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dc.contributor.author
Dahinden, Corinne
dc.contributor.author
Parmigiani, Giovanni
dc.contributor.author
Emerick, Mark C.
dc.contributor.author
Bühlmann, Peter
dc.date.accessioned
2018-09-04T12:38:46Z
dc.date.available
2017-06-08T16:29:43Z
dc.date.available
2018-09-04T12:38:46Z
dc.date.issued
2007-12
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/1471-2105-8-476
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/4160
dc.identifier.doi
10.3929/ethz-b-000004160
dc.description.abstract
Background The joint analysis of several categorical variables is a common task in many areas of biology, and is becoming central to systems biology investigations whose goal is to identify potentially complex interaction among variables belonging to a network. Interactions of arbitrary complexity are traditionally modeled in statistics by log-linear models. It is challenging to extend these to the high dimensional and potentially sparse data arising in computational biology. An important example, which provides the motivation for this article, is the analysis of so-called full-length cDNA libraries of alternatively spliced genes, where we investigate relationships among the presence of various exons in transcript species. Results We develop methods to perform model selection and parameter estimation in log-linear models for the analysis of sparse contingency tables, to study the interaction of two or more factors. Maximum Likelihood estimation of log-linear model coefficients might not be appropriate because of the presence of zeros in the table's cells, and new methods are required. We propose a computationally efficient ℓ1-penalization approach extending the Lasso algorithm to this context, and compare it to other procedures in a simulation study. We then illustrate these algorithms on contingency tables arising from full-length cDNA libraries. Conclusion We propose regularization methods that can be used successfully to detect complex interaction patterns among categorical variables in a broad range of biological problems involving categorical variables.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Lasso
en_US
dc.subject
Interaction Pattern
en_US
dc.subject
Order Interaction
en_US
dc.subject
Interaction Vector
en_US
dc.subject
MCMC Method
en_US
dc.title
Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
8
en_US
ethz.journal.abbreviated
BMC bioinformatics
ethz.pages.start
476
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
ethz.date.deposited
2017-06-08T16:29:59Z
ethz.source
ECIT
ethz.identifier.importid
imp59364b74339a117417
ethz.ecitpid
pub:14270
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T07:35:56Z
ethz.rosetta.lastUpdated
2018-11-08T01:32:26Z
ethz.rosetta.versionExported
true
ethz.COinS
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