Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings
(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:
https://lup.lub.lu.se/record/4261833
- author
- Soneson, Charlotte LU and Fontes, Magnus LU
- organization
- publishing date
- 2014
- 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
- 2016-04-01 10:09:28
- date last changed
- 2022-01-25 20:19:50
@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}},
keywords = {{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}},
doi = {{10.1016/j.csda.2012.07.026}},
volume = {{71}},
year = {{2014}},
}