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A method for visual identification of small sample subgroups and potential biomarkers

Soneson, Charlotte LU and Fontes, Magnus LU (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:
author
organization
publishing date
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
2011-10-19 18:31:56
date last changed
2017-01-01 03:15:58
@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},
  volume       = {5},
  year         = {2011},
}