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Wu J, Nie Q, Li G, Zhu K. Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm. BMC Bioinformatics 2023; 24:211. [PMID: 37221474 DOI: 10.1186/s12859-023-05319-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/04/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. RESULTS In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein-Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with "inclusion" relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the "inclusion" relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. CONCLUSIONS The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.
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Affiliation(s)
- Jingli Wu
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China.
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China.
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
| | - Qinghua Nie
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
| | - Gaoshi Li
- Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
| | - Kai Zhu
- Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China
- College of Computer Science and Engineering, Guangxi Normal University, Guilin, China
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A nonlinear model and an algorithm for identifying cancer driver pathways. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Wu J, Zhu K, Li G, Wang J, Cai Q. A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02330-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractIt is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.
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Tian J, Zhao J, Zheng C. Clustering of cancer data based on Stiefel manifold for multiple views. BMC Bioinformatics 2021; 22:268. [PMID: 34034643 PMCID: PMC8152349 DOI: 10.1186/s12859-021-04195-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 05/12/2021] [Indexed: 12/23/2022] Open
Abstract
Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.
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Affiliation(s)
- Jing Tian
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Jianping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
| | - Chunhou Zheng
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.,School of Computer Science and Technology, Anhui University, Hefei, China
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Zhang W, Zeng Y, Wang L, Liu Y, Cheng YN. An Effective Graph Clustering Method to Identify Cancer Driver Modules. Front Bioeng Biotechnol 2020; 8:271. [PMID: 32318558 PMCID: PMC7154174 DOI: 10.3389/fbioe.2020.00271] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 03/16/2020] [Indexed: 12/15/2022] Open
Abstract
Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.
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Affiliation(s)
- Wei Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
| | - Yifu Zeng
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Yue Liu
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, China
| | - Yi-Nan Cheng
- College of Science, Southern University of Science and Technology, Shenzhen, China
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Wu MJ, Gao YL, Liu JX, Zhu R, Wang J. Principal Component Analysis Based on Graph Laplacian and Double Sparse Constraints for Feature Selection and Sample Clustering on Multi-View Data. Hum Hered 2019; 84:47-58. [PMID: 31466072 DOI: 10.1159/000501653] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 06/23/2019] [Indexed: 11/19/2022] Open
Abstract
Principal component analysis (PCA) is a widely used method for evaluating low-dimensional data. Some variants of PCA have been proposed to improve the interpretation of the principal components (PCs). One of the most common methods is sparse PCA which aims at finding a sparse basis to improve the interpretability over the dense basis of PCA. However, the performances of these improved methods are still far from satisfactory because the data still contain redundant PCs. In this paper, a novel method called PCA based on graph Laplacian and double sparse constraints (GDSPCA) is proposed to improve the interpretation of the PCs and consider the internal geometry of the data. In detail, GDSPCA utilizes L2,1-norm and L1-norm regularization terms simultaneously to enforce the matrix to be sparse by filtering redundant and irrelative PCs, where the L2,1-norm regularization term can produce row sparsity, while the L1-norm regularization term can enforce element sparsity. This way, we can make a better interpretation of the new PCs in low-dimensional subspace. Meanwhile, the method of GDSPCA integrates graph Laplacian into PCA to explore the geometric structure hidden in the data. A simple and effective optimization solution is provided. Extensive experiments on multi-view biological data demonstrate the feasibility and effectiveness of the proposed approach.
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Affiliation(s)
- Ming-Juan Wu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Ying-Lian Gao
- Library of Qufu Normal University, Qufu Normal University, Rizhao, China,
| | - Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Rong Zhu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Juan Wang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
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Wu J, Cai Q, Wang J, Liao Y. Identifying mutated driver pathways in cancer by integrating multi-omics data. Comput Biol Chem 2019; 80:159-167. [PMID: 30959272 DOI: 10.1016/j.compbiolchem.2019.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 03/23/2019] [Indexed: 10/27/2022]
Abstract
Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.
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Affiliation(s)
- Jingli Wu
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China; College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China.
| | - Qirong Cai
- College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China.
| | - Jinyan Wang
- Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China; College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China.
| | - Yuanxiu Liao
- College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China.
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Zhang W, Wang SL. An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:455-464. [PMID: 29990286 DOI: 10.1109/tcbb.2017.2788016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.
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Dai LY, Zheng CH, Liu JX, Zhu R, Yuan SS, Wang J, Kong XZ. Integrative graph regularized matrix factorization for drug-pathway associations analysis. Comput Biol Chem 2019; 78:474-480. [DOI: 10.1016/j.compbiolchem.2018.11.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/21/2018] [Accepted: 11/30/2018] [Indexed: 02/06/2023]
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Hou MX, Gao YL, Liu JX, Dai LY, Kong XZ, Shang J. Network analysis based on low-rank method for mining information on integrated data of multi-cancers. Comput Biol Chem 2018; 78:468-473. [PMID: 30563751 DOI: 10.1016/j.compbiolchem.2018.11.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 02/01/2023]
Abstract
The noise problem of cancer sequencing data has been a problem that can't be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.
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Affiliation(s)
- Mi-Xiao Hou
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Ying-Lian Gao
- Library of Qufu Normal University, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China; Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, China.
| | - Ling-Yun Dai
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Xiang-Zhen Kong
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Junliang Shang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
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Gao B, Li G, Liu J, Li Y, Huang X. Identification of driver modules in pan-cancer via coordinating coverage and exclusivity. Oncotarget 2018; 8:36115-36126. [PMID: 28415609 PMCID: PMC5482642 DOI: 10.18632/oncotarget.16433] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Accepted: 03/13/2017] [Indexed: 12/30/2022] Open
Abstract
It is widely accepted that cancer is driven by accumulated somatic mutations during the lifetime of an individual. Cancer mutations may target relatively small number of cell functional modules. The heterogeneity in different cancer patients makes it difficult to identify driver mutations or functional modules related to cancer. It is biologically desired to be capable of identifying cancer pathway modules through coordination between coverage and exclusivity. There have been a few approaches developed for this purpose, but they all have limitations in practice due to their computational complexity and prediction accuracy. We present a network based approach, CovEx, to predict the specific patient oriented modules by 1) discovering candidate modules for each considered gene, 2) extracting significant candidates by harmonizing coverage and exclusivity and, 3) further selecting the patient oriented modules based on a set cover model. Applying CovEx to pan-cancer datasets spanning 12 cancer types collecting from public database TCGA, it demonstrates significant superiority over the current leading competitors in performance. It is published under GNU GENERAL PUBLIC LICENSE and the source code is available at:https://sourceforge.net/projects/cancer-pathway/files/
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Affiliation(s)
- Bo Gao
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.,Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.,Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA
| | - Juntao Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Yang Li
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA.,Molecular Biosciences Program, Arkansas State University, Jonesboro, Arkansas, 72401, USA
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Bi-stage hierarchical selection of pathway genes for cancer progression using a swarm based computational approach. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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