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Results for “Principal Component Analysis”

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PubMed2005

[Quantitative gait evaluation using principal component analysis].

Evaluation of human gait function is of great significance in clinical medicine and rehabilitation engineering. A quantitative gait evaluation method using principal component analysis was proposed. The evaluation steps included that a series of characteristic index was performed by the gait parameters with a gait detection, and the index was normalized, quantified and summarized by principal component analysis. Then the evaluation results were shown in formulation, figures and tables. The examples showed that this system could evaluate the recovery of the gait by treatment.

Evaluation Studies as Topic↗

PubMed1965

AUTOMATIC CLASSIFICATION OF STAPHYLOCOCCI BY PRINCIPAL-COMPONENT ANALYSIS AND A GRADIENT METHOD.

Hill, L. R. (Università Statale, Milano, Italy), L. G. Silvestri, P. Ihm, G. Farchi, and P. Lanciani. Automatic classification of staphylococci by principal-component analysis and a gradient method. J. Bacteriol. 89:1393-1401. 1965.-Forty-nine strains from the species Staphylococcus aureus, S. saprophyticus, S. lactis, S. afermentans, and S. roseus were submitted to different taxometric analyses; clustering was performed by single linkage, by the unweighted pair group method, and by principal-component analysis followed by a gradient method. Results were substantially the same with all methods. All S. aureus clustered together, sharply separated from S. roseus and S. afermentans; S. lactis and S. saprophyticus fell between, with the latter nearer to S. aureus. The main purpose of this study was to introduce a new taxometric technique, based on principal-component analysis followed by a gradient method, and to compare it with some other methods in current use. Advantages of the new method are complete automation and therefore greater objectivity, execution of the clustering in a space of reduced dimensions in which different characters have different weights, easy recognition of taxonomically important characters, and opportunity for representing clusters in three-dimensional models; the principal disadvantage is the need for large computer facilities.

Classification↗

PubMed2002

Principal component analysis of dissolution data with missing elements.

The use of principal component analysis (PCA) for incomplete dissolution data sets is examined. The PC space is constructed using a reference set and the test set is projected in that space. Several cases such as a reference set with missing data, an incomplete test set and both sets measured at different time points, are discussed using two examples: one simulation and one obtained from the pharmaceutical practice. From the many possibilities to deal with missing data, the expectation-maximization algorithm in combination with PCA was chosen. The influence on the similarity or f2 factor is examined too. The sampling with replacement or bootstrap technique, which can be used to obtain confidence limits, can also be used when missing data are present in one of the data sets.

Algorithms↗

PubMed2007

Principal components analysis of the photoresponse nonuniformity of a matrix detector.

The principal component analysis is used to identify and quantify spatial distributions of relative photoresponse as a function of the exposure time for a visible CCD array. The analysis shows a simple way to define an invariant photoresponse nonuniformity and compare it with the definition of this invariant pattern as the one obtained for long exposure times. Experimental data of radiant exposure from levels of irradiance obtained in a stable and well-controlled environment are used.

Journal Article↗

PubMed1983

Principal components analysis as a tool for the optimization of experimental conditions.

Principal Components Analysis is used to display the variation in a data set consisting of the free amino-acid patterns in serum from dogs as a function of fasting time. Inhomogeneities found in the data set resulted in an optimization of the experimental conditions. Moreover important observations concerning sampling time and biological variability could be made.

Amino Acids↗

PubMed2003

Event-related potentials in newborns with and without familial risk for dyslexia: principal component analysis reveals differences between the groups.

Differences revealed by factor scores extracted by principal component analysis (PCA) from event-related potential (ERP) data of newborns with and without familial risk for dyslexia were examined and compared to results obtained by using original averaged ERPs. ERPs to consonant-vowel syllables (synthetic /ba/, /da/, /ga/; and natural /paa/, /taa/, /kaa/) were recorded from 26 at-risk and 23 control 1-7 day-old infants. The stimuli were presented equiprobably and with interstimulus intervals varying at random from 3,910 to 7,285 ms. Statistically significant between-group differences were found to be relatively similar irrespective of the methods of analysis (original ERPs vs. factor scores from PCA). Responses to /ga/ differed from those to /ba/ and /da/ between the groups in the right hemisphere at the latencies of 50-170 ms (Factor 4) and 540-630 ms (Factor 3). The groups differed also in their responses to /da/ in the posterior electrode sites at 740-940 ms (Factor 2). There were no group differences in the natural stimulus set. These results demonstrate that brain activation differences may be implicated in risk for dyslexia immediately after birth. The results also show that the PCA-ANOVA procedure is an effective way of identifying the group-related variance in the ERP-data when the component structure, such as those of infants, is not well-known in advance.

Acoustic Stimulation↗

PubMed2005

Qualitative study of ethanol content in tequilas by Raman spectroscopy and principal component analysis.

Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.

Alcoholic Beverages↗

PubMed2005

Principal-components analysis of fluorescence cross-section spectra from pathogenic and simulant bacteria.

Principal-components analysis of a new set of highly resolved (< 1 nm) fluorescence cross-section spectra excited at 354.7 nm over the 370-646 nm band has been used to demonstrate the potential ability of UV standoff lidars to discriminate among particular biological warfare agents and simulants over short ranges. The remapped spectra produced by this technique from Bacillus globigii (Bg) and Bacillus anthracis (Ba) spores were sufficiently different to allow them to be cleanly separated, and the Ba spectra obtained from Sterne and Ames strain spores were distinguishable. These patterns persisted as the spectral resolution was subsequently degraded in processing from approximately 1 to 34 nm. This is to the author's knowledge the first time that resolved fluorescence spectra from biological warfare agents have been speciated or shown to be distinguishably different from those normally used surrogates by optical spectroscopy.

Algorithms↗

PubMed1992

Principal component analysis and exploratory factor analysis.

In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.

Animals↗

PubMed1987

Principal components analysis of therapeutic orientations of doctoral programs in clinical psychology.

A principal components analysis was conducted on a data set that consisted of ratings of therapeutic orientations reported by directors of clinical psychology training programs in 96 clinical psychology programs in the U.S. Two principal components emerged, which contrasted (1) behavioral vs. psychoanalytic approaches and (2) humanistic vs. conditioning approaches. A plotting of factor scores revealed relatively clear separation among programs primarily identified with either behavioral or psychoanalytic/humanistic approaches. The majority of training programs, however, clustered around the midpoint on both underlying factor dimensions, an indication of the adoption of multiple approaches in training.

Curriculum↗

PubMed2005

Identification of copper sources in urban surface waters using the principal component analysis based on aquatic parameters.

The goal of this work was to identify the sources of copper loads in surface urban waters using principal component analysis under the aquatic parameters data evaluation approach. Water samples from the Irai and Iguacu rivers were collected monthly during a 12-month period at two points located upstream and downstream of a metropolitan region. pH, total alkalinity, dissolved chloride, total suspended solids, dissolved organic matter, total recoverable copper, temperature, and precipitation data provided some reliable information concerning the characteristics and water quality of both rivers. Principal component analysis indicated seasonal and spatial effects on copper concentration and loads in both environments. During the rainy season, non-point sources such as urban run-off are believed to be the major source of copper in both cases. In contrast, during the lower precipitation period, the discharge of raw sewage seems to be the primary source of copper to the Iguacu River, which also exhibited higher total metal concentrations.

Cities↗

PubMed2004

Small interacting peptides. Part II: Interaction of cyclohexapeptides with immobilised model peptides. Comparison of infrared investigations, principal components analysis and force field calculations.

The interaction of cyclic peptides with surface-bound model peptides was investigated by ATR-FTIR spectroscopy, principal components analysis and force field calculations. Information about the interacting functional COOH, COO-, and NH3+ groups and the peptide backbone was gained through a set of cyclohexapeptides (seven of the type c(X1KX2KX3K) (K = L-lysine) and one of the type c(X1KX2KX3k) (k = D-lysine), which are interacting with L-arginine- or tripeptide-coated Si-ATR crystals. All measurements were performed in aqueous solutions. Spectra evaluation in the range 1800-1500 cm(-1) was done by band and principal components analysis (PCA). Only adsorbed molecules were present in these spectra. The coatings were investigated by ATR-FTIR spectroscopy too in order to characterise their functional groups. Based on this knowledge, the spectra of the interacting partners could be evaluated in relation to cyclohexapeptides and coatings. As a result, it was possible to identify the distinct differences in the bonding behaviour of the various peptides.

Amino Acid Sequence↗

PubMed2004

Principal component analysis is a powerful instrument in occupational hygiene inquiries.

Several investigators have successfully used principal component analysis (PCA) in interpreting occupational hygiene data. However, traditional textbooks in occupational hygiene provide no guidance for the application and interpretation of PCA. In this article I briefly review the basics of PCA (for those not statistically inclined), provide some guidelines for performing PCA (and designing studies that use the power of PCA), illustrate its application in understanding exposure to mixtures and the characterization of 'peak exposure', and highlight other benefits that occupational hygienists stand to gain by including PCA in their 'statistical toolkit'. I hope that this article will promote greater use and understanding of a data analysis approach that has long been helping investigators outside the field of occupational hygiene to unravel the structure behind the complex relationships among multiple correlated variables.

Humans↗

PubMed2005

Gene expression data classification with Kernel principal component analysis.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Journal Article↗

PubMed2006

Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.

The effectiveness of combining principal component analysis (PCA) with multi-linear regression (MLRA) and wind direction data was demonstrated in this study. PM data from three grain-size fractions from a highly industrialised area in Northern Spain were analysed. Seven independent PM sources were identified by PCA: steel (Pb, Zn, Cd, Mn) and pigment (Cr, Mo, Ni) manufacture, road dust (Fe, Ba, Cd), traffic exhaust (P, OC + EC), regional-scale transport (, , V), crustal contributions (Al2O3, Sr, K) and sea spray (Na, Cl). The spatial distribution of the sources was obtained by coupling PCA with wind direction data, which helped identify regional drainage flows as the main source of crustal material. The same analysis showed that the contribution of motorway traffic to PM10 levels is 4-5 microg m-3 higher than that of local traffic. The coupling of PCA-MLRA with wind direction data proved thus to be useful in extracting further information on source contributions and locations. Correct identification and characterisation of PM sources is essential for the design and application of effective abatement strategies.

Air Pollutants↗

PubMed1993

Functional connectivity: the principal-component analysis of large (PET) data sets.

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.

Algorithms↗

PubMed2000

Statistical analysis of mitochondrial pathologies in childhood: identification of deficiencies using principal component analysis.

Mitochondrial pathologies are a heterogeneous group of metabolic disorders that are frequently characterized by anomalies of oxidative phosphorylation, especially in the respiratory chain. The identification of these anomalies may involve many investigations, and biochemistry is a main tool. However, considering the whole set of biochemical data, the interpretation of the results by the traditionally used statistical methods remains complex and does not always lead to an unequivocal conclusion about the presence or absence of a respiratory chain defect. This arises from three main problems: (a) the absence of an a priori-defined control population, because the determination of the control values are derived from the whole set of investigated patients, (b) the small size of the population studied, (c) the large number of variables collected, each of which creates a wide variability. To cope with these problems, the principal component analysis (PCA) has been applied to the biochemical data obtained from 35 muscle biopsies of children suspected of having a mitochondrial disease. This analysis makes it possible for each respiratory chain complex to distinguish between different subsets within the whole population (normal, deficient, and, in between, borderline subgroups of patients) and to detect the most discriminating variables. PCA of the data of all complexes together showed that mitochondrial diseases in this population were mainly caused by multiple deficits in respiratory chain complexes. This analysis allows the definition of a new subgroup of newborns, which have high respiratory chain complex activity values. Our results show that the PCA method, which simultaneously takes into account all of the concerned variables, allows the separation of patients into subgroups, which may help clinicians make their diagnoses.

Adolescent↗

PubMed1987

Individualized principal component analysis of endocrine circannual variability.

The technique of principal component (PC) analysis (PCA) of multivariate observations is a method that allows dimension reduction of multivariate data for further analysis. It is here introduced as a means of selecting chronobiologically important variables that can be further studied by an analysis of variance. The use of PCA is illustrated for a study of major temporal sources of human endocrine variability. Contributions to temporal variability by seven steroidal and six nonsteroidal hormones are compared in samples available at 100-min intervals for 24 hr in three seasons for each of three clinically healthy individuals: an adolescent woman, a menstrually cycling woman, and a postmenopausal woman. On an individualized basis, it is ascertained that the first principal component, a new variable, is primarily determined by steroids and that PCA can single out variables displaying interseasonal (circannual) differences validated as statistically significant by a subsequent analysis of variance. The variables here scrutinized and identified as contributing to the PC, however, need not all differ with statistical significance along the scale of the seasons. The steroids contributing the first principal component are DHEA-S and an estrogen in all three individuals studied, cortisol and aldosterone in two of them, and 17-OH progesterone in one case.

Adolescent↗