The presentation of treatment responses from block experiments after analysis of variance of transformed data

In pearl millet, severe water deficit during the period of panicle development delays flowering. The flowering response of both main shoot and tillers to water stress during panicle development was investigated using four hybrids. Panicle initiation of all tillers occurred in the three early genotypes despite water stress. In the late genotype, however, panicle initiation of tillers occurred only after the release of stress. The delay in flowering due to water stress was more pronounced in the tillers than in the main shoot. However, the proportion of tillers producing an inflorescence was increased by water stress. Grain yield losses on the main shoot by water stress were compensated by an increase in tiller grain yields. Delay in flowering and buffering by tillers provide an important adaptive mechanism to overcome a period of drought stress prior to flowering.     

Information theory and the analysis of ligand-binding data

The phenomenological principles of information theory are used in the analysis of ligand-binding phenomena in biological macromolecules. Information maps are constructed to visualize regions of ligand chemical potential with maximum amount of information and to devise suitable experimental strategies therefrom. Extensive simulation studies and analysis of experimental data also point out the properties of information used as a weighting procedure in nonlinear least-squares analyses.     

Statistical analysis of survival data from radiation countermeasure experiments

We present an introduction to, and examples of, Cox proportional hazards regression in the context of animal lethality studies of potential radioprotective agents. This established method is seldom used to analyze survival data collected in such studies, but is appropriate in many instances. Presenting a hypothetical radiation study that examines the efficacy of a potential radioprotectant both in the absence and presence of a potential modifier, we detail how to implement and interpret results from a Cox proportional hazards regression analysis used to analyze the survival data, and we provide relevant SAS? code. Cox proportional hazards regression analysis of survival data from lethal radiation experiments (1) considers the whole distribution of survival times rather than simply the commonly used proportions of animals that survived, (2) provides a unified analysis when multiple factors are present, and (3) can increase statistical power by combining information across different levels of a factor. Cox proportional hazards regression should be considered as a potential statistical method in the toolbox of radiation researchers.     

A model for the analysis of growth data from designed experiments

A model for growth data from designed experiments is presented which extends the stochastic differential equation of Sandland and McGilchrist (1979, Biometrics 35, 255-272). Residual maximum likelihood (REML) is used to estimate the parameters of the model. The model is easily extended to incomplete data and is shown to overcome some of the practical difficulties encountered with the profile model. The procedure is applied to data from experiments on pigs and sheep.     

The ribosome-structure and functional ligand-binding experiments using cryo-electron microscopy

Cryo-electron microscopy has greatly advanced our understanding of the basic steps of protein synthesis in the bacterial ribosome. This article gives an overview of what has been achieved so far. Through three-dimensional visualization of complexes that represent the ribosome in defined binding states, locations were derived for the tRNA in A, P, and E sites, as well as the elongation factors. In addition, the pathways of messenger RNA and the exiting polypeptide chain could be inferred.     

Efficient analysis of Weibull survival data from experiments on heterogeneous patient populations

An efficient method is presented for analyses of death rated in one-way or cross-classified experiments where expected survival time for a patient at time of entry on trial is a function of observable covariates. The survival-time distribution used is a Weibull form of Cox's (1972) model. The analysis proceeds in two steps. In the first, goodness of fit of the model is checked, inefficient estimates of the parameters are obtained, and survival times adjusted for the entry covariates are calculated. In the second, efficient estimates and tests for the rate parameters are obtained. These can easily be calculated using hand or desk equipment. Reorganized data sets can be analyzed without repetition of step one, thereby reducing the computational load to hand level and facilitating exploratory data analysis.     

Variance reduction by simultaneous multi-exponential analysis of data sets from different experiments

The analysis of experimental data from the photocycle of bacteriorhodopsin (bR) as sums of exponentials has accumulated a large amount of information on its kinetics which is still controversial. One reason for ambiguous results can be found in the inherent instabilities connected with the fitting of noisy data by sums of exponentials. Nevertheless, there are strategies to optimize the experiments and the data analysis by a proper combination of well known techniques. This paper describes an applicable approach based on the correct weighting of the data, a separation of the linear and the non-linear parameters in the process of the least squares approximation, and a statistical analysis applying the correlation matrix, the determinant of Fisher's information matrix, and the variance of the parameters as a measure of the reliability of the results. In addition, the confidence regions for the linear approximation of the non-linear model are compared with confidence regions for the true non-linear model. Evaluation techniques and rules for an optimum experimental design are mainly exemplified by the analysis of numerically generated model data with increasing complexity. The estimation of the number of exponentials significant for the interpretation of a given set of data is demonstrated by using records from eight absorption and photocurrent experiments on the photocycle of bacteriorhodopsin.Offprint requests to: K.-H. Müller     

A computer method for analysis of radioactivity data from single and double labeled experiments

A generally applicable program has been devised for the machine processing and plotting of radioactivity data. The program can be used with single or double labeled experiments; it includes numerous options which increase its flexibility and facilitate optimization of data presentation.     

Overcoming confounded controls in the analysis of gene expression data from microarray experiments

A potential limitation of data from microarray experiments exists when improper control samples are used. In cancer research, comparisons of tumour expression profiles to those from normal samples is challenging due to tissue heterogeneity (mixed cell populations). A specific example exists in a published colon cancer dataset, in which tissue heterogeneity was reported among the normal samples. In this paper, we show how to overcome or avoid the problem of using normal samples that do not derive from the same tissue of origin as the tumour. We advocate an exploratory unsupervised bootstrap analysis that can reveal unexpected and undesired, but strongly supported, clusters of samples that reflect tissue differences instead of tumour versus normal differences. All of the algorithms used in the analysis, including the maximum difference subset algorithm, unsupervised bootstrap analysis, pooled variance t-test for finding differentially expressed genes and the jackknife to reduce false positives, are incorporated into our online Gene Expression Data Analyzer ( http:// bioinformatics.upmc.edu/GE2/GEDA.html ).     

Challenges in the presentation and analysis of plant-macrofossil stratigraphical data

Plant-macrofossil analysis is being increasingly used in Quaternary science, particularly palaeoecology and vegetation history. Although the techniques of macrofossil analysis are well-tried and relatively simple, the resulting data consisting of qualitative binary presences and absences, ordinal classes, and quantitative counts are not simple from the viewpoint of numerical data-analysis. This essay reviews the nature of macrofossil data and discusses the problem of zero and non-zero values. Problems in the presentation of macrofossil data are outlined and possible solutions are discussed. The handling of such data is discussed in terms of data summarisation, data analysis, and data interpretation. Newly developed numerical methods that take account of the mixed nature and the stratigraphical ordering of macrofossil data are outlined, such as (distance-based) multivariate regression trees, canonical analysis of principal coordinates, principal curves, cascade multivariate regression trees, and RLQ analysis. These and other techniques outlined have the potential to help exploit the full potential of macrofossil stratigraphical data in Quaternary palaeoecology.     

On the evaluation of data from flow-dialysis experiments

Flow dialysis can be used to measure (i) ligand binding to macromolecules and (ii) the size of transmembrane ion gradients. Generally an approximate method is used to calculate the binding or gradient parameters from the raw data. Here we present a simple but exact method and evaluate the errors that may arise when the approximate method is used to calculate the magnitude of ion gradients. In addition, equations are presented that allow for a correction for sampling from or additions to the upper compartment of a flow-dialysis vessel during the measurements. Setty and Hendler [(1982) J. Biochem. Biophys. Methods 7, 35-46] have reported artifacts in the measurement of ion-gradients caused by the addition of electron donors to the upper compartment of a flow-dialysis cell. Here we extend their observations and suggest additional methods to prevent such artifacts.     

Analysis of data from experiments using double labeling

Frequently as a result of experiments in which two isotopes are used one is left with a sequence of samples, the ratio of labeling in each sample, and the problem of analyzing the ratios. Suppose that the experiments are designed so that one expects uniform labeling except for one or two special groups of samples. The problem, then, is to find these groups. Because of the variability in the count rate from sample to sample, the variance of the ratios differs from sample to sample making statistical analysis difficult. Furthermore, there is significant serial correlation in the sample disintegrations per minute for each of the isotopes. We have found that the serial correlation in the labeling ratio is small and of questionable significance in controls but becomes significant when there is a subsequence of samples in which the labeling ratio differs from that in the remainder of the gel. We examine the analysis of variance as a test for significant deviations in the labeling ratio and suggest a method for plotting deviations of labeling ratio from the average background labeling ratio. Finally, we develop a method of estimating the mean labeling ratio from the regression of disintegrations per minute of one isotope on those of the other isotope. This provides another way of plotting deviations in labeling ratio in terms of the residuals around the line of regression.     

Design and analysis of experiments with high throughput biological assay data

The design and analysis of experiments using gene expression microarrays is a topic of considerable current research, and work is beginning to appear on the analysis of proteomics and metabolomics data by mass spectrometry and NMR spectroscopy. The literature in this area is evolving rapidly, and commercial software for analysis of array or proteomics data is rarely up to date, and is essentially nonexistent for metabolomics data. In this paper, I review some of the issues that should concern any biologists planning to use such high-throughput biological assay data in an experimental investigation. Technical details are kept to a minimum, and may be found in the referenced literature, as well as in the many excellent papers which space limitations prevent my describing. There are usually a number of viable options for design and analysis of such experiments, but unfortunately, there are even more non-viable ones that have been used even in the published literature. This is an area in which up-to-date knowledge of the literature is indispensable for efficient and effective design and analysis of these experiments. In general, we concentrate on relatively simple analyses, often focusing on identifying differentially expressed genes and the comparable issues in mass spectrometry and NMR spectroscopy (consistent differences in peak heights or areas for example). Complex multivariate and pattern recognition methods also need much attention, but the issues we describe in this paper must be dealt with first. The literature on analysis of proteomics and metabolomics data is as yet sparse, so the main focus of this paper will be on methods devised for analysis of gene expression data that generalize to proteomics and metabolomics, with some specific comments near the end on analysis of metabolomics data by mass spectrometry and NMR spectroscopy.     

Log-linear models in the analysis of disease prevalence data from survival/sacrifice experiments.

This paper considers the problem of analyzing disease prevalence data from survival experiments in which there may also be some serial sacrifice. The assumptions needed for "standard" analyses are reviewed in the context of a general model recently proposed by the authors. This model is then reparametrized in log-linear form, and a generalized EM algorithm is utilized to obtain maximum likelihood estimates of the parameters for a broad class of unsaturated models. Tests based on the relative likelihood are proposed to investigate the effects of treatment, time, and the presence of other diseases on the prevalences and lethalities of specific diseases of interest. An example is given, using data from a large experiment to investigate the effects of low-level radiation on laboratory mice. Finally, some possible directions for future research are indicated.     

On the determination of species fractions from ligand-binding data. Application to human hemoglobin

A method outlined in a previous study (S.J. Gill, H.T. Gaud, J. Wyman and G. Barisas, Biophys. Chem. 8 (1978) 53) is applied for the determination of species fractions from ligand-binding data for the oxygen reaction with human hemoglobin. The results obtained by this alternative approach, which is based on the solution of a system of linear equations, are consistent with those obtained using nonlinear least-squares analysis.     

Mixture modelling of gene expression data from microarray experiments

MOTIVATION: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. RESULTS: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).     

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.