SEARCH · PubMed Health

Results for “Principal Component Analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 721 records · Page 40Linked to original sources

PubMedDate not supplied

Component analysis in multivariate aging research.

A method of components analysis, related to principal components analysis, is described for applications in multivariate cross-sectional or longitudinal data. The method may be useful to researchers seeking to reduce the number of observed variables through scale construction. The method creates component variables as weighted sums of the observed variables using weights that are identical across groups and occasions. The statistical and conceptual properties of these components are discussed. The method is contrasted with traditional principal components analysis and factor analysis. An application of the method is presented using longitudinal WAIS and WAIS-R data.

Cross-Sectional Studies↗

PubMed1986

Application of pattern-recognition techniques in wavelength selection for instrumentally read reagent strips.

Pattern recognition techniques--discriminant analysis and principal component analysis--are utilized in selecting the wavelengths for monitoring, by reflectance spectroscopy, color-generating reactions involving uric acid and cholesterol in serum. The data base we used was accumulated by a rapid-scanning reflectance spectrophotometer that measured reflectance at 16 wavelengths every 5 s after the reaction was initiated. The data were then analyzed in multidimensional space mainframe computer with commercial statistical software packages. The most appropriate wavelengths were those that yielded the largest generalized distance between analyte concentration by discriminant analysis and the largest weighting coefficient by principal component analysis. For uric acid, taking the ratio of reflectance measured at two wavelengths instead of at a single wavelength much better separates the clinically significant concentrations. For cholesterol, the initiated. The data were then analyzed in multidimensional space hemoglobin, can be clearly demonstrated y the "pattern" generated with principal component analysis. generalized distance between analyte generalized distance between analyte concentration by discriminant

Cholesterol↗

PubMed2003

Morphological and biometric revision of the cleithra, opercular and pharyngeal bones of Iberian teleosts belonging to the genus Barbus (Pisces, Cyprinidae).

Species of the genus Barbus present in the Iberian Peninsula are an interesting study group, due to its diversity and complex taxonomic position. In this work, 299 specimens of eight species were studied. All of them are endemic to the Iberian Peninsula, except for Barbus meridionalis. Cleithra, opercular and pharyngeal bones were extracted from each specimen. These bones were morphologically compared and measured, obtaining biometric indices. From these indices, the biometric differences of each bone were analysed with a principal component analysis. Later, a principal component analysis and a discriminant analysis were performed considering the three bones together. The morphological differences and similarities are congruous with the biometric results. In addition, this osteological comparison partially agrees with the present taxonomic position of these species, being a contribution to the systematics and phylogeny of genus.

Animals↗

PubMed2004

Measurement of continuity of care in cardiac patients: reliability and validity of an in-person questionnaire.

BACKGROUND: Breaks in continuity of care occur during the care of patients with cardiac conditions, such as congestive heart failure (CHF) and atrial fibrillation (AF), during their hospitalization, the discharge process or follow-up care. Recently, to measure continuity of care for such patients, a comprehensive questionnaire was developed to reflect the cardiac patient's perspective on multifaceted aspects of continuity of care. OBJECTIVE: To examine the psychometric properties of an in-person interview questionnaire for the measurement of continuity of care in patients recently hospitalized with CHF or AF. METHODS: The Heart Continuity of Care Questionnaire (HCCQ) was administered to 350 cardiac patients (176 with CHF and 174 with AF) at least six months after discharge from hospitals in two urban centres. The questionnaire was assessed by item and principal components analysis. Factors derived from principal components analysis were assessed for internal consistency and construct validity against variables of comorbidity, health status and symptom severity. RESULTS: A principal components analysis produced three subscales. These subscales were internally consistent, and demonstrated construct validity through expected correlations with other variables and theoretical constructs. CONCLUSION: The present study supports the reliability and validity of the HCCQ for measuring continuity of care. The HCCQ subscales correspond to the theoretical components of continuity of care that have been proposed, namely relational, informational and management continuity. The HCCQ subscales may be of value for identifying problems in continuity of care and for evaluating interventions aimed at improving continuity of care for cardiac patients after hospital discharge.

Adult↗

PubMed2002

Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition.

Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.

Adsorption↗

PubMed1985

Towards automatic analysis of dynamic radionuclide studies using principal-components factor analysis.

A method is proposed for automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasises the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces "study space, S' and "theory space, T' are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99Tcm DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated.

Humans↗

PubMed1997

Development of outcome indicators for monitoring the quality of public mental health care.

OBJECTIVE: The study attempted to develop a brief and integrated set of reliable and valid outcome measures that could be used by both consumers and providers to assess the quality of public mental health care. METHODS: A model of outcomes in four domains-consumer satisfaction, functioning, quality of life, and clinical status-was developed from the literature and from the priorities expressed by members of an advisory group of stakeholders. Based largely on extant measures, a consumer survey and a case manager survey were then created to assess these domains. A total of 236 adult consumers of mental health services from six community mental health centers in Washington State were surveyed. The four-item case manager survey to rate consumers' clinical status was completed by 163 of the participants' case managers. Scores and ratings on the survey were analyzed using correlational analysis and principal components analysis to determine whether the data provided empirical support for the four-domain model. RESULTS: Principal components analysis demonstrated support for the four-domain model. Internal consistency of the outcome indicators was adequate, and their concurrent validity was partly supported. CONCLUSIONS: The described outcome measures provide a practical, empirically supported structure for monitoring and improving public mental health services.

Activities of Daily Living↗

PubMed2003

Assessment of the surface water quality in Northern Greece.

The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is presented in this study. The dataset consists of analytical results from a 3-yr survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches. Twenty-seven parameters have been monitored on 25 key sampling sites on monthly basis (total of 22,350 observations). The dataset was treated using cluster analysis (CA), principal component analysis and multiple regression analysis on principal components. CA showed four different groups of similarity between the sampling sites reflecting the different physicochemical characteristics and pollution levels of the studied water systems. Six latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.

Environmental Monitoring↗

PubMed2006

A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids.

The aim of the research was to investigate metabolic variations associated with genetic modifications in the grains of Zea mays using metabonomic techniques. With this in mind, the non-targeted characteristic of the technique is useful to identify metabolites peculiar to the genetic modification and initially undefined. The results obtained showed that the genetic modification, introducing Cry1Ab gene expression, induces metabolic variations involving the primary nitrogen pathway. Concerning the methodological aspects, the experimental protocol used has been applied in this field for the first time. It consists of a combination of partial least square-discriminant analysis and principal component analysis. The most important metabolites for discrimination were selected and the metabolic correlations linking them are identified. Principal component analysis on selected signals confirms metabolic variations, highlighting important details about the changes induced on the metabolic network by the presence of a Bt transgene in the maize genome.

Amino Acids, Branched-Chain↗

PubMed2005

Numerical approaches for quantitative analysis of two-dimensional maps: a review of commercial software and home-made systems.

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

Cluster Analysis↗

PubMed1999

Application of multivariate data analysis methods to comparative molecular field analysis (CoMFA) data: proton affinities and pKa prediction for nucleic acids components.

Multivariate data analysis methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)) are applied to the analysis of the CoMFA (Comparative Molecular Field Analysis) data for several nucleic acids components. The data set includes nitrogenated bases, nucleosides, linear nucleotides, 3', 5'-cyclic nucleotides and oligonucleotides. PCA is applied to study the structure of the CoMFA data and to detect possible outliers in the data set. PLS is applied to correlate the CoMFA data with either calculated AM1 proton affinities or with experimental pKa values. The possibility of making a prediction of pKa values directly from 3D structures of the monomers for polynucleotides is also shown. The influence of the superposition criteria and of conformational changes along the glycosidic bond on the pKa prediction are studied as well.

Glycosylation↗

PubMed2007

Evaluation of PCA and ICA of simulated ERPs: Promax vs. Infomax rotations.

Independent components analysis (ICA) and principal components analysis (PCA) are methods used to analyze event-related potential (ERP) and functional imaging (fMRI) data. In the present study, ICA and PCA were directly compared by applying them to simulated ERP datasets. Specifically, PCA was used to generate a subspace of the dataset followed by the application of PCA Promax or ICA Infomax rotations. The simulated datasets were composed of real background EEG activity plus two ERP simulated components. The results suggest that Promax is most effective for temporal analysis, whereas Infomax is most effective for spatial analysis. Failed analyses were examined and used to devise potential diagnostic strategies for both rotations. Finally, the results also showed that decomposition of subject averages yield better results than of grand averages across subjects.

Algorithms↗

PubMed1993

[Multivariate analysis of mandibular morphology with fetal growth by means of the soft standardized radiographs].

Mandibular growth of the fetus is a great matter of importance to anatomy, anthropology and also oral and Maxillofacial surgery. The postnatal changes of the mandible that have occurred with growth have been reported by numerous investigators. However, a few researchers have investigated the prenatal growth of the mandible. The purposes of this study were discussed how to change in size and shape of the mandible during the fetal period. The materials consisted of 162 human fetuses from the body length (BL) of 95 mm, to 500 mm. They were divided into four groups: 95-199 mm (BL1, N = 40), 200-299 mm (BL2, N = 76), 300-399 mm (BL3, N = 26), 400-500 mm (BL4, N = 20). All specimens were stored in formalin, and only those free from gross deformation were selected for study. The heads were removed by decapitation at the level of the hyoid bone. The soft standardized radiographs were taken of the frontal, lateral, basal aspects. They were used for tracing and angular and linear measurements (total thirty). The data were used to evaluate the mandibular form by multivariate analysis. They were supported the univariate analysis. The findings leads to the following conclusions: 1) Cluster analysis and principal component analysis were applied to the data to make clear the correlation between 30 variables. Four clusters were constructed at the level ninety similarities in cluster analysis. They were corresponded to four factors were obtained in principal component analysis. 2) The data were subjected to a stepwise discriminant function analysis using the seventeen of original 30 variables. Nine variables, mandibular length (Go-Me), mandibular ramus height (Col-Go), Symphyseal height (Id-Me), mandibular body height (Kr'-beta), gonial angle (< Co2, Go, Me), mental angle (< Id, Pog, Go), gonial width (BGo), alveolar width (BKr'), basal angle of mandible (< RGo, Me, LGo), were selected as the best discriminators. The incidence of correctly classified cases 88.68%. 3) As a result of the first discriminant function of the discriminant analysis and one-way analysis of variance (ANOVA) and t-test, mandibular length (Go-Me), gonial width (BGo), alveolar width (BKr'), Symphyseal height (Id-Me) were longer in tall group that short group (BL4 > BL3 > BL2 > BL1). Therefore, the first discriminant function was accounted for the size factor. 4) In consequence of the second discriminant function of the discriminant analysis and regression analysis, gonial width (BGo) was grown broader than alveolar width (BKr') from BL1 to BL2.(ABSTRACT TRUNCATED AT 400 WORDS)

Anthropometry↗

PubMed1987

Chemometric classification of reversed-phase high-performance liquid chromatography columns.

Cluster analysis and principal components analysis have been used to classify nine octadecyl (C18) high-performance liquid chromatography (HPLC) columns into three general groups displaying similar chromatographic behaviour. Principal components analysis was also able to identify the key test compounds on which the classification was most highly dependent. These identifications agreed with the classification and test compound selection by an HPLC specialist. In addition, the chemometric techniques can easily be extended to many more columns and test measurements than could be conveniently examined by a human expert.

Chemical Phenomena↗

PubMed2001

Statistical pattern analysis of gene expression profiles for glioblastoma tissues and cell lines.

The recent development and refinement of cDNA array and genechip techniques allows for large-scale parallel screening of gene expression and thereby provides a global assessment of molecular events transpiring in cell populations. We hypothesized that such an approach might help illuminate current issues in glioma research. To what degree are glioblastoma multiforme (GBM) cell lines representative of primary GBM tumors? We carried out a gene expression profiling study using cDNA array technology on 10 GBM primary tissues and 3 GBM cell lines. The gene expression levels were quantified, and the within-subject ranks of the gene expression levels were subsequently evaluated by hierarchical clustering analysis, multidimensional scaling analysis, and principal component analysis. Hierarchical cluster analysis shows that the 3 cell lines form one main cluster and the 10 tissue samples form a separate cluster. Multidimensional scaling and principal components analysis provided further graphical demonstration that the cell lines are clearly different from the tissue samples and that the cell lines are more different between themselves than are the tissues.

Central Nervous System Neoplasms↗

PubMed2003

Multivariate chemometrics for the forensic discrimination of blue ball-point pen inks based on their Vis spectra.

Fifty blue ball-point pen inks of five different brands were examined on the basis of the Vis spectrum of their ethanolic solutions with a view to achieving good discrimination between them. Samples were dissolved in absolute ethanol and their absorbance values in the range of 400-750 nm, after appropriate transformations, were used as variables in the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA). These techniques were used successively so that an effective and meaningful discriminant model was calculated in the final step. The initial 351 variables (log10 transformed ink absorption values at each wavelength) were subjected to a K-means CA over the objects (samples) and only 20 variables were retained. Principal component analysis was used to detect any outliers (four samples were removed) and the remaining samples were re-subjected to PCA to decide how many variables to enter into DA and whether original variables or components should be used. It was found that the first three principal components (in accordance with the Kaiser criterion) were good descriptors of the 20 original variables (96.97% of the data variance was explained) and their use as latent variables in DA lead to low average variable redundancy (33.6%) in the discriminant model. The calculated model had a Wilks' lambda of 8.98 x 10(-5) and was statistically significant at the P = 0.05 level. The post hoc classification of the training dataset was 100% correct. From the DA results and the component loadings it was found that discrimination was achieved on the basis of differences in the shape of the absorption bands as well as their relative intensities. The method was therefore deemed appropriate for supporting exclusionary forensic purposes.

Journal Article↗

PubMed1996

Muscle biopsy index for discriminating between endurance horses with different performance records.

Biopsies were taken from three different depths of the gluteus medius muscle of 36 endurance horses aged between four and 17 years. Twenty of the horses were considered excellent performers according to their three fastest records in endurance events over the previous two or three years. The other 16 horses were moderate performers, with a mean racing speed < 3.5 ms-1 (in 120 to 180 km endurance rides), < 4.0 ms-1 (in 80 to 120 km endurance rides) or < 3.75 ms-1 (in 40 to 60 km endurance rides). The biopsy specimens were analysed for fibre type distribution (type I, IIA and IIB), fibre area and relative fibre area. The activities of the enzymes citrate synthase, 3-hydroxy-acyl-CoA-dehydrogenase and lactate dehydrogenase were also determined. The data were subjected to discriminant analysis and principal component analysis. It was possible to discriminate correctly between the horses according to their performance records by means of the discriminant analyses when the histochemical and morphometric data from all three muscle biopsies were used, but not when data from only a single muscle biopsy were used. Principal component analysis confirmed that the most efficient muscle parameters describing the athletic potential of endurance horses were the percentages and relative areas of the fibre types. Horses with a better endurance performance had higher percentages and relative areas of types I and IIA fibres and lower percentages and relative areas of type IIB fibres than moderate performers.

3-Hydroxyacyl CoA Dehydrogenases↗

PubMed2004

Evaluation of pyridine-3-carboxylic acid as a drug carrier by utilizing multivariate methods, structure property correlations, and pattern recognition techniques.

Multivariate methods and molecular properties are utilized to show similarity of pyridine-3-carboxylic acid (nicotinic acid) to seven drugs that penetrate the central nervous system. Multivariate methods applied include cluster analysis, discriminant analysis, correspondence analysis, self organizing tree algorithm (SOTA) analysis, factor analysis, and principal component analysis. Numerical values of properties for nicotinic acid showed very high correlation with the values from dihydropyridine, barbital, metharbital, phenobarbital, methohexital, 4-aminohex-5-enoic acid, and (4-chlorophenyl)(5-fluoro-2-hydroxyphenyl)methanone. Descriptive statistics of property values for these drugs showed overlapping numerical values for partition coefficients, index of refraction, and nOnN. Principal component analysis and factor analysis of molecular properties showed that nicotinic acid is highly similar to dihydropyridine as well as to other members of this group of drugs. Applying cluster analysis utilizing standard euclidean distance with single linkage and centroid linkage showed that nicotinic acid is highly similar to dihydropyridine and both are consistently grouped into the identical cluster (indicating high similarity). Both nicotinic acid and dihydropyridine show zero violations of the Rule of 5, which indicates good bioavailability characteristics. Discriminant analysis of molecular properties for these eight compounds could not demonstrate differentiation between nicotinic acid and dihydropyridine within the properties applied, this indicating high level of similarity. Neural cluster analysis of molecular properties showed that nicotinic acid and dihydropyridine are included into the same cluster (indicating very strong similarity). SOTA analysis also placed nicotinic acid and dihydropyridine into the same cluster unit. SOTA analysis of molecular properties indicates high similarity between nicotinic acid and dihydropyridine. Correspondence analysis was performed on the molecular properties and showed that there exists considerable association between dihydropyridine and nicotinic acid. Multiple regression analysis of these molecular properties produced a mathematical equation to predict the formula weight of similar compounds for use as drug carriers.

Algorithms↗