逐步聚类法及其应用

本文介绍了一种非等级分类方法——逐步聚类法,并将其应用于翅果油树灌丛的数量分类研究,结果表明：逐步聚类法实现最优分类的目标过程,是依样方组内具有最小的离差平方和。样方组间具有最大的离差平方和为标准,使样方组内具有最大的同质性,样方组间具有最大的异质性,其分类结果与实际情况吻合度较高;其次,逐步聚类法只需计算每个样方到该样方形心的距离,可缩短计算时间和节省计算机内存单元,提高工作效率。 与模糊c—均值聚类和TWINSPAN结果相比,逐步聚类的结果类似于模糊c—均值聚类,即样方组内具有较高的同质性;在不要求分类结果具有明显上下级关系的前提下,逐步聚类结果要优于TWINSPAN。     

高效毛细管电泳-聚类分析法鉴别娑罗子品种

运用高效毛细管电泳法分离娑罗子中植物蛋白等成分,系统聚类法鉴定九个娑罗子样品,实验结果与原植物鉴定一致。     

模糊聚类分析法及其在群落聚类分析中的应用

以不同种类蔬菜菜地节肢动物群落作为研究对象,介绍模糊聚类分析法在群落相似性分析中的应用。得到如下的结果：在建立模糊等价关系的基础上,根据不同的需要,当λ取０．８９９时,聚合成３个类群,当λ为０．８５２时,聚合成２个类群。而Ｆ－ＰＦＳ聚类分析法则根据群落在聚类类群之间或其中的散布情况,从所有的聚类结果中,选择最佳的聚类方案。本文材料的最佳的聚类结果为：把群落聚合成２类。类群１包括：菜心、小白菜、奶白菜、芥菜、芥兰、通菜、豇豆、四季豆上的节肢动物群落;类群２包括：藤菜与苋菜上节肢动物群落。     

松林节肢动物群落结构相似性分析

应用系统聚类、模糊聚类和主成分分析法,研究湿地松纯林、湿地松一木荷混交林 和湿地松-大叶相思混交林节肢动物群落结构的相似性．结果表明,２种混交林节肢动物群 落结构比较相似,而纯林与混交林相异;林冠层亚群落与灌木一草本届亚群落的结构也不 相同．分析结果能较好地反映不同林分和不同层次的节肢动物群落结构的差异性与连续 性．模糊聚类的分析方法优于系统聚类．     

复方配伍的摄动模糊聚类方法

利用摄动模糊聚类方法对中药复方桂枝汤的药群进行了分类,结果表明该方法优于传统模糊聚类方法,避免了利用传递闭包求模糊等价矩阵进行分类的失真问题,与中药传统组方原则相吻合。     

小麦抗旱生态分类中适合性聚类方法的研究

探索了适合于小麦品种抗旱生态分类的聚类方法。选用２１个农艺性状和１５个冬小麦品种（系）,在聚类分析的各环节上,通过采用不同的策略,大规模进行了各种分类结果的比较。结果表明,在与专家经验分类接近程度上,数据转换方法中,原始数据法依次大于普通相关阵基础上的方差极大正交旋转法、Ｐｒｏｍａｘ斜交旋转法、主成份法;相似性度量上,欧氏距离大于马氏距离;聚类方式上,对应分析法和模糊聚类法大于最短距离法、最长距离     

改进的K—mean聚类算法在基因系统发育谱分析中的应用

简要介绍了系统发育谱法的原理,着重阐述了K—mean聚类算法在对基因系统发育谱分析中的改进,并与传统的K—mean聚类算法进行比较。实验结果表明,改进的K—mean聚类算法在运用系统发育谱法进行基因功能注释上是快而有效的,可以快速收敛到近似最优解。     

我国草兔的聚类研究

本文用新的聚类方法, 对中国草兔进行系统聚类。     

对似近分析的一点改进及其拓广

本文给出了似近分析一个重要的改进,并将改进的似近分析推广为m维似近分析,同时给出了两个数学定理及其证明。根据多维似近系数的数学性质,提出了一种新的聚类方法,即逐步多维聚类法。实例分析证明,这种聚类法比二维系数聚类优越,聚类功能强,所获得的结果更为客观。     

群落有序样本的一种聚类分析方法

目前,适用于群落聚类分析有序样本的聚类方法主要是“最优分割法”。本文提出了一种新的适用于群落有序样本的聚类方法,该法较最优分割法计算简便。     

光谱技术研究固氮酶铁硫簇的理化特性

Ｎ－甲基甲酰胺碱度是提取高质量固氮酶铁钼辅基的关键因素之一。过量的亚甲蓝能氧化并分解铁铜铺基为含双相铁硫簇和铁硫簇固氮酶铁钼辅基和在紫外可见光谱区中均无特征吸收峰,而在３２０ｎｍ处却呈弱吸收峰,棕色固氮菌固氮酶和该菌的突变菌侏ＵＷ４５固氮酶（缺铁钼辅基）中的非含钼的铁硫簇在紫外可见光谱区３２０ｎｍ和４０５ｎｍ处均含有特征吸收峰．     

基因芯片数据分析过程:从原始数据到生物学意义

基因芯片实验要得到可靠的生物学结论,必须基于优化的实验设计和科学的数据分析。讨论了与基因芯片数据分析方法相关的实验设计方面的几个问题,简述了差异表达分析、聚类分析及功能富集分析等分析方法及其进展,并介绍了部分软件及应用。     

假丝酵母属的聚类分析和代谢活性的研究

本文对35株假丝酵母测定了84项性状,用不同的聚类方法进行了聚类,结果以Ssm系数的UPGMA方式处理较合适。根据UPGMA聚类结果,这些菌株主要分成两大类群。对代谢活性(生理生化性状的阳性百分率,MA%)分析的结果发现,第一类群的代谢活性在40%至73%之间,而第二类群在9%至33%之间,类群间代谢活性有明显差异。实验结果还表明,某些传统鉴定方法中的鉴定指标(如硝酸盐同化等)不能代表类群的特征,而另外一些指标(如MA%,海藻糖、松三糖和麦芽糖的同化等)具有类群的特征性。虫生假丝酵母(C.insectorum)和穿凿假丝酵母(C.terebra)的相似性系数高达90%,这两个种应该合并为一种。     

The multivariate analysis of dermatoglyphics in population systematics

In this paper a multivariate analysis of dermatoglyphics was carried out. The following traits were used: pattern intensity on fingers, average of main line D terminations and frequencies of patterns in the five configurational areas of the palm. Firstly a cluster analysis is produced and completed with a principal components analysis. The results of both studies show clear agreement. The main conclusion is: the multivariate analysis of dermatoglyphics displays several groups of populations, which with the peculiarities pointed out in the paper, are in concordance with the classical racial stocks.     

Power calculation for cross-sectional stepped wedge cluster randomized trials with variable cluster sizes

Standard sample size calculation formulas for stepped wedge cluster randomized trials (SW-CRTs) assume that cluster sizes are equal. When cluster sizes vary substantially, ignoring this variation may lead to an under-powered study. We investigate the relative efficiency of a SW-CRT with varying cluster sizes to equal cluster sizes, and derive variance estimators for the intervention effect that account for this variation under a mixed effects model—a commonly used approach for analyzing data from cluster randomized trials. When cluster sizes vary, the power of a SW-CRT depends on the order in which clusters receive the intervention, which is determined through randomization. We first derive a variance formula that corresponds to any particular realization of the randomized sequence and propose efficient algorithms to identify upper and lower bounds of the power. We then obtain an “expected” power based on a first-order approximation to the variance formula, where the expectation is taken with respect to all possible randomization sequences. Finally, we provide a variance formula for more general settings where only the cluster size arithmetic mean and coefficient of variation, instead of exact cluster sizes, are known in the design stage. We evaluate our methods through simulations and illustrate that the average power of a SW-CRT decreases as the variation in cluster sizes increases, and the impact is largest when the number of clusters is small.     

Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross‐sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.     

基于表型性状的芍药不同品种群亲缘关系分析

芍药品种变异丰富,表型性状的研究对各品种进行科学分类及亲缘关系的研究具有重要意义。本研究以芍药DUS测试指南(2012版)为依据,以不同品种群的47个芍药品种为研究对象,选取了株高、分枝能力、茎秆颜色等33个表型性状(二元性状9个,数量多态性状3个,定性多态性状21个)进行了R型聚类分析、主成分分析和Q型聚类分析。结果表明:基于33个表型性状的聚类结果可以较好地区分芍药不同品种群的品种,其中国内品种与大多数国外品种亲缘关系较远;杂种芍药品种群的品种由于多亲本参与,起源复杂,聚类结果比较分散;伊藤品种群与杂种芍药品种群存在较近亲缘关系。本研究与前人利用形态和分子标记进行品种分类的结果基本一致,能够较准确反映品种间、品种群间的亲缘关系。     

一种基于类均值的肿瘤基因芯片数据的标准化方法

：分析了当前常用的标准化方法在肿瘤基因芯片中引起错误分类的原因,提出了一种基于类均值的标准化方法．该方法对基因表达谱进行双向标准化,并将标准化过程与聚类过程相互缠绕,利用聚类结果来修正参照表达水平．选取了5组肿瘤基因芯片数据,用层次聚类和K-均值聚类算法在不同的方差水平上分别对常用的标准化和基于类均值的标准化处理后的基因表达数据进行聚类分析比较．实验结果表明,基于类均值的标准化方法能有效提高肿瘤基因表达谱聚类结果的质量．     

Morphometrically-distinct sperm subpopulations defined by a multistep statistical procedure in ram ejaculates: intra- and interindividual variation

The existence of sperm subpopulations within the mammalian ejaculate has now been widely recognized. However, to the best of our knowledge, no data exist regarding the existence of sperm morphometric subpopulations within the ovine ejaculate. Computer assisted sperm morphometry analysis (ASMA) data and clustering methods were used in this study to identify sperm-head subpopulations in ram semen. Two experiments were carried out. In Experiment 1, ejaculates from 226 mature rams of the Manchega breed belonging to 36 different herds were used. A minimum of 100 sperm heads were analyzed from each male and eight morphometric characteristics for each individual sperm were recorded. Subpopulation analysis was performed in sequential steps: variable group analysis and correlation analysis to select which morphometric characteristics to use in cluster analyses; nonhierarchical clustering analysis using sperm head length and p2a (also known as roundness) shape factor as initial classificatory variables; and hierarchical clustering analysis to obtain the final number of clusters. The clustering analyses, based on 26 306 individual cells, revealed the existence of four sperm subpopulations (SP1, SP2, SP3 and SP4) with different morphometric characteristics. Significant differences in the proportion of spermatozoa in the SP1 and SP3 were found between rams belonging to different herds. In Experiment 2, the intra- and intermale variability on the distribution of sperm subpopulations was assessed. Three ejaculates from each of 21 rams were collected and the same multistep clustering analysis was performed. For all subpopulations defined, the intermale variability resulted in high values, being the intramale variability much lower. This fact would allow the use of sperm head morphometry to characterize a male and might provide valuable information to asses its fertility. In conclusion, our results show that using computer assisted sperm morphometry analysis and multivariate cluster analyses, four sperm subpopulations with different head phenotype were identified in ram ejaculates.