infer_subc

About The Project

infer_subc

• aims to create a simple, extensible, and reproducible workflow to measure (or infer) the shape, position, size, and interaction of several sub-cellular components. These data can then be applied later to better understand the spatial coordination of these structures and the interactome during key biological processes.

• is part of a larger collaboration between the CZI Neurodegeneration Challenge Network (NDCN) Data Science Concierge program and the Cohen lab at UNC (website, github) to migrate a multispectral imaging dataset of iPSCs which identifies sub-cellular components to a scalable cloud-based pipeline.

infer_subc Workflow

The staring point of this workflow is a set of multichannel images, where each channel labels a different sub-cellular component. The workflow can then be completed in a suggested series of steps, outlined in the notebooks below.

Identify a single cell of interest

1. Infer cellmask (🚨 Steps 2-9 depend on establishing a good solution here)
2. Infer nuclei
3. Infer cytoplasm

Segment each of the organelles

4. Infer lysosomes
5. Infer mitochondria
6. Infer golgi complex
7. Infer peroxisomes
8. Infer endoplasmic reticulum
9. Infer lipid bodies

Built With

A quick note on tools and resources used.

• napari-allencell-segmenter -- We are leveraging the framework of the napari-allencell-segmenter plugin, which enables powerful 3D image segmentation while taking advantage of the napari graphical user interface.
• aicssegmentation -- We call the aicssegmentation package directly.
• napari -- Used as the visualization framework, a fast, interactive, multi-domensional image viewer for Python.
• scipy -- Image analysis
• scikit-image -- Image analysis
• itk -- Image analysis
• numpy -- Under the hood computation
• Alzheimer's Disease AD Workbench -- We initially wanted to use the ADDI's ADWB as a method of data sharing and to serve as a computational resource.

ADWB hints

Given that the github repos are not yet whitelisted, the source directory needs to be zipped and uploaded in order to make an "editable" pip install.

uploading guide uploading files via the workspace article. Using BLOB storage

Getting Started

Prerequisites

The following are prerequisites and should be installed prior to using the workflow.

• napari

  pip install "napari[all]"
  

• scipy

  python -m pip install scipy
  

• scikit-image

  pip install scikit-image
  

• itk

  pip install itk
  

• numpy

  pip install numpy
  

Installation

infer_subc is not yet available on PyPI so must be be pip ionstalled from source

pip install git+https://github.com/ndcn/infer-subc.git

Usage

from infer_subc.organelles import infer_lyso
from infer_subc.core.file_io import read_czi_image

img_data,meta_dict = read_czi_image("path/to/image.czi")
lyso_obj =  infer_lyso(img_data) 

🚧 WIP 🚧 (🚨🚨🚨🚨 )

NOTE: command line capabilities not implimented

$ python -m infer_subc
#or
$ infer_subc

Roadmap

• Create PyPI package
• Update installation instructions to reflect optimal use of conda environments

Development

Read the CONTRIBUTING.md file.

License

Distributed under the Unlicense license. See LICENSE for more information.

Issues

If you encounter any problems, please file an issue with a detailed description.