Paper Info
Title
Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression.
Abstract
Motivation: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable. Results: We develop a new technique for using the marginal relationship between gene expression measurements and patient survival outcomes to identify a small subset of genes which appear highly relevant for predicting survival, produce a low-dimensional embedding based on this small subset, and amplify this embedding with information from the remaining genes. We motivate our methodology by using gene expression measurements to predict survival time for patients with diffuse large B-cell lymphoma, illustrate the behavior of our methodology on carefully constructed synthetic examples, and test it on a number of other gene expression datasets. Our technique is computationally tractable, generally outperforms other methods, is extensible to other phenotypes, and also identifies different genes (relative to existing methods) for possible future study.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx265
BIOINFORMATICS
Field
DocType
Volume
Data mining,Gene,Embedding,Interaction structure,Regression,Phenotype,Computer science,Gene expression,Computational biology,DNA microarray,Eigenvalues and eigenvectors
Journal
33
Issue
ISSN
Citations 
14
1367-4803
1
PageRank 
References 
Authors
0.48
0
2
Name
Order
Citations
PageRank
Lei Ding114226.77
Daniel J. McDonald293.48