A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints

Xi, Jianing; Li, Ao; Wang, Minghui

doi:10.1016/j.neucom.2018.03.026

Cited by 27 publications

(13 citation statements)

References 58 publications

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“…In particular, two significant GO biological processes, antigen processing and presentation and interferon-gamma-mediated signaling pathway, are both essential for immune response, which is often observed to be inhibited in the tumor microenvironment [ 7 , 32 ]. In addition, we test the relationship between the functional modules and cancer driver genes [ 33 , 34 ]. By following a previous work [ 35 ], we utilized 2,372 genes from the Network of Cancer Genes (NCG) [ 36 ] as benchmarking cancer genes, including 711 known cancer genes from the Cancer Gene Census (CGC) [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

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BackgroundComprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge.ResultsHere, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc.ConclusionsConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

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“…A typical strategy to learn the low dimension representation from the data matrix D is to apply the optimization procedures of alternatively updating rules in nonnegative matrix factorization (NMF) [28]. When the iteration reaches convergence, the multiplication of the two learnt matrices W and H can effectively approximate the original data matrix D. Here matrix W is the coefficient matrix of the linear transformation from low-dimension representation H to the original data D. e rows of matrix H are the low-dimension representations learnt from mutation data, preserving the cancer mutation information beyond interaction network.…”

Section: Dimension Reduction For Small Samplesmentioning

confidence: 99%

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

Xie

Duan

2020

Complexity

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The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.

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“…Several large-scale studies have reported CNV participates in phenotypic variation and adaptation by disrupting genes and altering gene dosage [1], [2]. Some CNVs are remained by normal individuals while others are implicated in many diseases including Parkinson [3], diabetes mellitus [4], Autism [5], Alzheimer [6], and cancer [7]. Thus, comprehensive identification and cataloging of CNVs from…”

Section: Introductionmentioning

confidence: 99%

dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Zhang

Yuan

et al. 2020

IEEE Access

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Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes. Due to the large-scale sequencing and cost control whole-genome sequencing (WGS) data, low-coverage data is favorably disposed towards CNV identification. However, such low-coverage data is sensitive to noise and sequencing biases, which results in low resolution of CNV detection in past experimental designs for WGS datasets. In this paper, we present a control-free Dirichlet process Gaussian mixture model (dpGMM) based approach, to analyze the read depth (RD) of low-coverage WGS datasets for CNV discovery. First, noise and biases of the RD signals are corrected through the preprocessing step of dpGMM. Then we assume that RD signals across genomic regions follow a Gaussian mixture model (GMM) in which each Gaussian distribution is followed by a copy number state. Without requiring the number of Gaussian distributions, dpGMM builds a Dirichlet process (DP) GMM for RD signals and further uses a DP prior to infer the number of Gaussian models. After that, we apply dpGMM to simulation datasets with different coverages and individual datasets, and compare ours to three widely used RD-based pipelines, CNVnator, GROM-RD, and BIC-seq2. Simulation results demonstrate that our approach, dpGMM, has a high F1 score in both low-and highcoverage sequences. Also, the number of overlaps between CNVs detected in real data by ours and the standard benchmark is twice as much as that detected by other tools such as CNVnator and GROM-RD. INDEX TERMS Copy number variation, Dirichlet process, Gaussian mixture model, read depth, low coverage.

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A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints

Cited by 27 publications

References 58 publications

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

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