夜蛾科(鳞翅目)卵分类性状的定量分析

本文用主成分分析法对夜蛾科卵的分类性状进行定量分析,用以说明各主成分的生物含义和各变量对分类的重要性．     

中国海菜花属植物的性状分析

本文对中国海菜花属全部已知分类群的84个形态学、生物学和生态学性状,进行了R分析和主成分分析。R分析结果表明,被研究的性状可明显地划分为若干组高度相关的性状,它们或表明本属向次生性陆生和异花传粉方向演化,或具重要的分类价值。主成分分析结果表明,仅前16个主成分几乎可保留84个性状的全部信息量,这说明在中国海菜花属分类研究中所选性状很合理;前3个主成分可保留总信息量的76.56%,说明在三维空间内能较好地反映中国海菜花属所有已知分类群间的相对位置。     

大蒜品种生态型性状指标分析

在用１２个生态性状对大蒜生态型进行数量分类的基础上,采用主成分分析和判别分析的方法,分析了区分不同生态型的性状指标,主成分分析表明,越冬期叶片生长量。叶片受冻比率和秋,春播叶片数差比（Ｘ９）在各生态型中比较稳定,可做为区分各生态型的代表性状,判别分析筛选出抽薹指数和Ｘ９为判别各生态型的性状,用这２个性状建立的分类函数回判后,判准率为９３．２％,研究结果还表明Ｘ９在生型型分类中有重要意义。     

金翅夜蛾亚科分类系统的讨论

金翅夜蛾亚科Plusiinae是夜蛾科中在形态、生物学与生理生态都比较特殊而稳定的一个类群,无论成虫或幼虫很容易和其他亚科区分开来。但属种间的区别在外形上很不显著,极易混淆。而属种的分类位置,文献中又为混乱。它们在农业上的关系则愈来愈显得重要。     

我国微茎类吸虫研究:ⅩⅢ.微茎类吸虫成虫结构特征排序

对我国52种微茎类吸虫的18项成虫形态学特征进行主成分分析,结果表明:卵巢位置、子宫延伸位置等7项性状对第一主成分贡献较大,提示描述器官位置的指标是重要的分类依据。52个虫种在前三个主成分上的排序图显示应将其划分成4个亚科。     

人类群体遗传结构的协方差阵主成分分析方法

目的:探讨基因频率矩阵的中心化(或均值化)协方差阵主成分分析方法在人类群体遗传结构研究中的适用性和合理性。方法:从基因频率矩阵的结构特征入手,分析中心化、均值化协方差阵主成分分析与标准化相关阵主成分分析在特征根、特征向量以及降维效果等方面的差异,并通过实例比较不同方法在解释群体遗传结构特征上合理性。结果:中心化(或均值化)协方差阵的主成分不仅反映了基因变异程度的“方差信息量权”,而且反映了基因间相互影响程度的“相关信息量权”;标准化相关阵的主成分反映的仅是“相关信息量权”,不包括“方差信息量权”。通过比较中国26个汉族人群HLA-A基因座中心化协方差阵和标准化相关阵2种主成分分析结果,证实中心化协方差阵主成分分析方法在特征根与特征向量、保留主成分的个数和对主成分的群体遗传学解释的合理性等方面均优于标准化相关阵主成分分析方法。结论:在对群体遗传结构进行主成分分析时,应使用中心化(或均值化)变换消除基因频率矩阵中量级的影响,然后在用其协方差阵提取主成分。     

基于表型性状的孔雀草种质遗传多样性分析

为了了解孔雀草种质间的亲缘关系,提高孔雀草种质的利用效率,从18个形态特征对40份孔雀草种质进行了遗传变异分析、主成分分析及聚类分析。结果表明,孔雀草表型多样性丰富,种质间表型性状变异程度高,变异系数(CV值)范围3.92%~46.25%,以花朵数最大,其次是冠幅和株高;Shannon-Weaver多样性指数则以叶片性状及花性状较高,平均2.0以上。国内种质比国外种质多样性更丰富。通过主成分分析,筛选出对总体方差累计贡献率达78.949%的4个主成分,并筛选出综合性状表现良好的种质10份。参试的40份种质在欧氏距离阈值为7.29处可分为两大类,一类植株生长势较强包含2份种质(水星黄色和橙色);其余38份种质为一类,在欧氏距离阈值为6.0处,又以花色差异可进一步划分为2个类群,说明生长势及花色可作为孔雀草种质依据表型性状分类的重要指标。     

基于两种主成分分析方法分析石斛化学成分差异

探索相关性矩阵和协方差矩阵两种算法的主成分分析方法的差异。本文采用两种主成分分析方法分别计算色谱技术检测得到的石斛化学成分并进行聚类讨论。结果表明在16种石斛植物中得到的11种化学成分含量差异明显,通过相关性矩阵和协方差矩阵两种算法提取得到的主成分及其次序有一定差异,结合聚类分析表明两种聚类结果也存在差异。因此,在对近缘种植物天然产物成分进行差异分析或化学成分聚类,甚至在药材类真伪品鉴别的研究中,建立高效、精准的检测手段与选择适当的数据分析方法同等重要。     

禾本科植物幼苗性状的聚类分析

本文用201种禾本科植物幼苗的60个形态和解剖性状进行了聚类分析,将禾本科植物分为四个大类(一级聚合单位)—狐茅类(Festucoid)、黍类(Panicoid),竹类(Bambusoid)和画眉草类(Eragrostidoid)。这四类基本上与分类系统中的亚科相吻合,只是芦竹亚科植物的幼苗分散聚到了其它类中。推测芦竹亚科是一个比较原始的类群,狐茅亚科、黍亚科和画眉草亚科可能是在芦竹亚科的基础上进化而来的。 除个别种外,大多数同属的种在较低的聚合水平上聚在一起,超族在亚聚合单位中基本上能够反映出来。 研究证明禾本科植物的幼苗性状在禾本科植物分类中具有重要的分类学意义。     

多个主成分性状的综合优化研究

考虑育种目标的要求,对多个独立的主成分性状进行综合优化,建立了综合主成分性状的数学模型．综合主成分性状是S个表型方差（遗传方差）最大的主成分性状在育种目标约束下表型方差（或遗传方差）极大化下的线性组合,作为育种指标,选择效果好,预见性强．     

光皮树不同无性系主要农艺性状多样性分析

依据光皮树(Cornus wilsoniana)丰产育种目标,对7个光皮树优良无性系的28个农艺性状进行观测,并进行方差、主成分、聚类及相关性分析。方差分析结果显示28个农艺性状的变异系数为804%~9430%,其中叶形态（叶形、叶色、叶缘）的变异系数最高;表型分化系数为4527%~9640%,即各性状的遗传较为稳定;主成分分析结果显示花序、果实、含油率以及果穗果数因子是主要的遗传因子,是光皮树无性系性状遗传的主要来源;依据主成分因子得分将7个光皮树优良无性系聚为4个类群,其中第Ⅱ类群与第Ⅲ类群杂交可以获得高产高含油率的目标资源。     

Development and phenotypic correlations: the evolution of tooth shape in Sorex araneus

Summary Do morphogenetic processes cause common patterns of phenotypic covariation, and do those patterns evolve over microevolutionary timescales? Evolution of molar shape variance–covariance (P) matrixes was studied in five populations of the common shrew, Sorex araneus. P matrix evolution was assessed using matrix correlation, matrix disparity, and common principal component analysis (CPCA). Significant changes in covariance structure were found among the populations, but the differences were small. A computer model was used to estimate the theoretical covariance introduced into the phenotype by developmental interactions. Molar developmental processes explained some of the covariance in the shrew samples, especially as measured by matrix correlation, but the proportion was relatively small. Developmental principal components (PCs) were only infrequently associable with common principal components. The results suggest that molar shape P matrixes can evolve quickly in a manner only loosely constrained by development, and that their shared covariance is probably dominated by factors more proximate than development. Rarefaction showed that sample size severely affected P comparisons when n < 15 for matrix correlation and disparity, and when n < 30 for CPCA. Among CPCA evaluation criteria, Akaike Information Criterion performed better than jump‐up at n < 30, but worse at n > 30.     

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits'' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.     

Characters: the central mystery of taxonomy and systematics

Taxonomic and systematic theory is hopelessly confused because the term character has nine different, previously confused, meanings. After a historical analysis, it is shown that some form pairs, one used in taxonomy (= operational identification of phenetic patterns of character x individual spread) and the other in systematics (= theoretical analysis of patterns of taxonomy). On the basis of a stratigramy model, names are given to each usage and are defined for taxonomy, then systematics, as necessary: component : (tax.) a defined bit-or-piece of one individual (no syst. meaning); homology : (tax.) conceptual identity of components of several individuals, attributable (syst.) to common ancestry; homology avatar : (tax.) case of recognized homology which (syst.) shows broad phylogenetic continuity (e.g. eye) (= character sensu Sokal and Sneath); homolostratum/homology state : (tax.) specified condition of a homology avatar whose distribution (syst.) enables cladogenetic happenings to be identified (e.g. colour:red/green/blue/etc.) (= character state of Sokal and Sneath); character sensu stricto : (tax.) homolostratum limited to a taxon which (syst.), with hierarchy, identifies chronological sequences of most cladogenetic happenings; taxonomoids : (tax.) mixed group of homolostrata, including yet unknown characters, that identifies a taxon and so (syst.) has same role as characters (= roughly symplesiomorphies); Ante- (Ah) and Post-(Ph) happening characters : (tax.) the hierarchy levels immediately above and below an empty level which (syst.) reveal a cladistic happening (= roughly one usage of synapomorphies and apomorphies).     

MORPHOLOGICAL EVOLUTION IN MUROID RODENTS I. CONSERVATIVE PATTERNS OF CRANIOMETRIC COVARIANCE AND THEIR ONTOGENETIC BASIS IN THE NEOTROPICAL GENUS ZYGODONTOMYS

Analyses of craniodental measurement data from 15 wild-collected population samples of the Neotropical muroid rodent genus Zygodontomys reveal consistent patterns of relative variability and correlation that suggest a common latent structure. Eigenanalysis of each sample covariance matrix of logarithms yields a first principal component that accounts for a large fraction of the total variance. Variances of subsequent sample principal components are much smaller, and the results of bootstrap resampling together with asymptotic statistics suggest that characteristic roots of the covariance matrix after the first are seldom distinct. The coefficients of normalized first principal components are strikingly similar from sample to sample: inner products of these vectors reveal an average between-sample correlation of 0.989, and the mean angle of divergence is only about eight degrees. Since first principal component coefficients identify the same contrasts among variables as comparisons of relative variability and correlation, we conclude that a single factor accounts for most of the common latent determination of these sample dispersions. Analyses of variance based on toothwear (a coarse index of age) and sex in the wild-collected samples, and on known age and sex in a captive-bred population, reveal that specimen scores on sample first principal components are age- and sex-dependent; residual sample dispersion, however, is essentially unaffected by age, sex, or age × sex interaction. The sample first principal component therefore reflects the covariance among measured dimensions induced by general growth, and its coefficients are interpretable as exponents of postnatal growth allometry. Path-analytic models that incorporate prior knowledge of the equivalent allometric effects of general growth within these samples can be used to decompose the between-sample variance by factors corresponding to other ontogenetic mechanisms of form change. The genetic or environmental determinants of differences in sample mean phenotypes induced by such mechanisms, however, can be demonstrated only by experiment.     

中国薹草属黑穗薹草组的数量分类研究

对中国薹草属黑穗薹草组Sect.Racemosae及外类群冻原薹草组Sect.Frigidae共20种3变种采用38个形态特征进行了数量分类研究。聚类分析结果表明,黑穗薹草组应是一个自然分类群;支持了《中国植物志》将两对相似种：乌拉草（Carer meyeriana）与红原薹草（Carer hongyuanensis）、膨囊薹草（Carex lehmanii）与五台山薹草（Carex montis-wutaii）分别处理为种的观点。主成分分析结果表明,本组的分类特征较为稳定与集中,本组主成分分析散点图与聚类分析的分类结果大致吻合,并表明有关小坚果、果囊、鳞片、主茎叶和苞片的性状在本组的分类中起到了主要作用。     

An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree

Variance component (VC) approaches based on restricted maximum likelihood (REML) have been used as an attractive method for positioning of quantitative trait loci (QTL). Linkage disequilibrium (LD) information can be easily implemented in the covariance structure among QTL effects (e.g. genotype relationship matrix) and mapping resolution appears to be high. Because of the use of LD information, the covariance structure becomes much richer and denser compared to the use of linkage information alone. This makes an average information (AI) REML algorithm based on mixed model equations and sparse matrix techniques less useful. In addition, (near-) singularity problems often occur with high marker densities, which is common in fine-mapping, causing numerical problems in AIREML based on mixed model equations. The present study investigates the direct use of the variance covariance matrix of all observations in AIREML for LD mapping with a general complex pedigree. The method presented is more efficient than the usual approach based on mixed model equations and robust to numerical problems caused by near-singularity due to closely linked markers. It is also feasible to fit multiple QTL simultaneously in the proposed method whereas this would drastically increase computing time when using mixed model equation-based methods.     

Analysis of human collagen sequences

The extracellular matrix is fast emerging as important component mediating cell-cell interactions, along with its established role as a scaffold for cell support. Collagen, being the principal component of extracellular matrix, has been implicated in a number of pathological conditions. However, collagens are complex protein structures belonging to a large family consisting of 28 members in humans; hence, there exists a lack of in depth information about their structural features. Annotating and appreciating the functions of these proteins is possible with the help of the numerous biocomputational tools that are currently available. This study reports a comparative analysis and characterization of the alpha-1 chain of human collagen sequences. Physico-chemical, secondary structural, functional and phylogenetic classification was carried out, based on which, collagens 12, 14 and 20, which belong to the FACIT collagen family, have been identified as potential players in diseased conditions, owing to certain atypical properties such as very high aliphatic index, low percentage of glycine and proline residues and their proximity in evolutionary history. These collagen molecules might be important candidates to be investigated further for their role in skeletal disorders.     

Predicting the QRS complex and detecting small changes using principal component analysis.

In this paper, a new method for QRS complex analysis and estimation based on principal component analysis (PCA) and polynomial fitting techniques is presented. Multi-channel ECG signals were recorded and QRS complexes were obtained from every channel and aligned perfectly in matrices. For every channel, the covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Then the corresponding eigenvectors and eigenvalues were calculated and reconstruction parameter vectors were computed by expansion of every beat in terms of the principal eigenvectors. These parameter vectors show short-term fluctuations that have to be discriminated from abrupt changes or long-term trends that might indicate diseases. For this purpose, first-order poly-fit methods were applied to the elements of the reconstruction parameter vectors. In healthy volunteers, subsequent QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured. Based on this work, thresholds can be defined for changes in the parameter vectors that indicate diseases.