Inferring network structure in non‐normal and mixed discrete‐continuous genomic data

Inferring network structure in non‐normal and mixed discrete‐continuous genomic data IntroductionWith rapid advances in high‐throughput genomic technologies using array and sequencing‐based approaches, it is now possible to collect detailed high‐resolution molecular information across the entire genomic landscape at various levels. The data can be genetic (e.g., mutations or single neucleotide polymophisms), genomic (e.g., expression levels of messenger RNA and microRNA), epigenomic (e.g., DNA methylation) or proteomic (e.g., protein expression). The interrelations among these data provide key insights into the etiology of many diseases, including cancer. Statistically, the question of uncovering the major modes of multivariate interactions in genomic data can be phrased in terms of inferring a conditional independence graph. A unifying feature of these genomics problems is that the number of parameters far exceeds the sample size. Therefore, a multivariate sparse Gaussian graphical model is commonly applied to analyze the conditional independence structure (see, e.g., Lauritzen, ; Meinhausen and Bühlmann, ; Carvalho et al., ; Friedman et al., ; Bhadra and Mallick, ; Feldman et al., ). Given this high‐dimensional setting, the purpose of the current article is to study multivariate interactions in two important situations where a Gaussian graphical model is inappropriate. These are (i) when the data are continuous, but display non‐normal features such as heavy http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Inferring network structure in non‐normal and mixed discrete‐continuous genomic data

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12711
Publisher site
See Article on Publisher Site

Abstract

IntroductionWith rapid advances in high‐throughput genomic technologies using array and sequencing‐based approaches, it is now possible to collect detailed high‐resolution molecular information across the entire genomic landscape at various levels. The data can be genetic (e.g., mutations or single neucleotide polymophisms), genomic (e.g., expression levels of messenger RNA and microRNA), epigenomic (e.g., DNA methylation) or proteomic (e.g., protein expression). The interrelations among these data provide key insights into the etiology of many diseases, including cancer. Statistically, the question of uncovering the major modes of multivariate interactions in genomic data can be phrased in terms of inferring a conditional independence graph. A unifying feature of these genomics problems is that the number of parameters far exceeds the sample size. Therefore, a multivariate sparse Gaussian graphical model is commonly applied to analyze the conditional independence structure (see, e.g., Lauritzen, ; Meinhausen and Bühlmann, ; Carvalho et al., ; Friedman et al., ; Bhadra and Mallick, ; Feldman et al., ). Given this high‐dimensional setting, the purpose of the current article is to study multivariate interactions in two important situations where a Gaussian graphical model is inappropriate. These are (i) when the data are continuous, but display non‐normal features such as heavy

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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