PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis

• URL: http://arxiv.org/abs/2409.12728v2
• Date: Fri, 20 Sep 2024 02:38:14 GMT
• Title: PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis
• Authors: Xinlei Huang, Zhiqi Ma, Dian Meng, Yanran Liu, Shiwei Ruan, Qingqiang Sun, Xubin Zheng, Ziyue Qiao,
• Abstract summary: We propose a novel spatial multi-modal omics resolved framework, termed PRototype-Aware Graph Adaptative Aggregation for Spatial Multi-modal Omics Analysis (PRAGA) PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge.
• Score: 1.1619559582563954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes with spatial context. Recently, graph neural networks based on K-nearest neighbor (KNN) graphs have gained prominence in spatial multi-modal omics methods due to their ability to model semantic relations between sequencing spots. However, the fixed KNN graph fails to capture the latent semantic relations hidden by the inevitable data perturbations during the biological sequencing process, resulting in the loss of semantic information. In addition, the common lack of spot annotation and class number priors in practice further hinders the optimization of spatial multi-modal omics models. Here, we propose a novel spatial multi-modal omics resolved framework, termed PRototype-Aware Graph Adaptative Aggregation for Spatial Multi-modal Omics Analysis (PRAGA). PRAGA constructs a dynamic graph to capture latent semantic relations and comprehensively integrate spatial information and feature semantics. The learnable graph structure can also denoise perturbations by learning cross-modal knowledge. Moreover, a dynamic prototype contrastive learning is proposed based on the dynamic adaptability of Bayesian Gaussian Mixture Models to optimize the multi-modal omics representations for unknown biological priors. Quantitative and qualitative experiments on simulated and real datasets with 7 competing methods demonstrate the superior performance of PRAGA.

Related papers

• How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method [11.719027225797037]
  We propose a novel textbfContrastive Learning-based textbfLink textbfPrediction model, textbfCLP. Our mymodel consistently outperforms the state-of-the-art models, demonstrating an average improvement of 10.10%, 13.44% in terms of AUC and AP.
  arXiv  Detail & Related papers  (2024-11-01T14:20:53Z)
• Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework [14.899157568336731]
  DeepBayesic is a novel framework that integrates Bayesian principles with deep neural networks to model the underlying distributions. We evaluate our approach on several mobility datasets, demonstrating significant improvements over state-of-the-art anomaly detection methods.
  arXiv  Detail & Related papers  (2024-10-01T19:02:06Z)
• GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation [68.63955715643974]
  Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o) We propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o)
  arXiv  Detail & Related papers  (2024-07-08T01:06:13Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• PGODE: Towards High-quality System Dynamics Modeling [40.76121531452706]
  This paper studies the problem of modeling multi-agent dynamical systems, where agents could interact mutually to influence their behaviors. Recent research predominantly uses geometric graphs to depict these mutual interactions, which are then captured by graph neural networks (GNNs) We propose a new approach named Prototypical Graph ODE to address the problem.
  arXiv  Detail & Related papers  (2023-11-11T12:04:47Z)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• Sparse Graphical Linear Dynamical Systems [1.6635799895254402]
  Time-series datasets are central in machine learning with applications in numerous fields of science and engineering. This work proposes a novel approach to bridge the gap by introducing a joint graphical modeling framework. We present DGLASSO, a new inference method within this framework that implements an efficient block alternating majorization-minimization algorithm.
  arXiv  Detail & Related papers  (2023-07-06T14:10:02Z)
• GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network [7.876789380671075]
  We propose a graph neural network approach with score-based method aiming at learning a sparse DAG. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference.
  arXiv  Detail & Related papers  (2023-01-28T02:49:13Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.