diff_predictor

Description

Diff_predictor is a package to work in tandem with diff_classifier (https://github.com/Nance-Lab/diff_classifier). This package contains tools for prediction and analysis of multiple particle tracking data of nanoparticles taken from biological tissue imaging.

Organization of the package

The project has the following structure:

|- diff_predictor/
  |- tests/
  |- __init__.py
  |- data_process.py
  |- dataio.py
  |- eval.py
  |- predxgboost.py
  |- temporal.py
  |- version.py
|- notebooks/
  |- demo_notebooks
  |- publication_notebooks
|- LICENSE
|- README.md
|- requirements.txt
|- setup.py

Installation

Pip install

Install diff_predictor with pip

  pip install diff_predictor
  cd my-project

Cloning

Users can clone a copy of diff_predictor with the command

  git clone https://github.com/Nance-Lab/diff_predictor.git

Running the setup file will install needed dependencies:

  python setup.py develop

GPU Computing

Default installation uses CPU computing. It is recomended to run xgboost and tensorflow commands using gpu enabled computing. To do this see:

https://www.tensorflow.org/install/gpu

Analysis Notebooks

Data analysis was performed male Sprague-Dawley (SD) rat pups at varying ages, depending on the specific study. These analysis notebooks can be found in the diff_predictor/notebooks folder of the package. Individual slices were plated on 30-mm cell culture inserts in non-treated 6-well plates. Prior to plating, 6-well plates were filled with 1 mL SCM. Slices were incubated in sterile conditions at 37°C and 5% CO2. Predictive analysis tested for pup age and for brain region.

All MPT studies were performed within 24 h of slice preparation. Slices were imaged in a temperature-controlled incubation chamber maintained at 37°C, 5% CO2, and 80% humidity. 30 minutes (min) prior to video acquisition, 40nm polystyrene nanoparticles conjugated with poly(ethylene-glycol) (PS-PEG) were diluted in 1x phosphate-buffered saline (PBS) to a concentration of ~0.014%. Nanoparticles were injected into each slice using a 10 µL glass syringe (model 701, cemented needle, 26-gauge, Hamilton Company, Reno, NV). Videos were collected at 33 frames-per-second and 100x magnification for 651 frames via fluorescent microscopy using a cMOS camera (Hamamatsu Photonics, Bridgewater, NJ) mounted on a confocal microscope. Nanoparticle trajectories and trajectory mean square displacements (MSDs) were calculated via diff_classifier (https://github.com/Nance-Lab/diff_classifier), a Python package developed within Nance Lab.

References

1. Curtis, C., A. Rokem, and E. Nance, diff_classifier: Parallelization of multi-particle tracking video analyses. Journal of open source software, 2019. 4(36): p. 989.
2. Shapley, L.S., A value for n-person games. Contributions to the Theory of Games, 1953. 2(28): p. 307-317.