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

Author

Listed:
  • Kasim Adetayo

    (Universiteit Hasselt & Katholieke Universiteit Leuven)

  • Lin Dan

    (Universiteit Hasselt & Katholieke Universiteit Leuven)

  • Van Sanden Suzy

    (Universiteit Hasselt & Katholieke Universiteit Leuven)

  • Clevert Djork-Arné

    (Johannes Kepler University Linz & Charité - Universitätsmedizin Berlin)

  • Bijnens Luc

    (Janssen Pharmaceutica N. V., Beerse)

  • Göhlmann Hinrich

    (Janssen Pharmaceutica N. V., Beerse)

  • Amaratunga Dhammika

    (Johnson & Johnson Pharmaceutical Research & Development, Raritan)

  • Hochreiter Sepp

    (Johannes Kepler University Linz)

  • Shkedy Ziv

    (Universiteit Hasselt & Katholieke Universiteit Leuven)

  • Talloen Willem

    (Janssen Pharmaceutica N. V., Beerse)

Abstract

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.

Suggested Citation

  • Kasim Adetayo & Lin Dan & Van Sanden Suzy & Clevert Djork-Arné & Bijnens Luc & Göhlmann Hinrich & Amaratunga Dhammika & Hochreiter Sepp & Shkedy Ziv & Talloen Willem, 2010. "Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-31, January.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:4
    DOI: 10.2202/1544-6115.1460
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    References listed on IDEAS

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    1. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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    1. Perualila-Tan Nolen & Kasim Adetayo & Talloen Willem & Verbist Bie & Göhlmann Hinrich W.H. & QSTAR Consortium & Shkedy Ziv, 2016. "A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 291-304, August.

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