PyFLASH-analysis 0.1.4

ImageJ confocal microscopy data processing and analysis pipeline

PyFLASH

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

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

License

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

Requires: Python ≥ 3.9

Dependencies: pandas, numpy, matplotlib, seaborn, scipy, statsmodels, scikit-posthocs, openpyxl, read-roi, Pillow

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.

Quick start

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

rc_params()

# 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, specificity=('Time', 'WeekEight'))
plot_matrices(batch, cols)

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

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:

Jamie Malcolm. PyFLASH: ImageJ confocal microscopy data processing and analysis pipeline.
PyPI: https://pypi.org/project/PyFLASH-analysis/
Source: https://github.com/Jay2owe/PyFLASH

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