A method for visual identification of small sample subgroups and potential biomarkers
(2011) In Annals of Applied Statistics 5(3). p.2131-2149- Abstract
- In order to find previously unknown subgroups in biomedical data and
generate testable hypotheses, visually guided exploratory analysis can be of
tremendous importance. In this paper we propose a new dissimilarity measure
that can be used within the Multidimensional Scaling framework to obtain a
joint low-dimensional representation of both the samples and variables of a
multivariate data set, thereby providing an alternative to conventional biplots.
In comparison with biplots, the representations obtained by our approach are
particularly useful for exploratory analysis of data sets where there are small
groups of variables sharing unusually high or low values... (More) - In order to find previously unknown subgroups in biomedical data and
generate testable hypotheses, visually guided exploratory analysis can be of
tremendous importance. In this paper we propose a new dissimilarity measure
that can be used within the Multidimensional Scaling framework to obtain a
joint low-dimensional representation of both the samples and variables of a
multivariate data set, thereby providing an alternative to conventional biplots.
In comparison with biplots, the representations obtained by our approach are
particularly useful for exploratory analysis of data sets where there are small
groups of variables sharing unusually high or low values for a small group of
samples. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2173907
- author
- Soneson, Charlotte LU and Fontes, Magnus LU
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Annals of Applied Statistics
- volume
- 5
- issue
- 3
- pages
- 2131 - 2149
- publisher
- Institute of Mathematical Statistics
- external identifiers
-
- wos:000300382500017
- scopus:84866050365
- ISSN
- 1932-6157
- DOI
- 10.1214/11-AOAS460
- language
- English
- LU publication?
- yes
- id
- ada699ce-aa54-40b5-b6d8-94a453c080b7 (old id 2173907)
- date added to LUP
- 2016-04-01 10:05:26
- date last changed
- 2022-01-25 19:34:31
@article{ada699ce-aa54-40b5-b6d8-94a453c080b7,
abstract = {{In order to find previously unknown subgroups in biomedical data and <br/><br>
generate testable hypotheses, visually guided exploratory analysis can be of <br/><br>
tremendous importance. In this paper we propose a new dissimilarity measure <br/><br>
that can be used within the Multidimensional Scaling framework to obtain a <br/><br>
joint low-dimensional representation of both the samples and variables of a <br/><br>
multivariate data set, thereby providing an alternative to conventional biplots. <br/><br>
In comparison with biplots, the representations obtained by our approach are <br/><br>
particularly useful for exploratory analysis of data sets where there are small <br/><br>
groups of variables sharing unusually high or low values for a small group of <br/><br>
samples.}},
author = {{Soneson, Charlotte and Fontes, Magnus}},
issn = {{1932-6157}},
language = {{eng}},
number = {{3}},
pages = {{2131--2149}},
publisher = {{Institute of Mathematical Statistics}},
series = {{Annals of Applied Statistics}},
title = {{A method for visual identification of small sample subgroups and potential biomarkers}},
url = {{http://dx.doi.org/10.1214/11-AOAS460}},
doi = {{10.1214/11-AOAS460}},
volume = {{5}},
year = {{2011}},
}