_pyflash_style(
    point_size=9,
    title_size=20,
    labelsize=22,
    despine=True,
    legend_frame=False,
    significance_thresholds={0.0001: "****", 0.001: "***", 0.01: "**", 0.05: "*"},
    bar_point_fill="group",
    bar_point_edge="none",
    scatter_3d_edge="group",
    matrix_x_tick_rotation=60,
)

with pyflash_style_context(matrix_cmap="viridis"):
    plot_matrices(batch, cols)

Crossed (factorial) designs

genotype = (
    ConditionBuilder("Genotype")
    .add("WT", short="WT", color="blue")
    .add("KO", short="KO", color="red")
    .compare("WT", "KO")
    .build()
)

treatment = (
    ConditionBuilder("Drug")
    .add("Vehicle", short="Veh")
    .add("Drug A", short="DrugA")
    .compare("Veh", "DrugA")
    .build()
)

crossed = (
    ConditionBuilder.cross(genotype, treatment)
    .compare("Veh", "DrugA", within="WT")    # drug effect in WT
    .compare("Veh", "DrugA", within="KO")    # drug effect in KO
    .compare("WT", "KO", within="Veh")       # genotype effect, no drug
    .build()
)

Package structure

Module	Purpose
config.py	Global configuration (thresholds, pixel size, colors)
conditions.py	Experimental conditions, ConditionBuilder fluent DSL
markers.py	Data marker classes (Antibody, cellMarker, objectMarker)
experiment.py	Single-experiment CSV import, ROI processing, summary building
batch.py	Multi-experiment batch processing and merging
factory.py	High-level create_batch() with pickle caching
iteration.py	Composable iteration framework for analysis actions
plotting.py	Publication-quality plots (bar charts, heatmaps, spatial plots, image panels)
stats.py	Statistical testing (t-test, ANOVA, Kruskal-Wallis, post-hoc comparisons)
modelling.py	Iterative best-fit model selection with LOO cross-validation
export.py	Formatted Excel export with human-readable column names
serialization.py	Pickle save/load with cross-machine path resolution
image_io.py	Multi-backend image loading (tifffile, cv2, imageio, PIL)
_logging.py	Unified output system with verbosity control (set_verbosity, silent(), verbose())
utils.py	String, DataFrame, geometry, and plotting helpers

Controlling output

import PyFLASH

# Set verbosity: 0=error, 1=warning, 2=info (default), 3=hint, 4=debug
PyFLASH.set_verbosity('debug')

# Silence all output for a block
with PyFLASH.silent():
    batch.export_all_excel()

# Maximize detail for a block
with PyFLASH.verbose():
    batch.processData()

Citation

If you use PyFLASH in academic work, cite the software release you used:

Malcolm, J. (2026). PyFLASH: ImageJ confocal microscopy data processing and analysis pipeline
(Version 0.3.1) [Computer software]. https://github.com/Jay2owe/PyFLASH/releases/tag/v0.3.1
PyPI: https://pypi.org/project/PyFLASH-analysis/
Source: https://github.com/Jay2owe/PyFLASH

Machine-readable citation metadata is provided in CITATION.cff. When using a different release, cite its version and source tag instead.

Development and testing

From a checkout of this repository:

python -m pip install -e .
python -m pip install pytest
python -m pytest tests -q

For the optional browser interface, install .[ui] and run pyflash-ui. The documentation site covers usage and individual analysis functions; its sources are maintained in docs/wiki/.

Acknowledgements

Developed by Jamie Malcolm in the Brancaccio Lab at the UK Dementia Research Institute, Imperial College London.

This work was supported by the UK Dementia Research Institute, which receives its core funding from the UK Medical Research Council, the Alzheimer's Society, and Alzheimer's Research UK.

Data flow

Raw ImageJ exports (CSVs, ROI zips, images)
    → Experiment.processData()     — import, clean, compute colocalisation, build summary
    → Batch.processData()          — merge experiments, handle cross-experiment animals
    → Analysis & visualisation     — plot_mean_bars(), plot_matrices(), stats, modelling
    → Export                       — batch.export_all_excel(), save_state()

Expected data layout

Data Analysis/
├── Objects/           # CSV files for objectMarker data
├── Cells/             # CSV files for cellMarker data
├── ROI Intensities/   # CSV files for ROI-level Antibody data
├── Attributes/        # CSV files for generic Attribute data
├── ROIs/              # ImageJ ROI zip files
└── Images/            # Microscopy images organized by animal/marker