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Sheng X, Cai H, Nie Y, He S, Cheung YM, Chen J. Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025; 36:8577-8591. [PMID: 39178069 DOI: 10.1109/tnnls.2024.3439530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.
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Dai Q, Liu W, Yu X, Duan X, Liu Z. Self-Supervised Graph Representation Learning for Single-Cell Classification. Interdiscip Sci 2025:10.1007/s12539-025-00700-y. [PMID: 40180773 DOI: 10.1007/s12539-025-00700-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2024] [Revised: 03/02/2025] [Accepted: 03/04/2025] [Indexed: 04/05/2025]
Abstract
Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it is imperative to develop computational biology methods for cell classification. However, it remains a challenge for existing methods to adequately utilize the potential gene expression information within the vast amount of unlabeled cell data, which limits their classification and generalization performance. Therefore, we propose a novel self-supervised graph representation learning framework for single-cell classification, named scSSGC. Specifically, in the pre-training stage of self-supervised learning, multiple K-means clustering tasks conducted on unlabeled cell data are jointly employed for model training, thereby mitigating the issue of limited labeled data. To effectively capture the potential interactions among cells, we introduce a locally augmented graph neural network to enhance the information aggregation capability for nodes with fewer neighbors in the cell graph. A range of benchmark experiments demonstrates that scSSGC outperforms existing state-of-the-art cell classification methods. More importantly, scSSGC provides stable performance when faced with cross-datasets, indicating better generalization ability.
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Affiliation(s)
- Qiguo Dai
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, China.
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, 116650, China.
| | - Wuhao Liu
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, China
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, 116650, China
| | - Xianhai Yu
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116650, China
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, 116650, China
| | - Xiaodong Duan
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, 116650, China
| | - Ziqiang Liu
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, Dalian, 116650, China
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China
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Li S, Hua H, Chen S. Graph neural networks for single-cell omics data: a review of approaches and applications. Brief Bioinform 2025; 26:bbaf109. [PMID: 40091193 PMCID: PMC11911123 DOI: 10.1093/bib/bbaf109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 02/09/2025] [Accepted: 02/25/2025] [Indexed: 03/19/2025] Open
Abstract
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.
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Affiliation(s)
- Sijie Li
- School of Mathematical Sciences and The Key Laboratory of Pure Mathematics and Combinatorics, Ministry of Education (LPMC), Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300071, China
| | - Heyang Hua
- School of Mathematical Sciences and The Key Laboratory of Pure Mathematics and Combinatorics, Ministry of Education (LPMC), Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300071, China
| | - Shengquan Chen
- School of Mathematical Sciences and The Key Laboratory of Pure Mathematics and Combinatorics, Ministry of Education (LPMC), Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300071, China
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Huang YA, Li YC, You ZH, Hu L, Hu PW, Wang L, Peng Y, Huang ZA. Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation. BMC Biol 2025; 23:23. [PMID: 39849579 PMCID: PMC11756145 DOI: 10.1186/s12915-025-02128-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Accepted: 01/13/2025] [Indexed: 01/25/2025] Open
Abstract
BACKGROUND Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks. RESULTS We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations. CONCLUSIONS scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell-cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.
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Affiliation(s)
- Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710000, China.
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518063, China.
| | - Yue-Chao Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710000, China
| | - Zhu-Hong You
- School of Electronic Information, Xijing University, Xi'an, 710000, China.
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China
| | - Lei Wang
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530001, China
| | - Yuzhong Peng
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China.
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Knudsen JE, Rich JM, Ma R. Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma. Urol Clin North Am 2024; 51:47-62. [PMID: 37945102 DOI: 10.1016/j.ucl.2023.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.
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Affiliation(s)
- J Everett Knudsen
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA
| | - Joseph M Rich
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA
| | - Runzhuo Ma
- Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Center for Robotic Simulation & Education, University of Southern California, Los Angeles, CA, USA.
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Chen Y, Zheng R, Liu J, Li M. scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data. Brief Bioinform 2024; 25:bbae101. [PMID: 38493339 PMCID: PMC10944569 DOI: 10.1093/bib/bbae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 01/03/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.
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Affiliation(s)
- Yuxuan Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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