Statistical Modelling 11 (2011), 89–114
Exploiting blank spots for model-based background correction in discovering
genes with DNA array data
Serena Arima
Dipartimento di metodi e modelli per l’economia, il territorio e la
finanza,
Sapienza Università di Roma
via del Castno Laurenziano 9
I–00161 Roma
Italy
eMail: serena.arima@uniroma1.it
Brunero Liseo
Dipartimento di metodi e modelli per l’economia, il territorio e la
finanza,
Sapienza Università di Roma
Italy
Francesca Mariani
Istituto di Neurobiologia e Medicina Molecolare,
CNR
Italy
Luca Tardella
Dipartimento di Statistica,
Sapienza Università di Roma
Italy
Abstract:
Motivated by a real data set deriving from a study on the genetic determinants
of the behavior of Mycobacterium tuberculosis (MTB) hosted in macrophage, we
take advantage of the presence of control spots and illustrate modelling issues
for background correction and the ensuing empirical findings resulting from a
Bayesian hierarchical approach to the problem of detecting differentially
expressed genes. We prove the usefulness of a fully integrated approach where
background correction and normalization are embedded in a single model-based
framework, creating a new tailored model to account for the peculiar features
of DNA array data where null expressions are planned by design. We also
advocate the use of an alternative normalization device resulting from a
suitable reparameterization. The new model is validated by using both simulated
and our MTB data. This work suggests that the presence of a substantial
fraction of exact null expressions might be the effect of an imperfect
background calibration and shows how this can be suitably re-calibrated with
the information coming from control spots. The proposed idea can be extended
to all experiments in which a subset of genes whose expression levels can be
ascribed mainly to background noise is planned by design.
Keywords:
background correction; Bayesian hierarchical model; blank spots;
differential expression; gene expression data; normalization issues
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