triangles-segga

This repository contains python code to extract triangle-based cell shape anisotropy (see this publication for details) from biological image data segmented using SEGGA.

This code was developed for the research in: Anisotropy links cell shapes to a solid-to-fluid transition during convergent extension. Xun Wang, Matthias Merkel, Leo B. Sutter, Gonca Erdemci-Tandogan, M. Lisa Manning, Karen E. Kasza. bioRxiv, doi: 10.1101/781492 (2019).

Requirements

• a working installation of python
• the following python packages:
  • numpy
  • scipy
  • PyQt4 - used for drawing only and not absolutely necessary (to get rid of this dependency, just remove any imports of Drawing.py and NetworkDrawing.py from extractAverageQ.py)

Structure and usage

• This package contains a collection of data structures and routines to extract and display the cellular structure from SEGGA-segmented data, as well as compute a triangle-based cell shape tensor.
• One way to use this code is to start from extractAverageQ.py and adapt it to your needs.
• This repository contains the following python files:
  • Geometry/ folder containing routines to carry out geometric computations:
    • Point.py definition of a point
    • Nematic.py definition of a "nematic" (i.e. a symmetric, traceless tensor in 2D)
    • Triangle.py definition of a triangle and computation of triangle properties
  • Network.py definition of the cell network structure Network and conversion into a list of triangles
  • Segga.py loading of a SEGGA .mat file and translation into a Network structure
  • Drawing.py general routines to draw into a pdf file based on PyQt4
  • NetworkDrawing.py drawing routines for Network cells and triangles using the routines in Drawing.py
  • extractAverageQ.py example file reading a number of SEGGA .mat files, translating them into Networks, extracting the triangles, computing the average Q tensor, and drawing cell networks, triangles, and Q tensors on the original images