Low-Rank and Sparse Matrix Decomposition for GeneticInteraction Data
Joint Authors
Wang, Yishu
Yang, Dejie
Deng, Minghua
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Background.
Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms.
One approach to analyze EMAP data is to identify gene modules with densely interacting genes.
In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative.
Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology.
However, the high missing rate in EMAP data hinders the development of such approaches.
To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part.
Results.
LRSDec has been demonstrated as an effective technique for analyzing EMAP data.
We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae.
Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.
American Psychological Association (APA)
Wang, Yishu& Yang, Dejie& Deng, Minghua. 2015. Low-Rank and Sparse Matrix Decomposition for GeneticInteraction Data. BioMed Research International،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1055965
Modern Language Association (MLA)
Wang, Yishu…[et al.]. Low-Rank and Sparse Matrix Decomposition for GeneticInteraction Data. BioMed Research International No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1055965
American Medical Association (AMA)
Wang, Yishu& Yang, Dejie& Deng, Minghua. Low-Rank and Sparse Matrix Decomposition for GeneticInteraction Data. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1055965
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055965