title: Informative or noninformative calls for gene expression: a latent variable approach.
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.
authors.affiliation: Universiteit Hasselt & Katholieke Universiteit Leuven.
authors.forename: Adetayo
authors.initials: A
authors.lastname: Kasim
authors.forename: Dan
authors.initials: D
authors.lastname: Lin
authors.forename: Suzy
authors.initials: S
authors.lastname: Van Sanden
authors.forename: Djork-Arné
authors.initials: DA
authors.lastname: Clevert
authors.forename: Luc
authors.initials: L
authors.lastname: Bijnens
authors.forename: Hinrich
authors.initials: H
authors.lastname: Göhlmann
authors.forename: Dhammika
authors.initials: D
authors.lastname: Amaratunga
authors.forename: Sepp
authors.initials: S
authors.lastname: Hochreiter
authors.forename: Ziv
authors.initials: Z
authors.lastname: Shkedy
authors.forename: Willem
authors.initials: W
authors.lastname: Talloen
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D015233:Models, Statistical; D015336:Molecular Probe Techniques / Q000706:statistics & numerical data; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data