Bayesian Blind Source Separation for Data with Network Structure

Illner K, Fuchs C, Theis FJ (2014)
Journal of Computational Biology 21(11): 855-865.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
In biology, more and more information about the interactions in regulatory systems becomes accessible, and this often leads to prior knowledge for recent data interpretations. In this work we focus on multivariate signaling data, where the structure of the data is induced by a known regulatory network. To extract signals of interest we assume a blind source separation (BSS) model, and we capture the structure of the source signals in terms of a Bayesian network. To keep the parameter space small, we consider stationary signals, and we introduce the new algorithm emGrade, where model parameters and source signals are estimated using expectation maximization. For network data, we find an improved estimation performance compared to other BSS algorithms, and the flexible Bayesian modeling enables us to deal with repeated and missing observation values. The main advantage of our method is the statistically interpretable likelihood, and we can use model selection criteria to determine the (in general unknown) number of source signals or decide between different given networks. In simulations we demonstrate the recovery of the source signals dependent on the graph structure and the dimensionality of the data.
Erscheinungsjahr
Zeitschriftentitel
Journal of Computational Biology
Band
21
Ausgabe
11
Seite(n)
855-865
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Illner K, Fuchs C, Theis FJ. Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology. 2014;21(11):855-865.
Illner, K., Fuchs, C., & Theis, F. J. (2014). Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology, 21(11), 855-865. doi:10.1089/cmb.2014.0117
Illner, K., Fuchs, C., and Theis, F. J. (2014). Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology 21, 855-865.
Illner, K., Fuchs, C., & Theis, F.J., 2014. Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology, 21(11), p 855-865.
K. Illner, C. Fuchs, and F.J. Theis, “Bayesian Blind Source Separation for Data with Network Structure”, Journal of Computational Biology, vol. 21, 2014, pp. 855-865.
Illner, K., Fuchs, C., Theis, F.J.: Bayesian Blind Source Separation for Data with Network Structure. Journal of Computational Biology. 21, 855-865 (2014).
Illner, Katrin, Fuchs, Christiane, and Theis, Fabian J. “Bayesian Blind Source Separation for Data with Network Structure”. Journal of Computational Biology 21.11 (2014): 855-865.

1 Zitation in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

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14 References

Daten bereitgestellt von Europe PubMed Central.

Analysis of stopping criteria for the EM algorithm in the context of patient grouping according to length of stay
Vasilakis C.., 2008
A new look at the statistical model identification
Akaike H.., 1974
A blind source separation technique using second-order statistics
Cardoso J.-F.., 1997
Using Bayesian networks to analyze expression data.
Friedman N, Linial M, Nachman I, Pe'er D., J. Comput. Biol. 7(3-4), 2000
PMID: 11108481
Indepedent component analysis: algorithms and applications
Oja E.., 2000
Blind source separation using latent gaussian graphical models
Theis F.J.., 2012
Bayesian blind source separation applied to the lymphocyte pathway
Theis F.J.., 2014
Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation.
Kowarsch A, Blochl F, Bohl S, Saile M, Gretz N, Klingmuller U, Theis FJ., BMC Bioinformatics 11(), 2010
PMID: 21118515

Lauritzen S.L.., 1996

Krishnan T.., 2007
The Bayes net toolbox for Matlab
Murphy K.., 2001
Estimating the dimension of a model
Schwarz G.., 1978
AMUSE: A new blind identification algorithm
Huang Y.F.., 1990

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