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 37,932 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

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.
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PMID:Informative or noninformative calls for gene expression: a latent variable approach. 2019 54

The paper focused primarily on certain charges, claims, and interpretations of the P value as they relate to CIs and the AIC. It as argued that some of these comparisons and claims are misleading because they ignore key differences in the procedures being compared, such as (1) their primary aims and objectives, (2) the nature of the question posed to the data, as well as (3) the nature of their underlying reasoning and the ensuing inferences. In the case of the P value, the crucial issue is whether Fisher's evidential interpretation of the P value as "indicating the strength of evidence against H0" is appropriate. It is argued that, despite Fisher's maligning of the Type II error, a principled way to provide an adequate evidential account, in the form of post-data severity evaluation, calls for taking into account the power of the test. The error-statistical perspective brings out a key weakness of the P value and addresses several foundational issues raised in frequentist testing, including the fallacies of acceptance and rejection as well as misinterpretations of observed CIs: see Mayo-Spanos (2011). The paper also uncovers the connection between model selection procedures and hypothesis testing, revealing the inherent unreliability of the former. Hence, the choice between different procedures should not be "stylistic" (Murtaugh 2013), but should depend on the questions of interest, the answers sought, and the reliability of the procedures.
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PMID:Recurring controversies about P values and confidence intervals revisited. 2480 49