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 235 records · Page 13Linked to original sources
PubMed1991

[Aging effects on blood biochemical parameters in aged women: a longitudinal study using principal component analysis].

In order to examine the aging effects on blood biochemical parameters, 25 aged females (70 +/- 8 years old at the beginning of this investigation) were studied prospectively for 7 years. Biochemical parameters were measured in 1982, 1985, 1987, and 1989. Component scores were obtained by principal component analysis applied to all of these 100 data. The data of 13 biochemical parameters and 4 component scores were analyzed for aging, cohort, and time effects by utilizing 3 types of approach i.e., longitudinal, cross-sectional, and time-series. Positive aging effects were observed on blood urea nitrogen and 4th component score. Fourth component score had positive relations to blood urea nitrogen and total bilirubin, and a negative relation to total protein. This 4th component score increased with longitudinal age changes among all cohorts divided by decade, and it increased markedly over 70 years old. These results suggest that the aging effects on biochemical parameters are related with both the decrease in glomerular and hepatocellular functions and the increase in breakdown of tissue protein.

Aged↗
PubMed2005

Effect of multivariate process instability on principal component analysis: a case study.

With the rising use of principal component analysis/partial least squares (PCA/PLS) in the process analytical technology (PAT) initiative of the pharmaceutical industry, it seems appropriate to view that approach from a statistical process control (SPC) perspective. The purpose of this study was to demonstrate the effect of process instability (ie, state of statistical out-of-control) on use of PCA/PLS. The demonstrated differences in results should encourage PCA/PLS users to incorporate SPC as an active part of their process analytical control (PAC) toolkit to check for stability prior to drawing conclusions based on PCA/PLS analysis.

Chi-Square Distribution↗
PubMed2006

Metabolomic differentiation of deer antlers of various origins by 1H NMR spectrometry and principal components analysis.

The metabolomic analysis of various types of deer antler was performed by 1H NMR spectrometry and principal components analysis (PCA). The PCA of the 1H NMR spectra of the aqueous fractions allowed a clear discrimination between antler samples according to their origins by the first three principal components (PC1, PC2, and PC3), which cumulatively accounted for 93.5% of the variation in all variables. In particular, the score plots by the combination of PC1 and PC3 allowed an excellent separation of the antler samples. In addition, the major peaks in 1H NMR spectra contributing to the discrimination were assigned to lactate, alanine, acetic acid, choline, glycine, valine, tyrosine, and phenylalanine. This metabolomic-analysis-based method allows various types of deer antler to be efficiently differentiated without any pre-purification steps.

Animals↗
PubMed1996

[Classification of pulmonary-function tests by principal component analysis].

The structure of pulmonary-function data obtained from 468 randomly selected subjects was analyzed. The subjects included patients with chronic bronchitis, bronchial asthma, chronic pulmonary emphysema, diffuse panbronchiolitis, and idiopathic interstitial pneumonitis, and normal health adults. From among the many possible indices of pulmonary function, 19 were chosen and were used as variables in principal component analysis. Six significant principal components (PCs) were extracted. The first three PCs accounted for 70% of the total information and were termed "ventilation," "volume," and "diffusion." The second three PCs accounted for 17% of the information and were termed "small airway," "lower airway," and "shape." Indices of ventilatory unevenness were not separated from indices of airway obstruction, and were included in the first PC. No other, unknown PC was detected with these pulmonary-function indices. The relationship among indices is displayed in a factor loading matrix, and pulmonary-function tests are classified statistically.

Adult↗
PubMed1997

Multipoint dissolution specification and acceptance sampling rule based on profile modeling and principal component analysis.

In dissolution testing, multiple dissolution measurements at specific time points are needed in quality control when the compliance of the product requires controlled dissolution throughout the time course. The dissolution specification based on general multivariate confidence region was proposed by Chen and Tsong (8). This paper presents two alternative procedures when the dissolution profile consists of important measurements at more than 4 time points. In the first procedure, when the dissolution profile can be described by a physical curve through modeling, the dissolution specification is developed based on the confidence region of the parameters of the physical curve. In the second procedure, the principal components (PCS) as the linear combinations of the dissolution measurements are identified and dissolution specification is set based by the confidence intervals of the values of principal components. In both approaches the specification can be set at lower dimensions than the general multivariate confidence region approach. A single-stage acceptance rule can be used in both approaches by first projecting the dissolution values of each tablet in the new testing batch onto the determined parameters axes (through modeling in modeling approach and through projection on the selected PCS in principal component approach). Then check if the projections of the new tablet fall within the specifications. Finally, count the number of tablets that fall outside the specification limits and reject the batch if the proportion of out-of-specification tablet is high and accept the lot for release if the proportion is low.

Chemical Phenomena↗
PubMed2001

Bleachability and characterization by Fourier transform infrared principal component analysis of Acetosolv pulps obtained from sugarcane bagasse.

Sugarcane bagasse Acetosolv pulps were bleached by xylanase and the pulps classified by using Fourier transform infrared (FTIR) spectroscopy and principal component analysis (PCA). Pulp was treated with xylanase for 4-8 hwith stirring at 30 degrees C. Some samples were further extracted with NaOH for 1 h at 65 degrees C. FTIR spectra were recorded directly from the dried pulp samples by using the diffuse reflectance technique. Reduction in kappa number of 69% was obtained after sequence xylanase (4 h)-alkaline extraction. During bleaching the viscosity decreased only 12%. FTIR-PCA showed that the first three principal components (PCs) explained more than 90% of the total variance of the pulp spectra. PC2 x PC1 plot showed that the points related to pulps from sequence xylanase (4 h)-alkaline extraction are different from the other. This group is enlarged by plotting PC3 x PC1 or PC3x PC2 containing all pulps submitted to alkaline extraction. PC2 and PC3 are the principal factor for differentiation of the pulps. These PCs suffer influence of the ester bands (1740 and 1244 cm(-1)). On the other hand, the pulps bleached only with xylanase could not be differentiated from the nonbleached pulps.

Biotechnology↗
PubMed2005

A web-based tool for principal component and significance analysis of microarray data.

UNLABELLED: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer. AVAILABILITY: The tool can be used on-line or downloaded at http://lgsun.grc.nia.nih.gov/ANOVA/

Algorithms↗
PubMed2006

Noise perturbation in functional principal component analysis filtering for two-dimensional correlation spectroscopy: its theory and application to infrared spectra of a poly(3-hydroxybutyrate) thin film.

A method based on noise perturbation in functional principal component analysis (NPFPCA) is for the first time introduced to overcome the noise interference problem in two-dimensional correlation spectroscopy (2D-COS). By the systematic addition of synthetic noise to the dynamic multivariate spectral data, the functional principal component analysis (FPCA) described in this report is able to accurately determine which eigenvectors are representing significant signals instead of noise in the original data. This feature is especially useful for the data reconstruction and noise filtering. Reconstructed data resulted from the smooth eigenvectors can produce much more reliable 2D correlation spectra by removing the correlation artifacts from noise, which in turn enable more accurate interpretation of the spectral variations. The usefulness of this method is demonstrated with a theoretical framework and applications to the 2D correlation analyses of both simulated data and temperature-dependent reflection-absorption infrared spectra of a poly(3-hydroxybutyrate) (PHB) thin film.

Absorption↗
PubMedDate not supplied

Qualitative organic analysis. Part 2. Identification of drugs by principal components analysis of standardized TLC data in four eluent systems and of retention indices on SE 30.

The principal components (PC) analysis of standardized Rf values in four eluent systems [ethyl acetate/methanol/30% ammonia (85:10:15), cyclohexane/toluene/diethylamine (65:25:10), ethyl acetate/chloroform (50:50), and acetone with the plate dipped in potassium hydroxide solution] and of gas chromatographic retention indices in SE 30 for 277 compounds provided a two-principal-components model that explains 82% of the total variance. The scores plot allowed identification of unknowns or restriction of the range of inquiry to very few candidates. Comparison of these candidates with those selected from another PC model derived from TLC data only allowed identification of the drug in all the examined cases.

Chromatography, Gas↗
PubMed2002

Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals.

The purpose of this research is twofold. First is to extend the work of Smith (1992, 1996) and Smith and Miao (1991, 1994) in comparing item fit statistics and principal component analysis as tools for assessing the unidimensionality requirement of Rasch models. Second is to demonstrate methods to explore how violations of the unidimensionality requirement influence person measurement. For the first study, rating scale data were simulated to represent varying degrees of multidimensionality and the proportion of items contributing to each component. The second study used responses to a 24 item Attention Deficit Hyperactivity Disorder scale obtained from 317 college undergraduates. The simulation study reveals both an iterative item fit approach and principal component analysis of standardized residuals are effective in detecting items simulated to contribute to multidimensionality. The methods presented in Study 2 demonstrate the potential impact of multidimensionality on norm and criterion-reference person measure interpretations. The results provide researchers with quantitative information to help assist with the qualitative judgment as to whether the impact of multidimensionality is severe enough to warrant removing items from the analysis.

Adolescent↗
PubMed2005

Use of principal component analysis and the GE-biplot for the graphical exploration of gene expression data.

This note is in response to Wouters et al. (2003, Biometrics 59, 1131-1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE-biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.

Algorithms↗
PubMed2006

Effect of washing on identification of Bacillus spores by principal-component analysis of fluorescence data.

The fluorescence spectra of Bacillus spores are measured at excitation wavelengths of 280, 310, 340, 370, and 400 nm. When cluster analysis is used with the principal-component analysis, the Bacillus globigii spores can be distinguished from the other species of Bacillus spores (B. cereus, B. popilliae, and B. thuringiensis). To test how robust the identification process is with the fluorescence spectra, the B. globigii is obtained from three separate preparations in different laboratories. Furthermore the fluorescence is measured before and after washing and redrying the B. globigii spores. Using the cluster analysis of the first two or three principal components of the fluorescence spectra, one is able to distinguish B. globigii spores from the other species, independent of preparing or washing the spores.

Algorithms↗
PubMed2001

Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model.

A multivariate statistical method has been applied to apportion the atmospheric pollutant concentrations measured by automatic gas analyzers placed on a mobile laboratory for air quality monitoring in Taranto (Italy). In particular, Principal Component Analysis (PCA) followed by Absolute Principal Component Scores (APCS) technique was performed to identify the number of emission sources and their contribution to measured concentrations of CO, NOx, benzene toluene m+p-Xylene (BTX). This procedure singled out two different sources that explain about 85% of collected data variance.

Air Pollutants↗
PubMed1998

Principal component ANN for modelling and control of baker's yeast production.

Modelling of baker's yeast production by the principal component based artificial neural networks (ANN) is presented. The models are derived for their application in adaptive control of fermentation by the internal model control (IMC) method. Modelling data are from industrial production in 40 m3 deep jet bioreactor and from computer simulations. The modelling effort is focused on selection of ANN structure and model verification. Principal component analysis of process variables results in projection of patterns to a space of low dimension, which enables determination of ANN structure, removes data colinearity and random components of measurement signals, and model degradation by over-training is eliminated. In view of IMC application, the models for prediction of the controlled variable (ethanol partial pressure) and the inverse model for manipulative variable (molasses feed rate) are determined. The models are tested for their predictability in the time horizon from 1 to 20 min. ANN models are derived with average relative errors for untrained patterns in the range from 1 to 10%.

Bioreactors↗
PubMed1982

Spatial principal components of multichannel maps evoked by lateral visual half-field stimuli.

Multichannel records of responses to large and small hemiretinal stimuli were obtained from 6 healthy subjects. Scalp distribution maps were constructed for all conditions at all post-stimulus times and component latencies were objectively determined by computing a reference-independent measure of field power. This procedure identified 2 components (at 100 and 140 msec). The scalp distribution data at these latencies were entered into a spatial principal components analysis which further reduced the data set to three underlying spatial principal components. These components may be regarded as reflecting underlying processes and were related to experimental conditions. A component reflecting lateralized brain activity displayed a significant interaction between size and retinal location of the stimulus with large stimuli showing a more pronounced lateralization over the hemisphere contralateral to the stimulated hemiretina, and the scores on this component were low for upper hemiretinal stimuli. These findings are in agreement with intracranially recorded evoked potential data and theoretical dipole source computations, and confirm a model of cortical neuronal generator processes whose locations and orientations in the hemisphere ipsilateral to the hemiretina stimulated are influenced not only by retinal stimulus location but also by stimulus size.

Brain↗
PubMed2005

Quantitative morphological study of microglial cells in the ischemic rat brain using principal component analysis.

Pathogenic stimuli induce alterations in the morphology of microglial cells. We analysed changes in lectin-stained cells on the 1st, 3rd, 7th or 14th day after transient global ischemia. Three areas differing in the degree of microglial reaction were selected for analysis: the upper cerebral cortex, the hippocampal CA1 area, and the hilus of the dentate gyrus. Nine morphological parameters, including fractal dimension, lacunarity, self-similarity range, solidity, convexity and form factor were determined. Then the resultant data were processed using principal component analysis (PCA). We found that the two first principal components together explained more than 73% of the observed variability, and may be sufficient both to describe the morphological diversity of the cells, and to determine the dynamics and direction of the changes. In both hippocampal areas, the transformation to hypertrophied and phagocytic cells was observed, but changes in the hilus were faster than in the CA1. In contrast, in the cortex, a microglial reaction was characterised by an increase in the complexity of processes. The results presented show that the quantitative morphological analysis can be an effective tool in research on the reactive behaviour of microglia and, particularly, in the detection of small and early changes in the cells.

Animals↗
PubMed2004

Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods.

BACKGROUND: The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood. METHODS: Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA). RESULTS: The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far.

Animals↗
PubMed2001

Characterisation of acute myocardial ischaemia in a canine model based on principal component analysis of unipolar endocardial electrograms.

The study presents a method for identifying endocardial electrical features relevant to local ischaemia detection at rest. The method consists of, first, normalisation of electrograms to a uniform representation; secondly, the use of principal component analysis to reduce the dimensionality of the electrogram vector space; and, thirdly, a search for a classification axis that matches the degree of ischaemia present in the tissue. Left ventricular myocardial states were assessed by echocardiography and NOGA mapping in eight dogs at baseline and then immediately after, 5h after and 3 days after occlusion of the left anterior descending coronary artery. Five principal components were required to approximate electrograms with an average error of less than 10% of the peak-to-peak amplitude. Correlations of 0.77, 0.80 and 0.84 were obtained between the principal component-based parameters and the echocardiography scores at the three ischaemic stages, respectively. Expression of these parameters in the time domain showed that the major changes occurred in the depolarisation segment of the endocardial electrogram as well as in the ST-segment. In conclusion, the proposed method provides a suitable alternative co-ordinate system for the classification of ischaemic regions and highlights signal segments that change as a result of pathology.

Acute Disease↗