Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses

How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.

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“…Liu et al . used LLR to select characteristic gene using gene expression data [12]. In [13], Witten et al .…”

Section: Introductionmentioning

confidence: 99%

Robust PCA based method for discovering differentially expressed genes

et al. 2013

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“…Liu et al . used the first PC of SPCA for characteristic genes selection [26] . These methods indeed assumed that the first component of PLS or SPCA plays a dominant role in gene selection.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Gene Selection via Weighting Principal Components by Singular Values

Liu

Zheng

et al. 2012

PLoS ONE

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Conventional gene selection methods based on principal component analysis (PCA) use only the first principal component (PC) of PCA or sparse PCA to select characteristic genes. These methods indeed assume that the first PC plays a dominant role in gene selection. However, in a number of cases this assumption is not satisfied, so the conventional PCA-based methods usually provide poor selection results. In order to improve the performance of the PCA-based gene selection method, we put forward the gene selection method via weighting PCs by singular values (WPCS). Because different PCs have different importance, the singular values are exploited as the weights to represent the influence on gene selection of different PCs. The ROC curves and AUC statistics on artificial data show that our method outperforms the state-of-the-art methods. Moreover, experimental results on real gene expression data sets show that our method can extract more characteristic genes in response to abiotic stresses than conventional gene selection methods.

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Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes

Gao

Wang

et al. 2015

Intelligent Computing Theories and Methodologies

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Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses

Cited by 5 publications

References 11 publications

Robust PCA based method for discovering differentially expressed genes

Robust PCA based method for discovering differentially expressed genes

Characteristic Gene Selection via Weighting Principal Components by Singular Values

Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes

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