Advanced

Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings

Soneson, Charlotte LU and Fontes, Magnus LU (2014) In Computational Statistics & Data Analysis 71. p.588-598
Abstract
Gene set-based analysis methods have recently gained increasing popularity for analysis of microarray data. Several studies have indicated that the results from such methods are more reproducible and more easily interpretable than the results from single gene-based methods. A new method for ranking gene sets with respect to their association with a given predictor or response, using a new framework for robust gene list representation, is proposed. Employing the concept of exchangeability of random variables, this method attempts to account for the functional redundancy among the genes. Compared to other evaluated methods for gene set ranking, the proposed method yields rankings that are more robust with respect to sampling variations in... (More)
Gene set-based analysis methods have recently gained increasing popularity for analysis of microarray data. Several studies have indicated that the results from such methods are more reproducible and more easily interpretable than the results from single gene-based methods. A new method for ranking gene sets with respect to their association with a given predictor or response, using a new framework for robust gene list representation, is proposed. Employing the concept of exchangeability of random variables, this method attempts to account for the functional redundancy among the genes. Compared to other evaluated methods for gene set ranking, the proposed method yields rankings that are more robust with respect to sampling variations in the underlying data, which allows more reliable biological conclusions. (C) 2012 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bioinformatics, Exchangeability, Gene set ranking, Microarray
in
Computational Statistics & Data Analysis
volume
71
pages
588 - 598
publisher
Elsevier
external identifiers
  • wos:000328869000043
  • scopus:84889089828
ISSN
0167-9473
DOI
10.1016/j.csda.2012.07.026
language
English
LU publication?
yes
id
a5b43b59-96c3-416c-b771-b2777d4f45c4 (old id 4261833)
date added to LUP
2014-02-10 14:57:50
date last changed
2017-01-01 03:19:52
@article{a5b43b59-96c3-416c-b771-b2777d4f45c4,
  abstract     = {Gene set-based analysis methods have recently gained increasing popularity for analysis of microarray data. Several studies have indicated that the results from such methods are more reproducible and more easily interpretable than the results from single gene-based methods. A new method for ranking gene sets with respect to their association with a given predictor or response, using a new framework for robust gene list representation, is proposed. Employing the concept of exchangeability of random variables, this method attempts to account for the functional redundancy among the genes. Compared to other evaluated methods for gene set ranking, the proposed method yields rankings that are more robust with respect to sampling variations in the underlying data, which allows more reliable biological conclusions. (C) 2012 Elsevier B.V. All rights reserved.},
  author       = {Soneson, Charlotte and Fontes, Magnus},
  issn         = {0167-9473},
  keyword      = {Bioinformatics,Exchangeability,Gene set ranking,Microarray},
  language     = {eng},
  pages        = {588--598},
  publisher    = {Elsevier},
  series       = {Computational Statistics & Data Analysis},
  title        = {Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings},
  url          = {http://dx.doi.org/10.1016/j.csda.2012.07.026},
  volume       = {71},
  year         = {2014},
}