🔬 Phindr3D is a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) high content screening image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and data visualization.
Please cite the original manuscript if you find Phindr3D useful for your work:
Mergenthaler P, Hariharan S, Pemberton JM, Lourenco C, Penn LZ, Andrews DW (2021) Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning. PLoS Comput Biol 17(2): e1008630. https://doi.org/10.1371/journal.pcbi.1008630.
- To install the stand-alone program, download the executable Phindr3D.exe from the Releases in the Github repository. This executable includes Phindr3D and the Organoid Contour Segmentation app.
- The executable is built using the code written in Python, in a Python 3.10 environment. To run the Phindr3D Python code directly, download the repository, configure the Python environment to include the dependencies specified in the libraries file, and run the Python code here.
The full 3D microscopy data sets generated in this study were deposited to the Open Microscopy Image Data Resource repository (http://idr.openmicroscopy.org) under accession number idr0105 (in progress).
Test image data sets with an excerpt of the neuron and organoid data that will allow quick evaluation of the core features of Phindr3D were deposited to the open science platform Zenodo (https://zenodo.org; neuron data: DOI 10.5281/zenodo.4064148; MCF10A organoid data: DOI 10.5281/zenodo.4384912). Numerical data for the figure panels were deposited to Zenodo (https://zenodo.org; DOI: 10.5281/zenodo.4385040).
A basic user manual is available here. Visit our FAQ page for answers to common questions. Note that many answers may pertain to the earlier MATLAB implementation of Phindr3D.
The organoid contour segmentation feature of Phindr3D (the Python implementation) is provided within the Phindr3D application. Please see the Phindr3D manual here and Figure 5D or the Materials & Methods Section of the PLOS Computational Biology Paper for details.