Open AccessSupervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection
Author(s) -
Liu Jian,
Cheng Yuhu,
Wang Xuesong,
Cui Xiaoluo
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.09.023
Subject(s) - computer science , class (philosophy) , gene , pattern recognition (psychology) , selection (genetic algorithm) , matrix (chemical analysis) , artificial intelligence , feature selection , matrix decomposition , computational biology , biology , genetics , physics , eigenvalues and eigenvectors , quantum mechanics , materials science , composite material
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.