Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data

Background  

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) is a proteomics tool for biomarker discovery and other high throughput applications. Previous studies have identified various areas for improvement in preprocessing algorithms used for protein peak detection. Bottom-up approaches to preprocessing that emphasize modeling SELDI data acquisition are promising avenues of research to find the needed improvements in reproducibility.     

Use and abuse of variance models in regression

In (nonlinear) regression with heteroscedastic errors, introduction of a variance model can be useful to obtain good estimators of the regression parameter. For example, the variance model can be used to obtain the optimal weights in weighted least squares. Methodology of this kind is often used in the analysis of assay data in clinical chemistry, pharmacokinetics, and toxicology. In a series of papers in the pharmacological literature, Sheiner and Beal and others advocate the extended least squares (ELS) methodology that combines regression and variance model into a single objective function based on normal-theory maximum likelihood. The inadequacy of this method is folklore in the (mathematical) statistical literature. In this article it is pointed out that this methodology may lead to inconsistent estimators in practically relevant situations. A review is given of other methods that may be preferable to ELS.     

Analysis of variance components in gene expression data

MOTIVATION: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances. RESULTS: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.     

Multiallelic models of genetic effects and variance decomposition in non-equilibrium populations

Quantitative genetics stems from the theoretical models of genetic effects, which are re-parameterizations of the genotypic values into parameters of biological (genetic) relevance. Different formulations of genetic effects are adequate to address different subjects. We thus need to generalize and unify them under a common framework for enabling researchers to easily transform genetic effects between different biological meanings. The Natural and Orthogonal Interactions (NOIA) model of genetic effects has been developed to achieve this aim. Here, we further implement the statistical formulation of NOIA with multiple alleles under Hardy–Weinberg departures (HWD). We show that our developments are straightforwardly connected to the decomposition of the genetic variance and we point out several emergent properties of multiallelic quantitative genetic models, as compared to the biallelic ones. Further, NOIA entails a natural extension of one-locus developments to multiple epistatic loci under linkage equilibrium. Therefore, we present an extension of the orthogonal decomposition of the genetic variance to multiple epistatic, multiallelic loci under HWD. We illustrate this theory with a graphical interpretation and an analysis of published data on the human acid phosphatase (ACP1) polymorphism.     

Models for the continuous culture of microorganisms under both oxygen and carbon limiting conditions

Two models for predicting the behavior of cultures of microorganisms under both oxygen and carbon limiting conditions have been evlauated on a chemostat growing Candida utilis on a glycolysis suppressing glycerol medium. The work indicated that parameter values obtained under wholly oxygen limiting or wholly carbon limiting conditions successfully predict the behavior of the chemostat under the wide range of flow and substrate concentration conditions tested. Both models are satisfactory and hence it is deduced that the simpler one may be used with confidence. It was found that Monod kinetics were applicable to the growth rate dependence on oxygen concentration but that Contois kinetics were superior for the corresponding dependence on carbon substrate concentration.     

Time-budget data: Log-linear and analysis of variance compared

Analysis of variance (ANOVA) and log-linear analyses of time-budget data from a study of sloth bear enclosure utilization are compared. Two sampling models that plausibly underlie such data are discussed. Either could lead to an analysis of variance, but only one to a log-linear analysis. Given an appropriate sampling model and appropriate data, there is much to recommend log-linear analysis, despite its unfamiliarity to most animal behaviorists. One need not worry whether distribution assumptions are violated. Moreover, the data analyzed are the data collected, not estimates derived from those data, and thus no power is lost through a data reduction step. No matter what analysis is used, effect size should be taken into consideration. Multiple R² can be used for ANOVA, but no directly comparable statistic exists for log-linear analyses. One possible candidate for a log-linear R² analog is discussed here, and appears to give sensible and interpretable results. © 1992 Wiley-Liss Inc.     

Nearest neighbour adjustment and linear variance models in plant breeding trials

This paper reviews methods for nearest neighbour analysis that adjust for local trend in one dimension. Such methods are commonly used in plant breeding and variety testing. The focus is on simple differencing methods, including first differences and the Papadakis method. We discuss mixed model representations of these methods on the scale of the observed data. Modelling observed data has a number of practical advantages compared to differencing, for example the facility to conveniently compute adjusted cultivar means. Most models considered involve a linear variance-covariance structure and can be represented as state-space models. The reviewed methods and models are exemplified using three datasets.     

On the Asymptotic Relative Efficiency of the Mantel-Haenszel Test

The Mantel-Haenszel test is optimal when the odds ratio is constant. This paper investigates the effects of departures from the assumption of a constant odds ratio on the behavior of the Mantel-Haenzel test. A simple approximation is proposed for the non-null distribution of the test statistic. Based on this approximation, the asymptotic relative efficiency of the Mantel-Haenszel test, compared to the overall χ² test for no partial association, is calculated. For the case of 2 strata, it is shown that the Mantel-Haenszel test is efficient as long as the logarithms of the odds ratios are of the same sign and their absolute values exceed 1.