几种数量综述方法的介绍与比较

综述是科学研究中的一种重要方法,数量综述较传统的叙述性综述有许多优多,文章介绍并比较了目前在科学研究中比较流行的几数量综述方法.     

构建数量性状基因图谱的几种统计方法

以线性数学模型为线索,概述了用于构建数量性状基因图谱的几种主要统计方法,包括方差分析法、标记回归法、区间作图法、复合区间作图法、Jansen的复合区间作图法、双侧标记回归法以及新近发展的多区间作图法和多亲本作图法等.讨论了各种方法的优缺点. Abstract:Statistical methods for mapping QTLs were summarized, including one marker analysis, arker regression analysis,interval mapping (IM),composite interval mapping (CIM),Jansen's composite interval mapping, flanking marker regression analysis,multiple interval mapping (MIM) and multiple families mapping.Their advantages and disadvantages were discussed.     

动物栖息地选择评估的常用统计方法

利用合适的统计学方法能够更准确地理解动物的栖息地选择。本文通过对2003~2012年期间,10个国际期刊所发表的177篇关于鸟类和兽类栖息地选择论文的30种统计学方法进行分析,简要概述了目前流行的栖息地选择统计学分析方法及特点,同时对同时期的中文文献也进行了简要分析。目前关于动物栖息地选择较为流行的分析方法主要有逻辑斯蒂回归、资源选择函数、成分分析、广义线性模型、多元方差分析、基于欧几里德距离的方法、广义线性混合模型、生态位因子分析、基于个体模型、典型相关分析、物种分布模型等。广义线性模型、逻辑斯蒂回归、多元方差分析和基于欧几里德距离这些方法可以很灵活地用来分析数据,但是缺乏一个有生态学意义的理论框架。资源选择函数和生态位因子分析各自为栖息地选择研究提供了一个统一的理论框架。基于个体的模型是一个自下而上的过程,很难在系统水平形成理论。232篇国内文章中使用较多的方法是主成分分析、Mann-Whitney U检验、t检验、卡方检验、判别分析、方差分析、Vanderloeg选择系数和Scavia选择指数、逻辑斯蒂回归、Kruskal-Wallis H检验和多元回归分析等。在实际研究中,应根据所要解决的研究问题,选择切实可行的分析方法。     

Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.     

Review of the Methods Frequently Used to Estimate the Abundance of Orthoptera in Grassland Ecosystems

Ecologists have quantified Orthoptera (grasshoppers and crickets) density in a wide variety of conservation studies. Objective determination of Orthoptera population size is possible using mark-release recapture techniques but these are time-consuming and of little use for all but the smallest scale studies. Therefore, a wide range of sampling techniques have been devised to quantify population density and the most commonly used methods include sweep netting and quadrat counts. It is the aim of this paper to critically review studies that have used these techniques and to provide useful suggestions for non-specialists on which method may be most applicable to their study site. This paper reviews a selection of the extensive literature reporting studies estimating the abundance of grasshoppers (Acrididae) in a wide range of grassland ecosystems. Where possible, studies on bush-crickets (Tettigoniidae) and crickets (Gryllidae) are included reflecting their overall contribution to assemblage diversity in grassland ecosystems and to highlight the need for further investigations of sampling efficiency on these two under-researched families.The most rapid and inexpensive sampling methods, such as quadrat and transect counts, involve ‘flushing’ grasshoppers from the sward. These techniques are fairly accurate in short, open swards (<50 cm sward height) where grasshopper densities are low (<2 adults per m²). At higher population densities (>2 adults per m²), methods which require the capture of grasshoppers such as box quadrats and sweep netting may be more appropriate. Sampling grasshopper populations in taller vegetation (>50 cm sward height) is more problematic as the efficiency of many techniques may be reduced by vegetation structure. Methods such as timed counts can be used at low densities (<2 adults per m²) and night trapping might be most applicable where high numbers of grasshoppers are present (>2 adults per m²).There is an urgent need for development of a standardised sampling technique that can produce comparable data from studies with a wide variety of observers in grasslands with differing vegetation structures and grasshopper densities.     

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.     

A Study of Statistical Methods for Function Prediction of Protein Motifs

Automatic discovery of new protein motifs (i.e. amino acid patterns) is one of the major challenges in bioinformatics. Several algorithms have been proposed that can extract statistically significant motif patterns from any set of protein sequences. With these methods, one can generate a large set of candidate motifs that may be biologically meaningful. This article examines methods to predict the functions of these candidate motifs. We use several statistical methods: a popularity method, a mutual information method and probabilistic translation models. These methods capture, from different perspectives, the correlations between the matched motifs of a protein and its assigned Gene Ontology terms that characterise the function of the protein. We evaluate these different methods using the known motifs in the InterPro database. Each method is used to rank candidate terms for each motif. We then use the expected mean reciprocal rank to evaluate the performance. The results show that, in general, all these methods perform well, suggesting that they can all be useful for predicting the function of an unknown motif. Among the methods tested, a probabilistic translation model with a popularity prior performs the best.     

Applications of the Repeatability of Quantitative Imaging Biomarkers: A Review of Statistical Analysis of Repeat Data Sets

Repeat imaging data sets performed on patients with cancer are becoming publicly available. The potential utility of these data sets for addressing important questions in imaging biomarker development is vast. In particular, these data sets may be useful to help characterize the variability of quantitative parameters derived from imaging. This article reviews statistical analysis that may be performed to use results of repeat imaging to 1) calculate the level of change in parameter value that may be seen in individual patients to confidently characterize that patient as showing true parameter change, 2) calculate the level of change in parameters value that may be seen in individual patients to confidently categorize that patient as showing true lack of parameter change, 3) determine if different imaging devices are interchangeable from the standpoint of repeatability, and 4) estimate the numbers of patients needed to precisely calculate repeatability. In addition, we recommend a set of statistical parameters that should be reported when the repeatability of continuous parameters is studied.     

A Statistical Review of Carcinoma of the Lip

Among 3166 patients with microscopically confirmed squamous cell carcinoma of the lip registered at The Ontario Cancer Treatment and Research Foundation''s Regional Clinics in 1938-1955, the five-year crude survival rate was 65%, and the five-year net survival rate 89%. Survival was influenced by age, site and size of primary lesion, local and regional invasion, long delay, and treatment method. The initial treatment appeared to control the primary lesion in 84% of cases and involved lymph nodes in 58%. The net survival rates improved over the survey period. Findings confirm the usefulness of the proposed TNM staging.     

用于GWAS结果后续研究的PBA方法简介

自提出全基因组关联研究(genome-wide association study,GWAS)设想以来,在人类复杂疾病和水稻农艺性状关联研究方面,GWAS已得到广泛运用。但作为一种典型的单标记研究方法,GWAS不能检测小效应的遗传变异,而稀有变异间的联合效应往往与表型密切相关,因此,需对GWAS结果进行深入的数据挖掘。基于通路的分析方法(pathway-based analysis,PBA)就是利用基因功能、生物代谢通路等相关信息建立的对GWAS结果进行二次挖掘的方法。该方法能从GWAS结果挖掘出与性状、疾病相关联的通路及具有相同功能的基因集等数据,从而获得更多的遗传信息。现对PBA的出现、计算方法和相关软件进行简要综述,以期为人们进行通路分析提供参考。     

Biosorption with Algae: A Statistical Review

ABSTRACT

The state of the art in the field of biosorption using algae as biomass is reviewed. The available data of maximum sorption uptake (q_max) and biomass-metal affinity (b) for Cd^{2 +}, Cu^{2 +}, Ni^{2 +}, Pb^{2 +} and Zn^{2 +} were statistically analyzed using 37 different algae (20 brown algae, 9 red algae and 8 green algae). Metal biosorption research with algae has used mainly brown algae in pursuit of treatments, which improve its sorption uptake. The information available in connection with multimetallic systems is very poor. Values of q_max were close to 1 mmol/g for copper and lead and smaller for the other metals. Metal recovery performance was worse for nickel and zinc, but the number of samples for zinc was very small. All the metals except lead present a similar affinity for brown algae. The difference in the behavior of lead may be due to a different uptake mechanism. Brown algae stand out as very good biosorbents of heavy metals. The best performer for metal biosorption is lead.