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

Loading next page...
 
/lp/wiley/inferring-network-structure-in-non-normal-and-mixed-discrete-FDJhpEprzd
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

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off