SVF and svf2MaMuT

This repository contains the Statistical Vector Flow (SVF) package and the tissue propagation software published in In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Now compatible with Python 3.x, it has been updated for easier terminal/console use, it can handle complex path names, is compatible with MaMuT v7, and there are other bugs fixes. Note svf2MaMuT is now integrated into this SVF repository for easy setup.

Description of the repository

Folders:

• IO: The class SpatialImage, a container for images and input/output. When the right external libraries are installed (see bellow), can read tiff, hdf5, klb and inr images.
• TGMMlibraries: The class lineageTree, a container for lineage trees and Statistical Vector Flow (SVF). Can read output data from TGMM.
• config-files: Example of parameter files for each scripted algorithm.

Python files to be run IN ORDER:

1. SVF-prop.py: builds Statistical Vector Flow from a TGMM dataset.
2. tissue-bw-prop.py: propagates tissue information from a manually annotated 3D image.
3. SVF2MaMuT.py: exports results to MaMuT format for quantification and visualization.

Basic usage

Each of the python scripts proposed here can be run from a terminal in the sequence:

python3 /path/to/SVF/SVF-prop.py config-files/SVF-prop-config.txt

python3 /path/to/SVF/tissue-bw-prop.py config-files/tissue-bw-prop-config.txt

python3 /path/to/SVF/SVF2MaMuT.py config-files/svf2MM-config.txt [--only=x,y] [--exclude=x,y] where x and y are tissue mask colors intended to create a subset by tissue

The user should modify the parameter files prior to running with the correct information.

Dependencies

Some dependecies are requiered:

• general python dependecies:
  • numpy, scipy, pandas
• SVF-prop.py:
  • TGMMlibraries installed (see TGMMlibraries README.md)
• tissue-bw-prop.py:
  • TGMMlibraries installed (see TGMMlibraries README.md)
  • IO library installed (see IO README.md)
• SVF2MaMuT.py:
  • TGMMlibraries installed (see TGMMlibraries README.md)
  • Blank Dataset.tar.gz contains an empty BigDataViewer dataset that is needed as a template.

Quick install

Install IO and TGMMlibraries packages:

cd ~/Downloads
git clone https://github.com/GuignardLab/IO
git clone https://github.com/leoguignard/TGMMlibraries
cd IO
sudo python3 setup.py install
cd ../TGMMlibraries
sudo python3 setup.py install
cd ..

Install-free preparation of SVF:

git clone https://github.com/mhdominguez/SVF

Unpack blank dataset for SVF / MaMuT reconstructions:

cd SVF
tar -xzvf Blank\ Dataset.tar.gz