Peak quantification in surface-enhanced laser desorption/ionization by using mixture models

Surface-enhanced laser desorption/ionization (SELDI) time of flight (TOF) is a mass spectrometry technology for measuring the composition of a sampled protein mixture. A mass spectrum contains peaks corresponding to proteins in the sample. The peak areas are proportional to the measured concentrations of the corresponding proteins. Quantifying peak areas is difficult for existing methods because peak shapes are not constant across a spectrum and because peaks often overlap. We present a new method for quantifying peak areas. Our method decomposes a spectrum into peaks and a baseline using so-called statistical finite mixture models. We illustrate our method in detail on 8 samples from culture media of adipose tissue and globally on 64 samples from serum to compare our method to the standard Ciphergen method. Both methods give similar estimates for singleton peaks, but not for overlapping peaks. The Ciphergen method overestimates the heights of such peaks while our method still gives appropriate estimates. Peak quantification is an important step in pre-processing SELDI-TOF data and improvements therein will pay off in the later biomarker discovery phase.     

生态网络分析方法研究综述

生态网络分析方法是分析生态系统作用关系、辨识系统内在、整体属性的一种有效的系统分析方法。总结了生态网络分析方法的主要研究成果:网络结构特性、网络稳定性、网络上升性、网络效能等;介绍了构建生态网络模型过程和群落构建规则;以德国西部城市诺伊斯河口氮循环为例,介绍David K是如何运用生态网络分析方法来揭示网络中的微动力流循环规律。生态网络分析方法的主要贡献:(1)对人们凭经验感知的生态系统分室间的关联关系,采用了严密的数学模型和推导进行了描述和证明;(2)为生态系统的微动力流循环的研究提供了方法,对生态系统中物质流的间接循环作用进行了科学论证;(3)不仅为分析生态系统提供了一种科学的数学方法,而且,它为探索生态系统提供了不同与牛顿世界观的崭新的认识论。总结与回顾生态网络分析方法,有益于该方法的运用和进一步完善。     

Some remarks about the analysis of active control studies

In an active-controlled trial, the experimental treatment can be declared to be non-inferior to the control if the confidence interval for the difference excludes a fixed pre-specified margin. Recently, some articles have discussed an alternative method where the data from the current study and placebo-controlled studies for the active control are combined together into a single test statistic to test whether a fixed fraction of the effect of the active control is preserved. It has been shown that, conditional on nuisance parameters from the active-controlled study, a fixed margin can be defined that will be operationally equivalent to this latter method. In this article, we will discuss statistical properties associated with these approaches. Specifically, the interim monitoring boundaries and level of evidence will be considered.     

Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Summary

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non‐randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non‐randomized study.     

The Sholl analysis of neuronal cell images: semi-log or log-log method?

Although the Sholl analysis is a quantitative method for morphometric neuronal studies and its application provides many benefits to neurobiology since it is obvious, common and meaningful, there are many unresolved theoretical issues that need to be addressed. Nevertheless, it can be practiced without much background or sophistication. The two different methods of the Sholl analysis--log-log and semi-log--have been applied previously without a clear basis as to what to use. To make an adequate choice of the method, one should try and accept that one which gives a better result. We consider that some of the underlying principles, assumptions and limitations for the Sholl analysis can be found in basic mathematics. In order to compare the results of applications of the semi-log and log-log methods to the same neuron, we introduce the concept of determination ratio as the ratio of the coefficient of determination for the semi-log method and that for the log-log method. If the semi-log method is better as related to the log-log method, the value of this parameter is larger than 1. Having in mind that dendrites exhibit enormously diverse forms, we point out that the semi-log Sholl method is more frequently utilizable in practice. Only the neurons, whose dendritic trees have one or a few sparsely ramified dendrites being much longer than the rest ones, could be successfully and exactly analysed using the log-log method. We also compare the Sholl analysis with fractal analysis for the characterization of neuronal arborization patterns and found that between the Sholl and fractal analysis exist various and important analogies.     

Compound method based on financial accounts versus process-based analysis in product carbon footprint: A comparison using wood pallets

Differences between product and corporate carbon footprint seriously hinder its wider implementation. The compound method based on financial accounts is a tiered hybrid analysis developed from advances in ecological footprint which main strength is its capability to calculate both product and corporate carbon footprint in a comprehensive assessment. This work aims to evaluate the compound method based on financial accounts as a tool for product carbon footprint. The objective is twofold: (1) to assess its advantages and disadvantages for product carbon footprint; and (2) to evaluate differences with process-based analysis. EUR-flat wood pallet is selected as the unit of study in a cradle-to-gate life-cycle perspective. Due to method requirements, a Spanish pallet manufacturer that produces and markets 1.6 million EUR-flat wood pallets annually has been selected for an in-depth assessment. Our life-cycle implementation reveals the following findings: (1) emissions from the compound method based on financial accounts are 22% higher than ones from process-based analysis, (2) process-based analysis provides greater detail in first life cycle phases than the compound method based on financial accounts, and (3) calculation time is drastically reduced using the compound method based on financial accounts. This study shows the compound method based on financial accounts as providing a correct assessment of the amount of direct and indirect emissions with easy-to-obtain data. Calculation time is drastically reduced, making it applicable to all shapes and sizes of businesses. It could, therefore, improve the CF in both approaches (product and corporate), stimulating innovation and increasing support for sustainable consumption decisions. Process-based analysis, in contrast, is not so easily implemented due to the large number of units involved in the upstream supply chain, and the use of thresholds hinders comparison. Even with these advantages, MC3 should consider other areas for improvement.     

Pathway analysis for genome-wide genetic variation data: Analytic principles,latest developments,and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for indepth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years,uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold:(1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data;(2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and(3)discuss remaining challenges and future directions for further method developments.     

Discrimination of higher plant calluses based on embryogenic capacity and taxonomic classification by pyrolysis mass spectrometry

Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum, and has been widely applied to the discrimination of closely related microbial strains. Minimally prepared samples of embryogenic and non-embryogenic calluses derived from various higher plants (sweet potato, morning glory, Korean ginseng, Siberian ginseng, and balloon flower) were subjected to PyMS for spectral fingerprinting. A dendrogram based on the unweighted pair group method, with arithmetic mean of pyrolysis mass spectra, divided the calluses into Siberian ginseng embryogenic callus and the others, which were subsequently divided into embryogenic and non-embryogenic callus groups, regardless of plant species from which the calluses were derived. In the non-embryogenic callus group, the dendrogram was in agreement with the known taxonomy of the plants. These results indicate that PyMS analysis could be applied for discriminating plant calluses based on embryogenic capacity and taxonomic classification.     

A Bayesian approach to case-control studies with errors in covariables

We develop Bayesian methodology for the analysis of case-control data with covariate imprecision. The pretense that the distribution of the imprecisely measured covariate is discrete on a heuristically chosen support set leads to a method which is reasonably simple to implement, and can be applied to different study designs. The methodological development emphasizes the interplay between retrospective and prospective analysis. We illustrate the method on simulated data, and on data from a cancer study where smoking history is the imprecisely measured covariate.     

Partitioning the variation among spatial,temporal and environmental components in a multivariate data set

Abstract We propose a method of partitioning the variation in a multivariate set of data according to (i) environmental variables, (ii) variables describing the spatial structure in the data and (iii) temporal variables. This method is an extension of an existing method for partialling out the spatial component of environmental variation, using canonical analysis. Our proposed method extends this approach by including temporal variables in the analysis. Thus, the partitioning of variation for a data matrix of species’abundances or biomass can include, by our methodology, the following components: (1) pure environmental, (2) pure spatial, (3) pure temporal, (4) pure spatial component of environmental, (5) pure temporal component of environmental, (6) pure combined spatial/temporal component, (7) combined spatial/temporal component of environmental and (8) unexplained. In addition, permutation testing accompanying the analyses allows tests of significance for the relationship between the different components and the species data. We illustrate the method with a set of survey data of penaeid species (prawns) obtained on the far northern Great Barrier Reef, Australia. This extension is a useful tool for multivariate analysis of ecological data from surveys, where space, time and environment commonly overlap and are important influences on observed variation.     

A group sequential test for survival trials: an alternative to rank-based procedures

A method of interim monitoring is described for survival trials in which the proportional hazards assumption may not hold. This method extends the test statistics based on the cumulative weighted difference in the Kaplan-Meier estimates (Pepe and Fleming, 1989, Biometrics 45, 497-507) to the sequential setting. Therefore, it provides a useful alternative to the group sequential linear rank tests. With an appropriate weight function, the test statistic itself provides an estimator for the cumulative weighted difference in survival probabilities, which is an interpretable measure for the treatment difference, especially when the proportional hazards model fails. The method is illustrated based on the design of a real trial. The operating characteristics are studied through a small simulation.     

LC/ESI-MS/MS detection of FAs by charge reversal derivatization with more than four orders of magnitude improvement in sensitivity

Quantitative analysis of fatty acids (FAs) is an important area of analytical biochemistry. Ultra high sensitivity FA analysis usually is done with gas chromatography of pentafluorobenzyl esters coupled to an electron-capture detector. With the popularity of electrospray ionization (ESI) mass spectrometers coupled to liquid chromatography, it would be convenient to develop a method for ultra high sensitivity FA detection using this equipment. Although FAs can be analyzed by ESI in negative ion mode, this method is not very sensitive. In this study, we demonstrate a new method of FA analysis based on conversion of the carboxylic acid to an amide bearing a permanent positive charge, N-(4-aminomethylphenyl)pyridinium (AMPP) combined with analysis on a reverse-phase liquid chromatography column coupled to an ESI mass spectrometer operating in positive ion mode. This leads to an ∼60,000-fold increase in sensitivity compared with the same method carried out with underivatized FAs. The new method is about 10-fold more sensitive than the existing method of gas chromatography/electron-capture mass spectrometry of FA pentafluorobenzyl esters. Furthermore, significant fragmentation of the precursor ions in the nontag portion improves analytical specificity. We show that a large number of FA molecular species can be analyzed with this method in complex biological samples such as mouse serum.     

Flow injection chemiluminescence determination of dihydralazine sulphate based on permanganate oxidation sensitized by rhodamine B.

A novel flow injection chemiluminescence (CL) method for the determination of dihydralazine sulphate (DHZS) is described. The method is based on the CL produced during the oxidation of DHZS by acidic permanganate solution in the presence of rhodamine B. Rhodamine B is suggested as a fluorescing compound for the energy-transferred excitation. The CL emission allows quantitation of DHZS concentration in the range 5-800 ng/mL, with a detection limit of 1.9 ng/mL (3sigma). The experimental conditions for the CL reaction are optimized and the possible reaction mechanism is discussed. The method has been applied to the determination of DHZS in pharmaceutical preparations and compares well with the high performance liquid chromatography (HPLC) method.     

Statistical Comment on a New Method for Noncompartmental Pharmacokinetic Analysis

A new method for parameter estimation and calculation of moments of concentration-time curves recently introduced by H. Knolle is reviewed from a statistical point of view.     

基因芯片数据分析

基因芯片是近年发展起来的自动化的、高通量的研究生物学问题的一门新技术。它综合了多学科的成就,在大规模研究基因功能的领域中已经有了卓有成效的应用。对大量产生的数据如何有效地分析,成为芯片研究中的一个热点。总体上,数据分析方法可分为非指导的方法和指导的方法。在分析前需要对数据进行标准化和精简,对分析结果需要检验和进行生物学分析。作者对目前常用的一些统计学方法作一介绍,并讨论其适用范围及优缺点。     

Methodologically induced differences in oak site classifications in a homogeneous tree-ring network

Spatiotemporal variations in tree growth are induced by varying environmental conditions. Different methods like variants of the principal component analysis and the hierarchical cluster analysis are commonly applied in dendroecology to separate subsets of growth patterns within large tree-ring datasets. To seek for methodological differences in classification techniques and their specific characteristics, we compared three standard methods using a homogeneous oak (Quercus spp.) network from temperate forests in Central-West Germany. Classifications of the original dataset consisting of 46 oak ring-width sites, carried out with the varimax rotated principle component analysis, Ward's method and the average linkage method, reveal differences in the classification of approximately 20% of the sites. Analyses with modified datasets are calculated to evaluate effects of dataset extension, different time periods and different tree-ring detrendings. The application of the principal component analysis generally leads to the most stable site classifications, whereas the most sensitive response to changes in the dataset is obtained by Ward's method. The average linkage method separates single sites in the classification and thus emphasises outliers within the tree-ring network.     

沙拐枣果实形态变异的数值分析

研究利用新疆、甘肃河西、宁夏沙坡头地区采集的头状沙拐枣（Ｃalligonum caput-medusae)、红皮沙拐枣(C.rubicundum)、白皮沙拐枣（Ｃ.leucocladum)、泡果沙拐枣（Ｃ.junceum)、河西沙拐枣（C.potaninii)和蒙古沙拐枣（C.mongolicum)果实的形态性状指标,应用双因素重复方差分析法,对不同种群间果实的形态变异进行了研究,得出红皮沙拐枣、蒙古沙拐枣、白皮沙拐枣、泡果沙拐枣下不同种群间果实的形态变异差异显著,头状沙拐枣、河西沙拐枣果灾的形态变里差异不显著,用系统聚类分析,对各种群间的相似性进行了研究。     

立枯丝核菌遗传多样性研究方法介绍

立枯丝核菌（Rhizoctonia solani Kühn）是一个集合种，遗传多样性丰富.关于遗传多样性的研究一直是R. solani研究的热点.本文就用于R. solani遗传多样性研究的方法进行了综述.分别解释并阐述了各种方法的优缺点，其中菌丝融合法是研究R. solani遗传多样性的传统方法，该法需借助显微镜且耗时耗力；同工酶法简便、高效、低廉，但常需要与其它方法联用；脂肪酸法操作难度小，价格相对较低，但该法受菌株生长状况和脂肪酸脂化方法的限制；分子标记法方法众多，各有利弊，通过比较发现rDNA-ITS是研究R. solani遗传多样性比较合适的方法.本文还介绍了不同方法在R. solani遗传多样性研究中的具体应用.     

Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis

The inverse normal and Fisher's methods are two common approaches for combining P-values. Whitlock demonstrated that a weighted version of the inverse normal method, or 'weighted Z-test', is superior to Fisher's method for combining P-values for one-sided T-tests. The problem with Fisher's method is that it does not take advantage of weighting and loses power to the weighted Z-test when studies are differently sized. This issue was recently revisited by Chen, who observed that Lancaster's variation of Fisher's method had higher power than the weighted Z-test. Nevertheless, the weighted Z-test has comparable power to Lancaster's method when its weights are set to square roots of sample sizes. Power can be further improved when additional information is available. Although there is no single approach that is the best in every situation, the weighted Z-test enjoys certain properties that make it an appealing choice as a combination method for meta-analysis.     

A simplified and efficient method for the analysis of fatty acid methyl esters suitable for large clinical studies

Conventional sample preparation for fatty acid analysis is a complicated, multiple-step process, and gas chromatography (GC) analysis alone can require >1 h per sample to resolve fatty acid methyl esters (FAMEs). Fast GC analysis was adapted to human plasma FAME analysis using a modified polyethylene glycol column with smaller internal diameters, thinner stationary phase films, increased carrier gas linear velocity, and faster temperature ramping. Our results indicated that fast GC analyses were comparable to conventional GC in peak resolution. A conventional transesterification method based on Lepage and Roy was simplified to a one-step method with the elimination of the neutralization and centrifugation steps. A robotics-amenable method was also developed, with lower methylation temperatures and in an open-tube format using multiple reagent additions. The simplified methods produced results that were quantitatively similar and with similar coefficients of variation as compared with the original Lepage and Roy method. The present streamlined methodology is suitable for the direct fatty acid analysis of human plasma, is appropriate for research studies, and will facilitate large clinical trials and make possible population studies.