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Low-rank Interpretable Cell–Cell Hidden Interactions from Embeddings

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
cell–cell interactionslive-cell imaginginteraction networksdynamical systemsdata-driven modelinginterpretable representationlow-rank optimization
Abstract:

Multicellular organisms rely on continuously changing cell–cell interactions that govern critical biological processes as cells modify their internal states and trajectories in space over time. Studying these interactions is critical to understand development, homeostasis, and disease progression. Live-cell imaging provides a unique opportunity to directly observe these dynamical events; however, current computational approaches often fail to model complex, time-varying events involving diverse populations and spatial contexts. Here, we present LICCHIE, a model designed to infer time-changing, feature-based cell-cell interactions, applicable across systems and conditions. Our approach represents each cell with a dynamic multi-feature vector, and interactions are modeled as spatially constrained, directed influences between cell pairs, evolving over time. We optimize the model using an iterative scheme balancing data fidelity, interactions smoothness, and low-rank sparse structure. We validated LICCHIE’s ability to capture cellular interactions across populations in a controlled synthetic setting and applied it to real-world 3D live-cell imaging of patient-derived tumor organoids to (1) identify components with interpretable structures that capture interaction type and directionality, and (2) suggest modulation strategies that may accelerate Natural Killer (NK) polarization and tumor cell death.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces LICCHIE, a framework for inferring time-varying, feature-based cell-cell interactions from live imaging data. It resides in the 'Low-Rank Interaction Modeling' leaf under 'Computational Inference and Causal Discovery Methods'. Notably, this leaf contains only the original paper itself—no sibling papers were identified in the taxonomy. This isolation suggests the specific combination of low-rank decomposition, spatially constrained directed influences, and iterative multi-regularization optimization occupies a relatively sparse niche within the broader computational inference landscape.

The taxonomy reveals neighboring leaves focused on information-theoretic causal discovery, probabilistic dynamics inference, and molecular-level FRET-based methods. LICCHIE diverges from these by emphasizing low-rank matrix structure and interpretable source-target motifs rather than causal graphs or stochastic physical models. The broader 'Computational Inference' branch includes four distinct methodological directions, indicating that while interaction inference is an active area, the low-rank decomposition approach represents a less crowded path compared to causal discovery or deep learning segmentation pipelines.

Among thirty candidates examined, none were found to clearly refute any of the three core contributions: the LICCHIE modeling framework, the low-rank decomposition with interpretable motifs, and the iterative optimization scheme. Each contribution was assessed against ten candidates, with zero refutable overlaps identified. This limited search scope—top-K semantic matches plus citation expansion—suggests that within the examined literature, no prior work directly anticipates LICCHIE's specific combination of temporal smoothness, spatial constraints, and low-rank sparse structure for multi-feature cell representations.

Given the restricted search scale and the absence of sibling papers in the taxonomy leaf, the analysis indicates LICCHIE occupies a relatively novel position within the examined corpus. However, the limited candidate pool and the possibility of relevant work outside the top-thirty semantic matches mean this assessment reflects the available evidence rather than an exhaustive field survey. The framework's integration of multiple regularization constraints and interpretable interaction motifs appears distinctive among the methods reviewed.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: inferring time-varying feature-based cell-cell interactions from live imaging data. The field encompasses a diverse set of approaches organized around five main branches. Computational Inference and Causal Discovery Methods focus on extracting interaction networks and causal relationships from temporal data, often employing statistical or machine learning frameworks to model how cellular features influence one another over time. Deep Learning-Based Image Segmentation and Tracking leverage neural architectures to automate the identification and following of individual cells across image sequences, while Non-Deep Learning Segmentation and Tracking rely on classical computer vision and algorithmic techniques. Experimental Imaging Platforms and Biological Applications emphasize the design of microscopy systems and biosensors that capture dynamic cellular behaviors in various biological contexts, from immune responses to tissue morphogenesis. Finally, Methodological Considerations and Biosensor Technologies address the practical challenges of resolution, noise, and reporter design that underpin reliable inference. Together, these branches reflect a pipeline from raw imaging through segmentation and tracking to higher-level interaction modeling, with representative works such as CausalXtract[2] and TIMING[5] illustrating computational inference strategies, and Deep Learning Cell Interactions[6] exemplifying modern segmentation efforts. Within the computational inference branch, a particularly active line of work explores how to represent complex, high-dimensional interaction patterns in a tractable form. Low-rank Cell Interactions[0] addresses this challenge by modeling cell-cell influences through low-rank matrix factorizations, thereby capturing dominant interaction modes without overfitting to noise. This approach contrasts with methods like CausalXtract[2], which emphasizes causal discovery from time-series features, and Optimal FRET Inference[1], which focuses on biosensor-based readouts of molecular interactions. By reducing dimensionality, Low-rank Cell Interactions[0] sits at the intersection of statistical modeling and biological interpretability, offering a complementary perspective to fully data-driven deep learning pipelines and to more mechanistic causal frameworks. The trade-offs among these strategies—scalability versus interpretability, model complexity versus generalization—remain central open questions as researchers seek to integrate segmentation, tracking, and inference into unified workflows for understanding dynamic multicellular systems.

Claimed Contributions

LICCHIE modeling framework for time-varying cell-cell interactions

The authors introduce a computational framework that models direct, time-varying, and feature-level interactions between cells from live-cell imaging data. The model represents each cell with a dynamic feature vector and captures interactions as spatially constrained, directed influences between cell pairs that evolve over time, operating in feature space rather than requiring persistent cell identities.

10 retrieved papers
Low-rank decomposition with interpretable source-target motifs

The method decomposes pairwise interaction matrices into a small set of rank-1 components representing shared biological processes. Each component consists of source and target effect vectors, modulated by time- and cell-pair-varying sparse weights, balancing expressivity with biological interpretability while maintaining parsimony.

10 retrieved papers
Iterative optimization scheme with multiple regularization constraints

The authors develop an optimization approach that jointly estimates interaction matrices, shared components, and sparse weights through alternating minimization. The scheme incorporates regularization for temporal smoothness in feature space, low-rank structure enforcement, and sparsity constraints to ensure biologically meaningful and interpretable results.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

LICCHIE modeling framework for time-varying cell-cell interactions

The authors introduce a computational framework that models direct, time-varying, and feature-level interactions between cells from live-cell imaging data. The model represents each cell with a dynamic feature vector and captures interactions as spatially constrained, directed influences between cell pairs that evolve over time, operating in feature space rather than requiring persistent cell identities.

Contribution

Low-rank decomposition with interpretable source-target motifs

The method decomposes pairwise interaction matrices into a small set of rank-1 components representing shared biological processes. Each component consists of source and target effect vectors, modulated by time- and cell-pair-varying sparse weights, balancing expressivity with biological interpretability while maintaining parsimony.

Contribution

Iterative optimization scheme with multiple regularization constraints

The authors develop an optimization approach that jointly estimates interaction matrices, shared components, and sparse weights through alternating minimization. The scheme incorporates regularization for temporal smoothness in feature space, low-rank structure enforcement, and sparsity constraints to ensure biologically meaningful and interpretable results.

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