DevoGraph

Introduction

DevoGraph is a GSoC 2022 project under the administration of INCF and DevoWorm. Our main goal is to provide examples and components that utlize (Temporal/Directed/...) Graph Neural Networks to model the developmental process of C. elegans.

Design a KNN-based method constructing temporal graphs. The method is implemented in ./devograph/datasets/datasets.py. These temporal graphs are based on 3d positions of cell centroids and mimic cell developmental process of C. elegans. Each node represents a cell at a certain frame, and edges at the same frame connect neighbors according to KNN while edges across different frames connect mother and daughter cells. Please refer to ./stage_2/stage_2.ipynb to check more details.
Refactor codes of constructing directed graphs initially implemented by cell-track-gnn. The re-implementation is in ./devograph/datasets/datasets1.py. This method gives each edge an direction implying the relationship between mother and daughter cells.
Refactor codes of a directed GNN initially implemented by cell-track-gnn. The re-implementation is in ./devograph/models/ct.py. The GNN is based on directed graphs and incorporates information of nodes and edges to aggregate messages.
Both of re-implementations above abstract the core logic, remove redundant and unrelated codes and unnecessary third-party frameworks, and finally provide easy-to-use APIs.
Design the whole pipeline of DevoGraph presented in ./miscellaneous/GSoC 2022 22.1.pdf.
Assign tasks to other participants.

Refactor codes of pre-processing 2-D images(frames of videos) and converting them into location information of cells stored in csv files. The re-implementation is based on cell-track-gnn. Please refer to ./stage_1/ and ./stage_1/stage1_by_longhui for more details.
Note that in ./stage_1/, ./stage_1/stage1_by_longhui is implemented by Longhui and other files are implemented by Wataru.