Identification of Biomarkers for Predicting the Overall Survival of Ovarian Cancer Patients: a Sparse Group Lasso Approach

Mai, Kristi; Zhang, Qingyang

doi:10.5539/ijsp.v5n6p57

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…To model the correlation between genes, we use Gaussian copula as it is convenient for multivariate problem. Another possible future work is to compare different latent variables and copula functions, e.g., Student's t copula (Nelson [1999]) and Gaussian mixture copula (Zhang & Shi [2016]), in a model comparison framework (Mai & Zhang [2016], Zhang et al [2014],…”

Section: Discussionmentioning

confidence: 99%

Classification of RNA-Seq Data via Gaussian Copulas

Zhang

2017

Preprint

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RNA-sequencing (RNA-Seq) has become a preferred option to quantify gene expression, because it is more accurate and reliable than microarrays. In RNA-Seq experiments, the expression level of a gene is measured by the count of short reads that are mapped to the gene region. Although some normalbased statistical methods may also be applied to log-transformed read counts, they are not ideal for directly modeling RNA-Seq data. Two discrete distributions, Poisson distribution and negative binomial distribution, have been commonly used in the literature to model RNA-Seq data, where the latter is a natural extension of the former with allowance of overdispersion. Due to the technical difficulty in modeling correlated counts, most existing classifiers based on discrete distributions assume that genes are independent of each other. However, as we show in this paper, the independence assumption may cause non-ignorable bias in estimating the discriminant score, making the classification inaccurate. To this end, we drop the independence assumption and explicitly model the dependence between genes using Gaussian copula. We apply a Bayesian approach to estimate covariance matrix and the overdispersion parameter in negative binomial distribution. Both synthetic data and real data are used to demonstrate the advantages of our model.

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Section: Discussionmentioning

confidence: 99%

Classification of RNA-Seq Data via Gaussian Copulas

Zhang

2017

Preprint

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“…We incorporated three data types into our model including gene expression level, DNA methylation level (in gene promoter region), and CNV. The data were normalized using a quantile normalization method by Bolstad et al 11 and Mai and Zhang 12 to correct the bias due to non-biological causes. In addition, we applied an effective method by Hsu et al 13 to remove age and batch effects (three age groups are defined as < 40, [40,70], and > 70 year old).…”

Section: Application To Tcga Ovarian Cancer Datamentioning

confidence: 99%

A mixture copula Bayesian network model for multimodal genomic data

Zhang¹,

Shi

2017

Cancer Inform

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Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation–maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

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“…We incorporated three data types into our model including gene expression level, DNA methylation level (on gene promoter region) and CNV. The data were normalized using a quantile normalization method by Balstad et al [11,12] to correct the bias due to non-biological causes. In addition, we applied an effective method by Hsu et al [13] to remove age and batch effects (three age groups are defined as < 40 y.o., [40,70] y.o., and > 70 y.o.).…”

Section: Application To Tcga Ovarian Cancer Datamentioning

confidence: 99%

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

Zhang

Shi

2017

Preprint

Self Cite

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Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately or severely violated, making it unsuitable to deal with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine Expectation-Maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling dataset. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

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Identification of Biomarkers for Predicting the Overall Survival of Ovarian Cancer Patients: a Sparse Group Lasso Approach

Cited by 4 publications

References 19 publications

Classification of RNA-Seq Data via Gaussian Copulas

Classification of RNA-Seq Data via Gaussian Copulas

A mixture copula Bayesian network model for multimodal genomic data

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

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