LIU Jian, CHENG Yuhu, WANG Xuesong, CUI Xiaoluo. Supervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection[J]. Chinese Journal of Electronics, 2018, 27(4): 845-851. doi: 10.1049/cje.2017.09.023
Citation: LIU Jian, CHENG Yuhu, WANG Xuesong, CUI Xiaoluo. Supervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection[J]. Chinese Journal of Electronics, 2018, 27(4): 845-851. doi: 10.1049/cje.2017.09.023

Supervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection

doi: 10.1049/cje.2017.09.023
Funds:  This work is supported by the National Natural Science Foundation of China (No.61772532, No.61472424).
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  • Corresponding author: CHENG Yuhu (corresponding author) received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. He is currently a professor in the School of Information and Control Engineering, China University of Mining and Technology. His main research interests include machine learning, transfer learning, and intelligent system. In 2010, he was the recipient of the New Century Excellent Talents in University from the Ministry of Education of China. (
  • Received Date: 2017-03-13
  • Rev Recd Date: 2017-04-25
  • Publish Date: 2018-07-10
  • A reliable and precise recognition of the differentially expressed genes of tumor is crucial to treat the cancer effectively. The small number of differentially expressed genes in a huge gene expression dataset determines the important role of sparse methods, such as Penalty matrix decomposition (PMD), among the feature selection methods. The sparse methods always have the drawback:they do not take advantage of known class labels of gene expression data. A novel supervised-sparse method named as Supervised PMD (SPMD) is proposed by adding the class information into PMD via the total scatter matrix. The brief idea of our method used to select the differentially expressed genes is given as follows. The total scatter matrix is obtained according to the gene expression data with class label. The obtained total scatter matrix is decomposed by PMD to acquire the sparse vectors. The non-zero items in sparse vectors are selected as the differentially expressed genes. The Gene ontology (GO) enrichment of functional annotation of the selected genes is detected by ToppFun. Experiments on synthetic data and two real tumor gene expression datasets show that the proposed SPMD is quite promising to select the differentially expressed genes.
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  • C.K. Sarmah and S. Samarasinghe, "Microarray gene expression:a study of between-platform association of Affymetrix and cDNA arrays", Computers in biology and medicine, Vol.41, No.10, pp.980-986, 2011.
    X.S. Wang, Q.F. Liu and Y.H. Cheng, "Protein function prediction based on active semi-supervised learning", Chinese Journal of Electronics, Vol.23, No.4, pp.595-600, 2016.
    Q. Li, H.B. Cheng and M. Yao, "Adaptive multi-phenotype based gene expression programming algorithm", Chinese Journal of Electronics, Vol.25, No.5, pp.807-816, 2016.
    X.S. Wang, Q.F. Liu, Y.H. Cheng and L.J. Li, "Identification of overlapping protein complexes using structural and functional information of PPI network", Chinese Journal of Electronics, Vol.24, No.3, pp.564-568, 2015.
    Y. Gao, Y.H. Cheng and X.S. Wang, "A quick convex hull building algorithm based on grid and binary Tree", Chinese Journal of Electronics, Vol.24, No.2, pp.317-320, 2015.
    X.S. Wang, G. Cao and Y.H. Cheng, "Multi-source local color transfer based on texture similarity", Chinese Journal of Electronics, Vol.23, No.4, pp.718-722, 2014.
    P. Gong, et al., "Benign or malignant classification of lung nodules based on semantic attributes", Acta Electronica Sinica, Vol.43, No.12, pp.2476-2483, 2015. (in Chinese)
    S.L. Zhang, L.L. Zhang, K.M. Qiu, Y. Lu and B.G. Cai, "Variable selection in logistic regression model", Chinese Journal of Electronics, Vol.24, No.4, pp.813-817, 2015.
    D.M. Witten, R. Tibshirani and T. Hastie, "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis", Biostatistics, Vol.10, No.3, pp.515-534, 2009.
    J.X. Liu, C.H. Zheng and Y. Xu, "Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition", Computers in biology and medicine, Vol.42, No.5, pp.582-589, 2012.
    C.H. Zheng, T.Y. Ng, D. Zhang, C.K. Shiu and H.Q. Wang, "Tumor classification based on non-negative matrix factorization using gene expression data", NanoBioscience, IEEE Transactions on, Vol.10, No.2, pp.86-93, 2011.
    J.X. Liu, Y.T. Wang, et al., "Robust PCA based method for discovering differentially expressed genes", BMC bioinformatics, Vol.14, No.Suppl 8, pp.S3, 2013.
    I. Guyon, J. Weston, S. Barnhill and V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine learning, Vol.46, No.1-3, pp.389-422, 2002.
    H. Wang, S. Yan, D. Xu, X. Tang and T. Huang, "Trace ratio vs. ratio trace for dimensionality reduction", IEEE Conference on Computer Vision and Pattern Recognition, Minneapdis, MN, USA, pp.1-8, 2007.
    H. Shen and J.Z. Huang, "Sparse principal component analysis via regularized low rank matrix approximation", Journal of multivariate analysis, Vol.99, No.6, pp.1015-1034, 2008.
    J.P. Brunet, P. Tamayo, T.R. Golub and J.P. Mesirov, "Metagenes and molecular pattern discovery using matrix factorization", Proceedings of the national academy of sciences, Vol.101, No.12, pp.4164-4169, 2004.
    J. Chen, et al., "ToppGene Suite for gene list enrichment analysis and candidate gene prioritization", Nucleic acids research, Vol.37, No.Web Server isscle, pp.W305-W311, 2009.
    A.M. Dahlin, M.V. Hollegaard, C. Wibom, U. Andersson, D.M. Hougaard, et al., "CCND2, CTNNB1, DDX3X, GLI2, SMARCA4, MYC, MYCN, PTCH1, TP53, and MLL2 gene variants and risk of childhood medulloblastoma", Journal of Neuro-Oncology, Vol.125, No.1, pp.75-78, 2015.
    M.J. Korenberg, "Gene expression monitoring accurately predicts medulloblastoma positive and negative clinical outcomes", Febs Letters, Vol.533, No.1-3, pp.110, 2003.
    E.A. Raetz, M.K. Kim, P. Moos, M. Carlson, C. Bruggers, D.K. Hooper, L. Foot, T. Liu, R. Seeger and W.L. Carroll, "Identification of genes that are regulated transcriptionally by Myc in childhood tumors", Cancer, Vol. 98, No.4, pp.841C-853, 2003.
    Y. Liu, X. Zhu, J. Zhu, S. Liao, Q. Tang, K. Liu, X. Guan, J. Zhang and Z. Feng, "Identification of differential expression of genes in hepatocellular carcinoma by suppression subtractive hybridization combined cDNA microarray", Oncology Reports, Vol.18, No.4, pp.943-51, 2007.
    P.A. Galante, D. Sandhu, R.D.S. Abreu, M. Gradassi, N. Slager, C. Vogel, S.J. Souza and L.O. Penalva, "A comprehensive in silico expression analysis of RNA binding proteins in normal and tumor tissue:Identification of potential players in tumor formation", Rna Biology, Vol.6, No.4, pp.426, 2009.
    T.J. Macdonald, I.F. Pollack, H. Okada, S. Bhattacharya and J. Lyons-Weiler, "Progression-associated genes in astrocytoma identified by novel microarray gene expression data reanalysis", Methods in Molecular Biology, Vol.377, pp.203-211, 2007.
    E.D. Hsi, S. Jung, R. Lai, J.L. Johnson, J.R. Cook, J. Dan, S. Devos, B.D. Cheson, et al., "Ki67 and PIM1 expression predict outcome in mantle cell lymphoma treated with high dose therapy, stem cell transplantation and rituximab:a Cancer and Leukemia Group B59909 correlative science study", Leukemia and Lymphoma, Vol.49, No.11, pp.2081, 2009.
    K. Cha, Y. Li and G.S. Yi, "Discovering gene expression signatures responding to tyrosine kinase inhibitor treatment in chronic myeloid leukemia", BMC Medical Genomics, Vol.9, No.1, pp.29, 2016.
    J. Wang, C.C. Fong, C.H. Tzang, P. Xiao, R. Han and M. Yang, "Gene expression analysis of human promyelocytic leukemia HL-60 cell differentiation and cytotoxicity induced by natural and synthetic retinoids", Life Sciences, Vol.84, No.17-18, pp.576-583, 2009.
    H. Wang, H. Hu, Q. Zhang, Y. Yang, Y. Li, Y. Hu, X. Ruan, Y. Yang, et al., "Dynamic transcriptomes of human myeloid leukemia cells", Genomics, Vol.102, No.4, pp.250-256, 2013.
    L. Qin, B.D. Smith, H.L. Tsai, N.K. Yaghi, P.H. Neela, M. Moake, J. Fu, Y.L. Kasamon, G.T. Prince and M. Goswami, "Induction of high-titer IgG antibodies against multiple leukemia-associated antigens in CML patients with clinical responses to K562/GVAX immunotherapy", Blood Cancer Journal, Vol.3, No.9, pp.e145, 2013.
    C.U. Niemann, L. Kjeldsen, E. Ralfkiaer, M.K. Jensen and N. Borregaard, "Serglycin proteoglycan in hematologic malignancies:a marker of acute myeloid leukemia", Leukemia, Vol.21, No.12, pp.2406-2410, 2007.
    T. Muzzafar, L.J. Medeiros and S.A. Wang, "Aberrant underexpression of CD81 in precursor B-cell acute lymphoblastic leukemia:utility in detection of minimal residual disease by flow cytometry", American Journal of Clinical Pathology, Vol.132, No.5, pp.692-698, 2009.
    S. Civini, P. Jin, J. Ren, M. Sabatino, L. Castiello, J. Jin, H. Wang, Y. Zhao, F. Marincola and D. Stroncek, "Leukemia cells induce changes in human bone marrow stromal cells", Journal of Translational Medicine, Vol.11, No.1, pp.298, 2013.
    L.T. Dong and G.R. Ball, "Exploration of leukemia gene regulatory networks using a systems biology approach", IEEE International Conference on Bioinformatics and Biomedicine, Belfast, UK, pp.68-73, 2014.
    F. Talab, J.C. Allen, V. Thompson, K. Lin and J.R. Slupsky, "LCK is an important mediator of B-cell receptor signaling in chronic lymphocytic leukemia cells", Molecular Cancer Research, Vol.11, No.5, pp.541, 2013.
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