Integrated Processing and Analysis Toolkit (iPA)

iPA Workflow Overview

iPA is an integrated toolkit for processing and analysis of multimodal cell images. It provides a comprehensive workflow from raw image loading to advanced quantitative analysis, supporting multiple imaging modalities including cryo-electron tomography (cryo-ET), soft X-ray tomography (SXT), structured illumination microscopy (SIM), and widefield microscopy (WFM).

Key Features

  • Multi-Modal Support: Unified interface for cryo-ET, SXT, SIM, and WFM data

  • Automated Segmentation: Deep learning-based organelle segmentation with pre-trained models

  • Spatial Partitioning: Novel radial cytoplasmic partitioning algorithm for standardized spatial analysis

  • Quantitative Analysis: Four-dimensional analysis framework:

    • Morphology: Volume, surface area, length, shape descriptors

    • Spatial Arrangement: Radial distribution functions (RDF), docking analysis, clustering

    • Dynamics: Velocity analysis, motion patterns (future: MSD, trajectory reconstruction)

    • Interactions: 14 predefined organelle interaction types with distance and contact analysis

  • Self-Supervised Denoising: Noise2Void (N2V) and Noise2Noise (N2N) for improved image quality

  • Comprehensive Logging: Dual-output logging system (file + console) with Docker support

Supported Imaging Modalities

Modality

Description

Key Applications

Cryo-ET

Cryo-electron tomography High-resolution 3D structures

Filament analysis, membrane segmentation, vesicle docking

SXT

Soft X-ray tomography Natural contrast, thick specimens

Whole-cell segmentation, ISG analysis, radial partitioning

SIM

Structured illumination microscopy ~100 nm resolution

Super-resolution ER, mito, ISG segmentation

WFM

Widefield microscopy Standard fluorescence imaging

General organelle analysis, multi-channel imaging

Quick Start

Basic Workflow Example:

from ipa.data_loader import UniversalDataLoader
from ipa.processing.segmentation import SXTCellSegmenter
from ipa.processing.partitioning import Partitioning
from ipa.analysis import calculate_rdf_from_xvg

# Step 1: Load data
volume = UniversalDataLoader.load_data('sxt_volume.mrc')
pm_mask = UniversalDataLoader.load_data('plasma_membrane.mrc')
ne_mask = UniversalDataLoader.load_data('nuclear_envelope.mrc')

# Step 2: Segment organelles
segmenter = SXTCellSegmenter(device='cuda')
segmenter.load_model()
results = segmenter.predict(volume)

# Step 3: Create radial partitions
partitioner = Partitioning(root_dir="results/", n_slices=8)
center, ne_edge, pm_edge = partitioner.extract_ne_pm_edges(pm_mask, ne_mask)
partition_mask = partitioner.create_nepm_radial_partitions(
    ne_edge, pm_edge,
    shape=volume.shape,
    n_slices=8,
    pm_mask=pm_mask,
    ne_mask=ne_mask
)

# Step 4: Analyze spatial distribution
# (Calculate RDF for organelles in each partition)
rdf_results = calculate_rdf_from_xvg(...)

For more examples, see the examples section.

Documentation Structure

Data Loading (Data Loader Module)

Universal data loader supporting MRC, TIFF, NPZ, LIF, CZI, ND2 formats with automatic format detection.

Image Processing (Processing Module)
Image Analysis (Analysis Module)

Installation

Install from source:

git clone https://github.com/SunLab-SH/iPA.git
cd iPA
pip install -e .

Core dependencies are automatically installed. For segmentation modules, PyTorch is required.

Logging System

iPA includes a comprehensive logging system with dual output (file + console):

from ipa.data_loader import QuickLogger

logger = QuickLogger("my_analysis", log_dir="logs", console_output=True)
logger.step("Starting analysis...")
logger.file_in("input.mrc")
# ... analysis code ...
logger.file_out("output.mrc")

Logs are saved as timestamped files in the logs/ directory with UTF-8 encoding.

Citation

If you use iPA in your research, please cite:

@software{iPA2024,
  title={Integrated Processing and Analysis Toolkit for Multi-Scale Cellular Imaging},
  author={Li, Angdi and others},
  year={2024},
  url={https://github.com/SunLab-SH/iPA}
}

Table of Contents

User Guide

Indices and tables