Quantifying Zebrafish Patterns

Code for quantifying spots and stripes using topological data analysis and machine learning

Authors: Melissa R. McGuirl, Alexandria Volkening, Bjorn Sandstede

For questions/comments please contact Melissa R. McGuirl at melissa_mcguirl@brown.edu.

Description

This software computes pattern statistics using topological data analysis and machine learning techniques. The input of this software is a collection of coordinate data from pigment cells.

This software is based upon the work presented in M.R. McGuirl, A. Volkening, and B. Sandstede, "Topological data analysis of zebrafish patterns," PNAS 2020, 117 (10) 5113-5124. https://www.pnas.org/content/117/10/5113

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Programs

• Ripser.py v0.3.2 (https://ripser.scikit-tda.org/index.html),
• Python
• Matlab

Python libraries

• ripser
• matplotlib
• numpy

Install the ripser program as follows:

	pip install Cython
	pip install Ripser

Source

      cd 
      git clone https://github.com/sandstede-lab/Quantifying_Zebrafish_Patterns
      cd Quantifying_Zebrafish_Patterns
      pip install -r requirements.txt

Data and Data samples:

A few MAT files from simulations of the agent-based model of A.V. and B.S. under the default parameter regime are provided as sample data in the data/sample_inputs/ folder. Complete datasets from full model simulations are freely available on figshare at www.figshare.com/projects/Zebrafish_simulation_data/72689.

      cd data/sample_inputs

1. Out_WT_default_1.mat
2. Out_pfef_default_1.mat
3. Out_shady_default_1.mat
4. Out_nacre_default_1.mat

Matlab examples:

Example scripts are provided to demonstrate how to generate input data and run the program from wild-type stripes and mutants.

      cd src/matlab/examples    

1. test_WT.m (quantify wild-type stripes)
2. test_pfeffer.m (quantify pfeffer spots)
3. test_shady.m (quantify shady spots)
4. test_nacre.m (quantify nacre spots)

Pipeline

The two main files are quantify_spots.m and quantify_stripes.m for quantifying spots and stripes, respectively.

Generating input data

The following steps are illustrated in the test examples in src/matlab/examples. After running test_WT.m, test_pfeffer.m, test_shady.m, or test_nacre.m, all of these steps will be complete. The distance matrices are saved in data/sample_dist_mats and the persistence diagrams should be saved in data/sample_barcodes/ after running Ripser in Python.

1. Load in cell-coordinate data to MATLAB (e.g. load data/sample_inputs/Out_WT_default_1.mat)
2. Extract cell-coordinate data at time point of interest
3. Save cell-coordinates as cells_mel, cells_iriL, cells_xanD, cells_xanL
4. Generate distance matrices of cell-cell pairwise distances and save as text file
5. Run Ripser using get_barcodes.py in src/python to get persistent homology data into PD_dir
6. Compute boundaryX and boundaryY, the right and top boundaries of the input domain (assume domain starts at origin). For the examples provided, these are all saved in the input .mat files as boundaryX(time_pt) and boundaryY(time_pt).
7. Specify persistence cut-off for betti number computation
8. For spots, specify cell-type used for quantifying spots
9. For stripes, get time series (1) of X^d cell locations (cellsXd_all), (2) number of X^d cells (numXand_all), and y-boundaries (boundayY_all) for identifying when new interstripes form.

Running the program

1. To quantify stripes: quantify_stripes(cells_mel, cells_iriL, cells_xanD, cells_xanL, mel1_dir, xanC1_dir, xanS1_dir, boundaryX, boundaryY, cellsXd_all, numXand_all, boundaryY_all)
2. To quantify spots: quantify_spots(cells_mel, cells_iriL, cells_xanD, cells_xanL, PD_dir, boundaryX, boundaryY, pers_cutoff, cell_type)

Full Documentation

For complete documentaion on this code, see Documentation_for_Quantifying_Zebrafish_Patterns_Code.pdf.

Notes

If the straightness measure is negative, divide by (max(top_bd_x)-min(top_bd_x)) instead of max(x_querys) in the calculation of top_cv and bottom_cv in straightness_measure.m. Negative straightness measures indicate erroneuous boundary detection, which will be improved in a future release of the software.