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A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification

Jingqi Lu, Keqi Han, Yun Wang, Lu Mi, Carl Yang

TL;DR

A benchmark for Caenorhabditis elegans neuron classification is established, comparing four graph methods against four non-graph methods and highlighting that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes.

Abstract

This study establishes a benchmark for Caenorhabditis elegans neuron classification, comparing four graph methods (GCN, GraphSAGE, GAT, GraphTransformer) against four non-graph methods (Logistic Regression, MLP, LOLCAT, NeuPRINT). Using the functional connectome, we classified Sensory, Interneuron, and Motor neurons based on Spatial, Connection, and Neuronal Activity features. Results show that attention-based GNNs significantly outperform baselines on the Spatial and Connection features. The Neuronal Activity features yielded poor performance, likely due to the low temporal resolution of the underlying neuronal activity data. Our benchmark validates the use of GNNs and highlights that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes. Code is available at: https://github.com/JingqiLuu/neuronclf-gnn-benchmark.

A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification

TL;DR

A benchmark for Caenorhabditis elegans neuron classification is established, comparing four graph methods against four non-graph methods and highlighting that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes.

Abstract

This study establishes a benchmark for Caenorhabditis elegans neuron classification, comparing four graph methods (GCN, GraphSAGE, GAT, GraphTransformer) against four non-graph methods (Logistic Regression, MLP, LOLCAT, NeuPRINT). Using the functional connectome, we classified Sensory, Interneuron, and Motor neurons based on Spatial, Connection, and Neuronal Activity features. Results show that attention-based GNNs significantly outperform baselines on the Spatial and Connection features. The Neuronal Activity features yielded poor performance, likely due to the low temporal resolution of the underlying neuronal activity data. Our benchmark validates the use of GNNs and highlights that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes. Code is available at: https://github.com/JingqiLuu/neuronclf-gnn-benchmark.
Paper Structure (12 sections, 2 equations, 1 figure, 1 table)

This paper contains 12 sections, 2 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Dataset
  4. Feature Construction
  5. Model Architectures
  6. Implementation Details
  7. Results
  8. Comparing Features
  9. Comparing Methods
  10. Conclusion, Limitation and Future Works
  11. Compliance with ethical standards
  12. Acknowledgments

Figures (1)

  • Figure 1: Visualization of C. elegans neuron data features and class distributions. (a) Example neuronal activity data for representative Sensory, Interneuron, and Motor neurons. (b) Normalized probability density distributions of cosine similarity, comparing the Spatial and Connection features across all neuron pairs. (c) Magnified view of the spatial distribution of neuron classes in the head region. (d) Spatial distribution of the three neuron classes along the entire body.