Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function
As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important researc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01353/full |
| _version_ | 1818331414038315008 |
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| author | Conghai Lu Juan Wang Jinxing Liu Chunhou Zheng Xiangzhen Kong Xiaofeng Zhang |
| author_facet | Conghai Lu Juan Wang Jinxing Liu Chunhou Zheng Xiangzhen Kong Xiaofeng Zhang |
| author_sort | Conghai Lu |
| collection | DOAJ |
| description | As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important research area in the bioinformatics field. In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S). First, a lowest rank matrix is obtained after NSLRG decomposition. The lowest rank matrix preserves the local data manifold information and the global data structure information of the gene expression data. Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature. Third, we rank the features according to their scores and select the feature genes for cancer sample clustering. Finally, based on selected feature genes, we use the K-means method to cluster the cancer samples. The experiments are conducted on The Cancer Genome Atlas (TCGA) data. Comparative experiments demonstrate that the NSLRG-S framework can significantly improve the clustering performance. |
| first_indexed | 2024-12-13T13:19:28Z |
| format | Article |
| id | doaj.art-66bf45d69b154dcc81f7b1848f4e3b7b |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T13:19:28Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-66bf45d69b154dcc81f7b1848f4e3b7b2022-12-21T23:44:27ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-01-011010.3389/fgene.2019.01353496650Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score FunctionConghai Lu0Juan Wang1Jinxing Liu2Chunhou Zheng3Xiangzhen Kong4Xiaofeng Zhang5School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaCollege of Electrical Engineering and Automation, Anhui University, Hefei, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSchool of Information and Electrical Engineering, Ludong University, Yantai, ChinaAs an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important research area in the bioinformatics field. In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S). First, a lowest rank matrix is obtained after NSLRG decomposition. The lowest rank matrix preserves the local data manifold information and the global data structure information of the gene expression data. Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature. Third, we rank the features according to their scores and select the feature genes for cancer sample clustering. Finally, based on selected feature genes, we use the K-means method to cluster the cancer samples. The experiments are conducted on The Cancer Genome Atlas (TCGA) data. Comparative experiments demonstrate that the NSLRG-S framework can significantly improve the clustering performance.https://www.frontiersin.org/article/10.3389/fgene.2019.01353/fullcancer gene expression datalow-rank representationfeature selectionscore functionclustering |
| spellingShingle | Conghai Lu Juan Wang Jinxing Liu Chunhou Zheng Xiangzhen Kong Xiaofeng Zhang Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function Frontiers in Genetics cancer gene expression data low-rank representation feature selection score function clustering |
| title | Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function |
| title_full | Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function |
| title_fullStr | Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function |
| title_full_unstemmed | Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function |
| title_short | Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function |
| title_sort | non negative symmetric low rank representation graph regularized method for cancer clustering based on score function |
| topic | cancer gene expression data low-rank representation feature selection score function clustering |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.01353/full |
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