Principal axis analysis of dental attrition data

The principal axis method is an age independent analysis method for dental attrition data which avoids many problems associated with earlier methods of analysis, correlation and regression. A principal axis equation is determined from the scatter of M1 on X and M2 on Y, and the slope of the equation can be used to indicate rate of wear. High slopes indicate rapid rates of wear. Because rate of wear rather than degree of wear is the parameter of interest, the procedure is age independent. Confidence regions can be calculated to test the distinctness of the slopes. Dental data from three Amerind skeletal samples, Indian Knoll, Hardin Site and Campbell Site, are used to illustrate the technique. Because least squares fits such as the principal axis solution are strongly influenced by the magnitude of variance in the data, two different methods of ordinal data collection are used in this test: Molnar's 1–8 and Scott's 4–40 scales. The Scott scoring system provides more satisfactory results when used in the principal axis analysis technique because of smaller confidence regions. The testing of the technique on other samples is urged.     

Principal components for allometric analysis

Logarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables. Interpretation is logically limited to the variables included in analysis. Principal components analysis depends also on relatively uniform intercorrelations; given this, it serves satisfactorily as a method for summarizing many bivariate combinations. Unmodified major principal component coefficients cannot represent scaling to body weight; rather, they represent scaling to a composite size vector which usually is highly correlated with body size or weight but has an unspecified allometry. Thus, the concepts of proportionality and of isometry must be kept distinct.     

Principal component analysis of DNA oligonucleotide structural data

The microstructure of a DNA helix is characterized by several base pair and base step parameters such as twist, rise, roll, propeller twist, etc., in addition to conformational parameters such as the backbone and the glycosidic torsion angles. Among these only a few, which are independent of all others and of each other, may be used to precisely characterize the helix. The problem however is to identify these independent parameters. We have used principal component analysis to identify a relatively small set of independent parameters, with which to characterize each DNA helix. We show that these principal components clearly discriminate between A and B DNA helical types. The calculations further suggest that the microstructure of a DNA helix is better characterized using dinucleotides.     

Principal component analysis of gene frequencies of Chinese populations

Principal components (PCs) were calculated based on gene frequencies of 130 alleles at 38 loci in Chinese populations, and geographic PC maps were constructed. The first PC map of the Han shows the genetic difference between Southern and Northern Mongoloids, while the second PC indicates the gene flow between Caucasoid and Mongoloids. The first PC map of the Chinese ethnic minorities is similar to that of the second PC map of the Han, while their second PC map is similar to the first PC map of the Han. When calculating PC with the gene frequency data from both the Han and ethnic minorities, the first and second PC maps most resemble those of the ethnic minorities alone. The third and fourth PC maps of Chinese populations may reflect historical events that allowed the expansion of the populations in the highly civilized regions. A clear-cut boundary between Southern and Northern Mongoloids in the synthetic map of the Chinese populations was observed in the zone of the Yangtze River. We suggest that the ancestors of Southern and Northern Mongoloids had already separated before reaching Asia. The ancestors of the Southern Mongoloids may result from the initial expansion from Africa or the Middle East, via the south coast of Asia, toward Southeast Asia, and ultimately South China. Upon reaching the Yangtze River, they might even have crossed the river to occupy the nearby regions for a period of time. The ancestors of the Northern Mongoloids probably expanded from Africa via the Northern Pamirs, first went eastward, then towards the south to reach the Yangtze River. The expansion of the Northern Mongoloids toward the south of the Yangtze River happened only in the last 2 or 3 thousand years.     

Principal axis analysis of dental attrition data from two Australian Aboriginal populations

Dental attrition scores from two distinct Australian Aboriginal populations were compared by principal axis analysis. The first group was composed of members of the Narrinyeri group who occupied the river basin and mouth of the River Murray. The second group consisted of members of the Kaurna tribe who occupied the coastal plain to the west of the Narrinyeri. The groups were isolated both physically and culturally and as a result differences in patterns of tooth wear might be expected. In the Narrinyeri sample, attrition tended to be more rapid in females for all but the anterior teeth. The only sex difference in the Kaurna was for the maxillary central incisors, which wore more rapidly in females. Interpopulation differences in the pattern of tooth wear were also evident. The incisors, canines, and premolars of Kaurna subjects tended to wear more rapidly, while the rate of posterior tooth wear tended to be greater in the Narrinyeri. The application of the principal axis method to quantitative attrition data provided a sensitive, objective evaluation of tooth wear. The reasons for the observed differences between the two groups are still not completely clear but may relect both functional and morphological differences between groups.     

Principal component analysis for clustering gene expression data

MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.     

Principal component analysis of gene frequencies and the origin of Basques

The genetic peculiarity of the Basque population has long been noted. We aim to describe Basque distinctiveness in space and assess the internal Basque heterogeneity. All these aspects are relevant to the question of the origin of Basques. After a thorough literature search, a data base was created containing all the available data on gene frequencies in the Iberian Peninsula and France. Twenty-nine systems, comprising 71 alleles, were used to carry out a principal component (PC) analysis. The results show a sharp peak in the first PC in the Basque area, which remains even when the geographic scope is widened to include western Europe. As demonstrated by “wombling” analysis, the steeper slope in the first PC is found to the east of the Basque area, along the Pyrenees. Measures of genetic heterogeneity (such as F_ST values) within the Basque country, as compared to those for non-Basques, do not show a particular internal substructuration in the Basque population. The genetic results support a scenario in which the Basques are the product of in situ differentiation around the time of the Last Glacial Maximum (18,000 B .P .), in agreement with archaeological and linguistic data. Isolation from the surrounding populations has allowed the differentiation to last for millennia, but has erased the differences existing among Basques. © 1994 Wiley-Liss, Inc.     

Taxonomic discrimination of flowering plants by multivariate analysis of Fourier transform infrared spectroscopy data

Fourier transform infrared spectroscopy (FTIR) provides biochemical profiles containing overlapping signals from a majority of the compounds that are present when whole cells are analyzed. Leaf samples of seven higher plant species and varieties were subjected to FTIR to determine whether plants can be discriminated phylogenetically on the basis of biochemical profiles. A hierarchical dendrogram based on principal component analysis (PCA) of FTIR data showed relationships between plants that were in agreement with known plant taxonomy. Genetic programming (GP) analysis determined the top three to five biomarkers from FTIR data that discriminated plants at each hierarchical level of the dendrogram. Most biomarkers determined by GP analysis at each hierarchical level were specific to the carbohydrate fingerprint region (1,200–800 cm^–1) of the FTIR spectrum. Our results indicate that differences in cell-wall composition and structure can provide the basis for chemotaxonomy of flowering plants.Abbreviations FTIR Fourier transform infrared spectroscopy - GP Genetic programming - PCA Principal component analysis - PyMS Pyrolysis mass spectrometry     

An analysis of macaque systematics using gene frequency data

Macaque gene frequencies for seven polymorphic protein systems are employed to generate dendrograms via two algorithms. The frequency data employed are drawn from 14 populations of macaques representing nine species. The two algorithms are the unweighted pair group method and an iterative program based on the additive hypothesis. The topologies generated by the two approaches are quite similar. The one major difference, involving M. fuscata, is investigated in detail.As measured by our index of dissimilarity (ID) conspecific populations of M. nemestrina, M. mulatta and M. fascicularis are closely related. Often the ID values separating conspecific groups are in agreement with expectations based on geographic considerations. Interspecific comparisons involving M. mulatta and M. fascicularis also exhibit clinal variation.M. cyclopis and M. fuscata show a particularly strong relationship to M. mulatta. This cluster of three species is closely related to M. fascicularis. M. speciosa, on the other hand, appears to be the most divergent of the species analyzed. The results are considered in light of previous thoughts about macaque systematics based on morphological characteristics.     

中国人群遗传结构分析

本文根据红细胞血型基因频率,用Harpending和Jenkins(1973)方法计算了中国22个人群间的遗传距离,同时在国内首次运用主坐标分析及其排序方法展示了中华民族的遗传结构,反映出中国东西人群与南北人群间的基因流。     

Principal component analysis of complex multijoint coordinative movements

Principal components analysis (PCA) has not been very much in vogue within the field of movement coordination even though it is useful to reduce data dimensionality and to reveal underlying data structures. Traditionally, studies of coordination between two joints have predominantly made use of relative phase analyses. This has resulted in the identification of principal constraints that govern the Central Nervous System’s organization and the control of coordination patterns. However, relative phase analyses on pairwise joints have some drawbacks because they are not optimal for revealing convergent patterns among multijoint coordination modes and for unraveling generic control strategies.In this paper, we present a method to analyze multijoint coordination based on the properties of PC, more specifically the eigenvalues and eigenvectors of the covariance matrix.The comparison between relative phase analysis and PCA shows that both provide similar and consistent results, underscoring the latter technique’s sensitivity to the study of coordination performance. In addition, it provides a method for automatic pattern detection as well as an index of performance for each joint within the context of the global coordination pattern.Finally, the merit of the PCA technique within the context of central pattern generators (CPG) will be discussed.     

Principal components analysis of physical growth in savannah baboons

Morphometric data collected from 118 male and 169 female savannah baboons (Papio cynocephalus anubis) aged between birth and 5.5 years were analyzed to describe the morphology and physical growth of this species. Measurements included weight, crown-rump length, triceps circumference, and skinfolds at the neck, subscapular, suprailiac, and triceps anatomical sites. Principal components analyses were applied to the data to provide multivariate assessments of morphological patterning among the variables. These analyses resulted in the extraction of two unrotated orthogonal components that accounted for 88% of the overall sample variation. The first component accounted for 77% of the variation and represents an axis of overall body size. The second component represents an axis of shape variation that contrasts body size with fat patterning, and was interpreted as a measure of body leanness. Individual component scores were computed for determining age, gender, and age-by-gender interaction effects. Both components were found to be age dependent for both genders. Males and females shared similar age patterning along the two components; however, gender differences did occur in patterning along the two components; however, gender differences did occur in respect to leanness. The multivariate measure of overall body size increased for both genders similarly with advancing age. Age patterning along the leanness component was described as a decrease from birth to 1 year, followed by an increase in leanness in older ages. Females had a delayed and significantly less intense increase in leanness relative to males.     

Correspondence analysis of HLA gene frequency data from 124 population samples.

Correspondence analysis, a variant of principal components analysis, is used to interpret and compare gene frequencies of the HLA system for 124 samples from different populations studied in the Fifth and Sixth International Histocompatibility Workshops. The major advantage of this analysis is that populations and HLA genes are represented simultaneously with respect to the same axes in multidimensional space. This permits the visual interpretation of genetic differences between populations, the relative participation of each gene in the dispersion, and the correspondence between populations and genes. By this method a clear separation of ethnic groups in the world is obtained. Furthermore, within Europe the separation of samples from different countries is concordant with their geographical distances. The HLA system appears sufficiently polymorphic to define a population by its gene frequencies.     

Comparison of three formal methods used to estimate the functional axis of rotation: an extensive in-vivo analysis performed on the knee joint

Estimating the main axis of rotation (AoR) of a human joint represents an important issue in biomechanics. This study compared three formal methods used to estimate functional AoR, namely a cylindrical fitting method, a mean helical axis transformation, and a symmetrical axis approach. These methods were tested on 106 subjects undergoing navigated total knee arthroplasty. AoR orientation in 3D and in the frontal and coronal planes provided by each method was compared to the transepicondylar axis direction. Although all the methods resulted effective, significant differences were identified among them, relatively to the orientation in 3D and in the frontal plane projection. This was probably due to the presence of secondary rotations during the first degrees of knee flexion.     

Genetic distance and gene diversity among ten endogamous groups in Chhattisgarh,Central India

A genetic survey has been conducted among the Rawats and Telis, two endogamous caste populations of Chhattisgarh, Central India. Using the gene frequency data for three genetic loci, genetic distance among ten population groups have been calculated. The gene differentiation among these population groups is only about 2 per cent.     

Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.