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Zhang X, Zhang H, Wang Z, Ma X, Luo J, Zhu Y. PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping. BMC Bioinformatics 2023; 24:490. [PMID: 38129803 PMCID: PMC10740247 DOI: 10.1186/s12859-023-05595-4] [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: 06/07/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Clustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC). RESULTS The PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e-05), while using the Calinski-Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/ . CONCLUSIONS PWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.
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Affiliation(s)
- Xiaomeng Zhang
- Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China
| | - Hongtao Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Zhihao Wang
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Xiaofei Ma
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China
| | - Jiancheng Luo
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430070, Hubei Province, China.
| | - Yingying Zhu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China.
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Hu Y, Liu J, Yu J, Yang F, Zhang M, Liu Y, Ma S, Zhou X, Wang J, Han Y. Identification and validation a costimulatory molecule gene signature to predict the prognosis and immunotherapy response for hepatocellular carcinoma. Cancer Cell Int 2022; 22:97. [PMID: 35193632 PMCID: PMC8864933 DOI: 10.1186/s12935-022-02514-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/05/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients. METHODS Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan-Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases. RESULTS All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues. CONCLUSION In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.
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Affiliation(s)
- Yinan Hu
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Jingyi Liu
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Jiahao Yu
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Fangfang Yang
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Miao Zhang
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Yansheng Liu
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Shuoyi Ma
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Xia Zhou
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Jingbo Wang
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China
| | - Ying Han
- Institute of Digestive Diseases, Xijing Hospital, Air Force Medical University, Xi'an, 710032, Shaanxi, China.
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Wang J, Lu CH, Kong XZ, Dai LY, Yuan S, Zhang X. Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data. BMC Bioinformatics 2022; 22:334. [PMID: 35057729 PMCID: PMC8772048 DOI: 10.1186/s12859-021-04220-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 05/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. RESULTS In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. CONCLUSIONS Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.
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Affiliation(s)
- Juan Wang
- School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Cong-Hai Lu
- School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xiang-Zhen Kong
- School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Ling-Yun Dai
- School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Shasha Yuan
- School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, 264025 China
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Liu Q. A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection. BMC Bioinformatics 2022; 22:436. [PMID: 35057728 PMCID: PMC8772046 DOI: 10.1186/s12859-021-04333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/23/2021] [Indexed: 12/24/2022] Open
Abstract
Background Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or contain a lot of noise. Results To address this drawback, a novel LRR model named TGLRR is proposed by integrating the truncated nuclear norm with graph-Laplacian. Different from the nuclear norm minimizing all singular values, the truncated nuclear norm only minimizes some smallest singular values, which can dispel the harm of shrinkage of the leading singular values. Finally, an efficient algorithm based on Linearized Alternating Direction with Adaptive Penalty is applied to resolving the optimization problem. Conclusions The results show that the TGLRR method exceeds the existing state-of-the-art methods in aspect of tumor clustering and gene selection on integrated gene expression data.
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Swamy KBS, Schuyler SC, Leu JY. Protein Complexes Form a Basis for Complex Hybrid Incompatibility. Front Genet 2021; 12:609766. [PMID: 33633780 PMCID: PMC7900514 DOI: 10.3389/fgene.2021.609766] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/20/2021] [Indexed: 12/20/2022] Open
Abstract
Proteins are the workhorses of the cell and execute many of their functions by interacting with other proteins forming protein complexes. Multi-protein complexes are an admixture of subunits, change their interaction partners, and modulate their functions and cellular physiology in response to environmental changes. When two species mate, the hybrid offspring are usually inviable or sterile because of large-scale differences in the genetic makeup between the two parents causing incompatible genetic interactions. Such reciprocal-sign epistasis between inter-specific alleles is not limited to incompatible interactions between just one gene pair; and, usually involves multiple genes. Many of these multi-locus incompatibilities show visible defects, only in the presence of all the interactions, making it hard to characterize. Understanding the dynamics of protein-protein interactions (PPIs) leading to multi-protein complexes is better suited to characterize multi-locus incompatibilities, compared to studying them with traditional approaches of genetics and molecular biology. The advances in omics technologies, which includes genomics, transcriptomics, and proteomics can help achieve this end. This is especially relevant when studying non-model organisms. Here, we discuss the recent progress in the understanding of hybrid genetic incompatibility; omics technologies, and how together they have helped in characterizing protein complexes and in turn multi-locus incompatibilities. We also review advances in bioinformatic techniques suitable for this purpose and propose directions for leveraging the knowledge gained from model-organisms to identify genetic incompatibilities in non-model organisms.
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Affiliation(s)
- Krishna B. S. Swamy
- Division of Biological and Life Sciences, School of Arts and Sciences, Ahmedabad University, Ahmedabad, India
| | - Scott C. Schuyler
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Head and Neck Surgery, Department of Otolaryngology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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