Graph Metric Learning Quantifies Morphological Differences between Two Genotypes of Shoot Apical Meristem Cells in Arabidopsis

Journal Article · Mon Jan 30 00:00:00 EST 2023 · in silico Plants

DOI:https://doi.org/10.1093/insilicoplants/diad001· OSTI ID:1923648

Scott, Cory Braker ^[1]; Mjolsness, Eric ^[2]; Oyen, Diane ^[3]; Kodera, Chie ^[4]; Uyttewaal, Magalie ^[5]; Bouchez, David ^[5]

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1. Colorado College, Colorado Springs, CO (United States); University of California, Irvine, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
2. University of California, Irvine, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
3. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
4. Université Paris-Saclay, Versailles (France); Univ. Paris-Saclay, Le Kremlin-Bicetre (France)
5. Université Paris-Saclay, Versailles (France)

We present a method for learning “spectrally descriptive” edge weights for graphs. We generalize a previously known distance measure on graphs (Graph Diffusion Distance), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of Arabidopsis thaliana specimens: wild-type and trm678 triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produces a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple k-nearest-neighbors classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant vs. wild-type Arabidopsis cells. We find that trm678 mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: Human Frontiers Science Program; National Institutes of Health (NIH); Saclay Plant Sciences; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1923648
• Report Number(s): LA-UR-23-20347
• Journal Information: in silico Plants, Journal Name: in silico Plants Journal Issue: 1 Vol. 5; ISSN 2517-5025
• Publisher: Oxford University Press
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
cell morphology
graph metrics
morphodynamics
neural networks
spectral graph theory