Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Principal component”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources
PubMed1994

Epidemiologic analysis of spatial clustering of bovine ephemeral fever outbreaks. II. Principal component analysis.

The principal component analysis (PCA) was applied to analyze a correlation matrix of three variables on epidemic data of bovine ephemeral fever (BEF) outbreaks. These original data were summarized from the official outbreak report of Fukuoka Prefecture. The first and the second principal components of the PCA were interpreted as the infectious potency due to BEF virus and the prevention against BEF virus infection, respectively. The BEF outbreak areas were able to be classified epidemically into 4 groups by using the two principal components. The valuable epidemiological insights can be reasonably obtained from an application of the PCA. The results provided an important information for a further BEF vaccination campaign in the western part of Japan.

Animals↗
PubMed2002

[Research into simultaneous spectrophotometric determination of components in cough syrup by principal component regression method].

Principal component regression (PCR) method is used to analyse five components: acetaminophen, p-aminophenol, caffeine, chlorphenamine maleate and guaifenesin. The basic principle and the analytical step of the approach are described in detail. The computer program of LHG is based on VB language. The experimental result shows that the PCR method has no systematical error as compared to classical method. The experimental result shows that the average recovery of each component is all in the range from 96.43% to 107.14%. Each component obtains satisfactory result without any pre-separation. The approach is simple, rapid and suitable for the computer-aid analysis.

Acetaminophen↗
PubMed1987

Comparison of principal components computed with principal factor analysis on the basis of averaged and single-trial ERPs using the Fischer-Roppert procedure.

Averaged and single-trial event-related potentials (ERPs) were analysed using the Principal Factor Analysis (PFA) with following varimax rotation, and the results were compared. The correspondence between the matrices of factor loadings was tested by means of the Fischer-Roppert-procedure. For the application of PFA, a preceding averaging to improve the signal to noise ratio is not necessary. If the group of subjects is homogeneous enough, the analysis of the single-trial ERPs provides results sufficient to investigate the component structure of the ERP. The ERPs from all subjects can be described with one common component structure.

Arousal↗
PubMed1997

[Computer technology of genogeographic study of the gene pool. IV. Population in the space of principal components].

On the basis of maps of principal components ("synthetic maps"), populations were arranged in the space of principal components. In terms of the applied model, nodes of a dense, uniform grid represented human populations. For each node, the frequency of a given gene was interpolated from these values for all original populations. Principal components were estimated and mapped on the basis of maps for all genes. Each population (grid node) was assigned a marker of an ethnic or some other group of populations and was positioned in the space of principal components according to the values from the original maps. The resultant "ethnic clouds" of populations and "ethnic centroids" of principal components provide some new possibilities for explaining the patterns of changes in gene pools. The maps of reliability of principal components allow the researcher to eliminate the information on populations which is unreliable and turn to the "reliable" space of principal components. The method was tested with the use of the maps of principal components for the gene pool of the East European population. Eastern Slavonic (Russians, Ukrainians, and Belarussians) and western and eastern Finno-Ugrian (Estonians and Mordovians, respectively) ethnic groups were mapped to the space of principal components. The relative positions of the populations of these ethnic groups was analyzed in the spaces of the first and the second, the first and the third, and the second and the third principal components of the East European gene pool.

Commonwealth of Independent States↗
PubMed2003

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis.

Multiway principal component analysis (MPCA) for the analysis and monitoring of batch processes has recently been proposed. Although MPCA has found wide applications in batch process monitoring, it assumes that future batches behave in the same way as those used for model identification. In this study, a new monitoring algorithm, adaptive multiblock MPCA, is developed. The method overcomes the problem of changing process conditions by updating the covariance structure recursively. A historical set of operational data of a multiphase batch process was divided into local blocks in such a way that the variables from one phase of a batch run could be blocked in the corresponding blocks. This approach has significant benefits because the latent variable structure can change for each phase during the batch operation. The adaptive multiblock model also allows for easier fault detection and isolation by looking at the relationship between blocks and at smaller meaningful block models, and it therefore helps in the diagnosis of the disturbance. The proposed adaptive multiblock monitoring method is successfully applied to a sequencing batch reactor for biological wastewater treatment.

Algorithms↗
PubMed2003

Comparing G matrices: are common principal components informative?

Common principal components (CPC) analysis is a technique for assessing whether variance-covariance matrices from different populations have similar structure. One potential application is to compare additive genetic variance-covariance matrices, G. In this article, the conditions under which G matrices are expected to have common PCs are derived for a two-locus, two-allele model and the model of constrained pleiotropy. The theory demonstrates that whether G matrices are expected to have common PCs is largely determined by whether pleiotropic effects have a modular organization. If two (or more) populations have modules and these modules have the same direction, the G matrices have a common PC, regardless of allele frequencies. In the absence of modules, common PCs exist only for very restricted combinations of allele frequencies. Together, these two results imply that, when populations are evolving, common PCs are expected only when the populations have modules in common. These results have two implications: (1) In general, G matrices will not have common PCs, and (2) when they do, these PCs indicate common modular organization. The interpretation of common PCs identified for estimates of G matrices is discussed in light of these results.

Data Interpretation, Statistical↗
PubMedDate not supplied

Principal component analysis of nonlinear chromatography.

Principal component analysis (PCA) has been used for the modeling of nonlinear chromatography under overload conditions. A 10-fold range of crude erythromycin samples were loaded onto columns with different stationary-phase chemistries (2 polystyrene, 1 methacrylate) in direct proportion to the bed volumes. The elution profiles indicated slightly concave isotherms for the polystyrene supports and a convex Langmuirian isotherm for the methacrylic support used. The principal component models accounted for over 98% of the original variance in the data for all three columns and were able to give excellent models of complete chromatograms in the absence of first-principle models or physicochemical data. Correlations between sample mass and the principal component scores were made for each that were consistent for the column types despite the different geometries and stationary phases. Linear relationships with high correlation coefficients were observed when the scores of the same principal component were compared between columns. Such correlations offer considerable potential for modeling of nonlinear chromatography.

Chromatography↗
PubMed1995

EP component identification and measurement by principal components analysis.

Between the acquisition of Evoked Potential (EP) data and their interpretation lies a major problem: What to measure? An approach to this kind of problem is outlined here in terms of Principal Components Analysis (PCA). An important second theme is that experimental manipulation is important to functional interpretation. It would be desirable to have a system of EP measurement with the following characteristics: (1) represent the data in a concise, parsimonous way; (2) determine EP components from the data without assuming in advance any particular waveforms for the components; (3) extract components which are independent of each other; (4) measure the amounts (contributions) of various components in observed EPs; (5) use measures that have greater reliability than measures at any single time point or peak; and (6) identify and measure components that overlap in time. PCA has these desirable characteristics. Simulations are illustrated. PCA's beauty also has some warts that are discussed. In addition to discussing the usual two-mode model of PCA, an extension of PCA to a three-mode model is described that provides separate parameters for (1) waveforms over time, (2) coefficients for spatial distribution, and (3) scores telling the amount of each component in each EP. PCA is compared with more traditional approaches. Some biophysical considerations are briefly discussed. Choices to be made in applying PCA are considered. Other issues include misallocation of variance, overlapping components, validation, and latency changes.

Brain↗
PubMed1992

[Classification of congenital superior oblique palsy based on principal component analysis].

An attempt was to classify unilateral congenital superior oblique palsy principal component analysis. Each principal component was calculated by taking a linear combination of an eigenvector of the correlation matrix with a standardized original variable. The variables selected for the analysis were vertical deviation in the nine diagnostic positions of 51 cases measured by a synoptometer. The cumulative contributive percent of principal components showed that 88.5% of the variation were accounted for by the first three principal components. The first principal component accounted for 56.7% of the variation in samples indicating the extent of superior oblique palsy in which vertical deviation increases or decreases proportionately. The second principal component accounted for 20.6% of the variation of samples indicating the extent of the incomitance of vertical deviation with a vertical change of gaze. The third principal component accounted for 11.1% of the variation in the sample indicating the extent of the vertical deviation with a horizontal change of gaze.

Adolescent↗
PubMed1997

Topographical characteristics and principal component structure of the hypnagogic EEG.

The purpose of the present study was to identify the dominant topographic components of electroencephalographs (EEG) and their behavior during the waking-sleeping transition period. Somnography of nocturnal sleep was recorded on 10 male subjects. Each recording, from "lights-off" to 5 minutes after the appearance of the first sleep spindle, was analyzed. The typical EEG patterns during hypnagogic period were classified into nine EEG stages. Topographic maps demonstrated that the dominant areas of alpha-band activity moved from the posterior areas to anterior areas along the midline of the scalp. In delta-, theta-, and sigma-band activities, the differences of EEG amplitude between the focus areas (the dominant areas) and the surrounding areas increased as a function of EEG stage. To identify the dominant topographic components, a principal component analysis was carried out on a 12-channel EEG data set for each of six frequency bands. The dominant areas of alpha 2- (9.6-11.4 Hz) and alpha 3- (11.6-13.4 Hz) band activities moved from the posterior to anterior areas, respectively. The distribution of alpha 2-band activity on the scalp clearly changed just after EEG stage 3 (alpha intermittent, < 50%). On the other hand, alpha 3-band activity became dominant in anterior areas after the appearance of vertex sharp-wave bursts (EEG stage 7). For the sigma band, the amplitude of extensive areas from the frontal pole to the parietal showed a rapid rise after the onset of stage 7 (the appearance of vertex sharp-wave bursts). Based on the results, sleep onset process probably started before the onset of sleep stage 1 in standard criteria. On the other hand, the basic sleep process may start before the onset of sleep stage 2 or the manually scored spindles.

Adult↗
PubMed1992

Measurement processes and spatial principal components analysis.

Spatial principal components analysis (SPCA) applied to the ongoing EEG yields factor loadings which, when mapped, consistently reveal symmetrical patterns resembling the spherical harmonics. In this paper, we consider the mechanisms responsible for these characteristic patterns. In doing so, we demonstrate that volume conduction is one of a family of processes capable of generating such patterns with SPCA. It is shown that any series of measurements on a sphere in which the covariance is only a function of measurement site angular separation (shift invariant processes) will yield the spherical harmonics as the eigenvectors or factor loadings of the covariance matrix. Simulations further indicate that this effect is robust and not determined by the geometry of the measurement sites. In situations where shift invariant signals coexist with those generated at specific sites (anatomically specific processes), such as evoked potentials and some artifacts, it is shown that the anatomically specific signals do not influence the eigenvectors of the covariance matrix in a uniform or random fashion. The factors most influenced are those whose symmetry is similar to that of the site specific signal.

Brain↗
PubMed2000

Topography of clonidine-induced electroencephalographic changes evaluated by principal component analysis.

BACKGROUND: Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. METHODS: Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. RESULTS: Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. CONCLUSIONS: Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

Adult↗
PubMed2002

Interpretation of the results of common principal components analyses.

Common principal components (CPC) analysis is a new tool for the comparison of phenotypic and genetic variance-covariance matrices. CPC was developed as a method of data summarization, but frequently biologists would like to use the method to detect analogous patterns of trait correlation in multiple populations or species. To investigate the properties of CPC, we simulated data that reflect a set of causal factors. The CPC method performs as expected from a statistical point of view, but often gives results that are contrary to biological intuition. In general, CPC tends to underestimate the degree of structure that matrices share. Differences of trait variances and covariances due to a difference in a single causal factor in two otherwise identically structured datasets often cause CPC to declare the two datasets unrelated. Conversely, CPC could identify datasets as having the same structure when causal factors are different. Reordering of vectors before analysis can aid in the detection of patterns. We urge caution in the biological interpretation of CPC analysis results.

Analysis of Variance↗
PubMed1990

Comparing principal components analyses of evoked potentials recorded from heterogeneous groups of subjects.

Principal components analysis of evoked potentials differing between groups presents an interpretive problem, particularly in psychiatric research. Two sets of principal components and associated factor scores may appear to differ. The issue is to determine the extent to which visually differing principal components and resultant factor scores span the same factor space. Sets of evoked potentials from controls and from schizophrenics were each subjected to principal components analysis, from which factor score coefficients were computed for all subjects. This allowed determination of the extent to which (1) the two sets of basis waves were similar and (2) the factor scores resulting from the set of basis waves derived from principal components analysis of the control subjects' evoked potential data adequately represented those of the schizophrenics and vice-versa. Canonical correlation analyses indicated substantial similarities between the principal component structures (sets of basis waves). Multiple correlational analyses confirmed that the basis waves from either group spanned the other group's factor space. Factor scores from either set of basis waves were highly correlated. These results suggest that principal component structures derived from evoked potentials of a control group may be used in computing evoked potential factor scores of psychiatrically diverse populations even though the average evoked potentials of the groups may differ in several ways.

Evoked Potentials↗
PubMed1990

Multivariate data reduction by principal components, with application to neurological scoring instruments.

Principal components analysis is widely used as a practical tool for the analysis of multivariate data. The aim of this analysis is to reduce the dimensionality of a multivariate data set to the smallest number of meaningful and independent dimensions. The analysis can also provide interpretable linear functions of the original measured variables that may serve as valuable indices of variation. A brief introduction to principal components analysis is given herein, followed by an examination of a particular set of multivariate data accruing from a study of acute brain injuries in a pediatric population, in which severity of brain injury had been assessed with the Glasgow Coma Scale (CGS). Principal components analysis reveals that the GCS sum score is a particularly inefficient summarizer of information in this cohort. The determination of an objective weighting of measured variables, as provided through principal components analysis, is essential in the construction of meaningful neurological scoring instruments.

Coma↗
PubMed1999

A principal-components approach based on heritability for combining phenotype information.

For many traits, genetically relevant disease definition is unclear. For this reason, researchers applying linkage analysis often obtain information on a variety of items. With a large number of items, however, the test statistic from a multivariate analysis may require a prohibitively expensive correction for the multiple comparisons. The researcher is faced, therefore, with the issue of choosing which variables or combinations of variables to use in the linkage analysis. One approach to combining items is to first subject the data to a principal components analysis, and then perform the linkage analysis of the first few principal components. However, principal-components analyses do not take family structure into account. Here, an approach is developed in which family structure is taken into account when combining the data. The essence of the approach is to define principal components of heritability as the scores with maximum heritability in the data set, subject to being uncorrelated with each other. The principal components of heritability may be calculated as the solutions to a generalized eigensystem problem. Four simulation experiments are used to compare the power of linkage analyses based on the principal components of heritability and the usual principal components. The first of the experiments corresponds to the null hypothesis of no linkage. The second corresponds to a setting where the two kinds of principal components coincide. The third corresponds to a setting in which they are quite different and where the first of the usual principal components is not expected to have any power beyond the type I error rate. The fourth set of experiments corresponds to a setting where the usual principal components and the principal components of heritability differ, but where the first of the usual principal components is not without power. The results of the simulation experiments indicate that the principal components of heritability can be substantially different from the standard principal components and that when they are different, substantial gains in power can result by using the principal components of heritability in place of the standard principal components in linkage analyses.

Computer Simulation↗
PubMed1992

Composite index of skeletal mass: principal components analysis of regional bone mineral densities.

Principal components analysis is a statistical method that is used to reduce and explore data to facilitate further analyses. This method was applied to bone mineral densities measured at seven sites in 109 black and 44 white women, ages 22-80, at an internal medicine clinic in urban Detroit. We excluded subjects with a history of diseases or drugs known to affect bone metabolism. Principal components analysis was used to summarize the interrelationship of the densities and yielded two major results. First, the seven site measurements were reduced to a single, composite index (PC1) of skeletal mass that accounted for 73% of the variation in density among subjects. PC1 had roughly equal weights among the sites. A second combination of the seven sites indicated that the contrast between axial and appendicular regional densities accounted for another 10% of the variation among subjects. In investigating the relationship of density to age, body mass index, and ethnic group, we found that the principal components composite index had a stronger correlation with age (r = -0.58) and with body mass index (r = 0.34) than almost all of the regional densities. Black-white differences were larger for the composite index than for any single site density. A multiple regression of the composite index on ethnicity, body mass index, and age yielded a larger R2 (0.46) than any of the individual site densities. The second principal component, although of theoretical interest, showed a minimal ability to discriminate among subjects using the three independent variables of this study.

Absorptiometry, Photon↗
PubMed2004

Adverse effect of drug-induced emotional problems on work and daily activities. A principal component as an independent predictor of ADRs in Shanghai patients with osteo-arthropathy taking nabumetone.

OBJECTIVE: To discover principal components among assessed items from the WHO-SF36 survey by principal component analysis (PCA) and then to establish the relationship between the candidate principal component and the incidence of ADRs induced by nabumetone in Shanghai osteoarthropathy patients. METHOD: A total of 145 patients were interviewed using the WHO-SF36 questionnaire for quality of life (QOL) assessment before the administration of nabumetone. The sub-items of the questionnaire were analyzed using PCA and several comprehensive variables were established. Relationships between these newly formed variables and the overall incidence of adverse drug reactions (ADRs) caused by nabumetone were evaluated using univariate and multivariate analyses. RESULTS: Several principal components were identified and their linear parameters were estimated using PCA. Through univariate analysis, only 1 principal component--"adverse effect on work and daily activities as a result of emotional problems"--was found related to the incidence of ADRs. The odds ratio (OR) was 1.28 with 95% confidence intervals (CI) of 1.11 and 1.48, p = 0.0384. This result was validated using a multivariate logistic analysis performed on all the alternative candidate covariates, including family income, a history of ADRs on NSAIDs, the course of the disease, level of education, control of stress, coffee consumption and consumption of salty food. The covariate information was taken from other parts of the clinical report form (CRF) used in this research. The analysis proved that the principal component, adverse effect on work and daily activities as a result of emotional problems, was an independent factor related to the overall incidence of ADRs. The odds ratio (OR) from the logistic analysis was 1.34, with 95% confidence intervals (CI) of 1.16 and 1.55, p = 0.0309. CONCLUSIONS: The principal component identified can be applied to overcome some limitations in the WHO-SF36 questionnaire such as high correlation between variables, information overlaps and weak representation of variables and the statistical data analysis of QOL can therefore be made more effective. The study shows that the principal component "adverse effect on work and daily activities as a result of emotional problems" is an independent predictor of the overall incidence of ADRs induced by nabumetone.

Activities of Daily Living↗