A Mixture Modeling Framework for Differential Analysis of High-Throughput Data
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The inventions of microarray and next generation sequencing technologies have revolutionized research in genomics; platforms have led to massive amount of data in gene expression, methylation, and protein-DNA interactions.
A common theme among a number of biological problems using high-throughput technologies is differential analysis.
Despite the common theme, different data types have their own unique features, creating a “moving target” scenario.
As such, methods specifically designed for one data type may not lead to satisfactory results when applied to another data type.
To meet this challenge so that not only currently existing data types but also data from future problems, platforms, or experiments can be analyzed, we propose a mixture modeling framework that is flexible enough to automatically adapt to any moving target.
More specifically, the approach considers several classes of mixture models and essentially provides a model-based procedure whose model is adaptive to the particular data being analyzed.
We demonstrate the utility of the methodology by applying it to three types of real data: gene expression, methylation, and ChIP-seq.
We also carried out simulations to gauge the performance and showed that the approach can be more efficient than any individual model without inflating type I error.
American Psychological Association (APA)
Taslim, Cenny& Lin, Shili. 2014. A Mixture Modeling Framework for Differential Analysis of High-Throughput Data. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-496456
Modern Language Association (MLA)
Taslim, Cenny& Lin, Shili. A Mixture Modeling Framework for Differential Analysis of High-Throughput Data. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-496456
American Medical Association (AMA)
Taslim, Cenny& Lin, Shili. A Mixture Modeling Framework for Differential Analysis of High-Throughput Data. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-496456
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496456