Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

See all formats and pricing
More options …
Volume 9, Issue 1


Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach

Adetayo Kasim / Dan Lin / Suzy Van Sanden / Djork-Arné Clevert / Luc Bijnens / Hinrich Göhlmann / Dhammika Amaratunga / Sepp Hochreiter / Ziv Shkedy / Willem Talloen
Published Online: 2010-01-06 | DOI: https://doi.org/10.2202/1544-6115.1460

The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ).Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.

Keywords: gene filtering; factor analysis; linear mixed model

About the article

Published Online: 2010-01-06

Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1460.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Martin Otava, Ziv Shkedy, Willem Talloen, Geert R Verheyen, and Adetayo Kasim
BMC Genomics, 2015, Volume 16, Number 1
Marijke Van Moerbeke, Adetayo Kasim, and Ziv Shkedy
Scientific Reports, 2018, Volume 8, Number 1
Marijke Van Moerbeke, Adetayo Kasim, Willem Talloen, Joke Reumers, Hinrick W. H. Göhlmann, and Ziv Shkedy
BMC Bioinformatics, 2017, Volume 18, Number 1
Tatsiana Khamiakova, Ziv Shkedy, Dhammika Amaratunga, Willem Talloen, Hinrich Göhlmann, Luc Bijnens, and Adetayo Kasim
Mathematical Biosciences, 2014, Volume 248, Page 1
Nolen Joy Perualila-Tan, Ziv Shkedy, Willem Talloen, Hinrich W. H. Göhlmann, Marijke Van Moerbeke, and Adetayo Kasim
Journal of Bioinformatics and Computational Biology, 2016, Volume 14, Number 04, Page 1650018
Raffaele Fronza, Michele Tramonti, William R Atchley, and Christine Nardini
BMC Bioinformatics, 2011, Volume 12, Number 1, Page 86

Comments (0)

Please log in or register to comment.
Log in