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PubMed2005

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis.

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.

Aminopyrine↗
PubMed2003

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis.

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

Adult↗
PubMed2005

Principal component analysis for predicting transcription-factor binding motifs from array-derived data.

BACKGROUND: The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs. RESULTS: The promoter matrix was defined to establish a quantitative relationship between the IL-1-driven mRNA alteration and genomic DNA sequences of the IL-1 responsive genes. The matrix was decomposed with SVD, and the effects of 8 potential TFBMs (5'-CAGGC-3', 5'-CGCCC-3', 5'-CCGCC-3', 5'-ATGGG-3', 5'-GGGAA-3', 5'-CGTCC-3', 5'-AAAGG-3', and 5'-ACCCA-3') were predicted from a pool of 512 random DNA sequences. The prediction included matches to the core binding motifs of biologically known TFBMs such as AP2, SP1, EGR1, KROX, GC-BOX, ABI4, ETF, E2F, SRF, STAT, IK-1, PPARgamma, STAF, ROAZ, and NFkappaB, and their significance was evaluated numerically using Monte Carlo simulation and genetic algorithm. CONCLUSION: The described SVD-based prediction is an analytical method to provide a set of potential TFBMs involved in transcriptional regulation. The results would be useful to evaluate analytically a contribution of individual DNA sequences.

Algorithms↗
PubMed2004

Principal-component-analysis eigenvalue spectra from data with symmetry-breaking structure.

Principal component analysis (PCA) is a ubiquitous method of multivariate statistics that focuses on the eigenvalues lambda and eigenvectors of the sample covariance matrix of a data set. We consider p, N-dimensional data vectors xi drawn from a distribution with covariance matrix C. We use the replica method to evaluate the expected eigenvalue distribution rho(lambda) as N--> infinity with p=alphaN for some fixed alpha. In contrast to existing studies we consider the case where C contains a number of symmetry-breaking directions, so that the sample data set contains some definite structure. Explicitly we set C=sigma2I+sigma(2)Sigma(S)(m=1)A(m)B(m)B(T)(m), with A(m)>0 for all m. We find that the bulk of the eigenvalues are distributed as for the case when the elements of xi are independent and identically distributed. With increasing alpha a series of phase transitions are observed, at alpha=A(-2)(m), m=1,2,..., S, each time a single delta function, delta(lambda-lambda(u)(A(m))), separates from the upper edge of the bulk distribution, where lambda(u)(A)=sigma(2)[1+A][1+(alphaA)(-1)]. We confirm the results of the replica analysis by studying the Stieltjes transform of rho(lambda). This suggests that the results obtained from the replica analysis are universal, irrespective of the distribution from which xi is drawn, provided the fourth moment of each element of xi exists.

Fourier Analysis↗
PubMed2005

Improved background removal method using principal components analysis for spatially resolved electron energy loss spectroscopy.

Principal components analysis (PCA) factor filtering is implemented for the improvement of background removal in noisy spectra. When PCA is used as a method for filtering before background removal in electron energy loss spectroscopy elemental maps, an improvement in the accuracy of the background fit with very short fitting intervals is achieved, leading to improved quality of elemental maps from noisy spectra. This opens the possibility to use shorter exposure times for elemental mapping, leading to fewer problems with, for example, drift and beam damage.

Journal Article↗
PubMedDate not supplied

Geographic variability of digital ridge-counts: principal components analysis of male and female world samples.

A principal components analysis was made on the means of the ten digital ridge-counts, separately for 195 male populations and 165 female populations, using the variance/covariance matrix. The first three components explain respectively 74%, 12% and 4.6% in the male sample and 77.2%, 11.3% and 4.1% in the female sample. The factor scores of the individual samples were computed and scatter diagrams were plotted for the first two components. They show the existence of a geographic distribution. Ellipses of dispersion of the geographic groups show some similarity between male and female samples, although homogeneity is less than in females and less on the right than on the left hand. The structure of the components shows that the first one corresponds to a factor size, the second contrasts the thumb with fingers 2 and 3, third opposes fingers 2 and 3 to fingers 4 and 5. Data from more recent population studies can easily be referred to the suggested scheme. Their position will define more accurately the differentiation between the geographic groups.

Africa↗
PubMed1985

Learning disabilities and intelligence test results: a model based on a principal components analysis of the WISC-R.

The unrotated principal components analysis of the WISC-R normative data yields a bipolar factor 2 which corresponds to a verbal-non-verbal continuum of test material. A review of published WISC-R and WISC data from learning disabled (LD) children reveals that the amount of deficit shown by these children on any particular subtest is closely proportional to the degree of verbal content, as expressed by the factor 2 score coefficient of that subtest. This test-specific effect was found to be very much greater in LD boys than in LD girls.

Adolescent↗
PubMed2004

Asymptotic biases of the unrotated/rotated solutions in principal component analysis.

Asymptotic biases of the parameter estimates in principal component analysis with substantial misspecification are derived. The solutions for unstandardized and standardized observed variables are considered with and without orthogonal and oblique rotations. The distribution of observed variables can be non-normal as long as the finite fourth-order moments of the observed variables exist. When multivariate normality holds for the observed variables, substantial reduction of the amount of computation can be achieved. Numerical examples with simulations are given, with some discussion on the tendency of the biases to reduce the absolute values of parameter estimates.

Bias↗
PubMed2006

Application of principal component analysis and Raman spectroscopy in the analysis of polycrystalline BaTiO3 at high pressure.

The principal component analysis (PCA) was applied to Raman spectra of polycrystalline BaTiO(3) under pressure from atmospheric pressure to approximately 6.72 GPa. For the system utilized, PCA was able to distinguish spectral features and to determine the phase transition pressure: tetragonal to cubic at approximately 2.0 GPa. The present study demonstrates the potentialities of the application of PCA to the investigation on phase transitions at high pressure by Raman spectroscopy.

Barium Compounds↗
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↗
PubMed2001

Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer.

OBJECTIVE: To assess the effect of different methods of classifying food use on principal components analysis (PCA)-derived dietary patterns, and the subsequent impact on estimation of cancer risk associated with the different patterns. METHODS: Dietary data were obtained from 232 endometrial cancer cases and 639 controls (Western New York Diet Study) using a 190-item semi-quantitative food-frequency questionnaire. Dietary patterns were generated using PCA and three methods of classifying food use: 168 single foods and beverages; 56 detailed food groups, foods and beverages; and 36 less-detailed groups and single food items. RESULTS: Classification method affected neither the number nor character of the patterns identified. However, total variance explained in food use increased as the detail included in the PCA decreased (approximately 8%, 168 items to approximately 17%, 36 items). Conversely, reduced detail in PCA tended to attenuate the odds ratio (OR) associated with the healthy patterns (OR 0.55, 95% confidence interval (CI) 0.35-0.84 and OR 0.77, 95% CI 0.49-1.20, 168 and 36 items, respectively) but not the high-fat patterns (OR 0.95, 95% CI 0.57-1.58 and OR 0.85, 0.51-1.40, 168 and 36 items, respectively). CONCLUSIONS: Greater detail in food-use information may be desirable in determination of dietary patterns for more precise estimates of disease risk.

Adult↗
PubMed1997

Recognition of patient anaesthetic levels: neural network systems, principal components analysis, and canonical discriminant variates.

The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. It is shown that inferential parameters can be used to recognise the levels of anaesthesia. In addition to demonstrating the ability of neural networks for classification we were interested in understanding the classification strategy discovered by the neural networks. Multivariate data analysis techniques, namely Principal Components Analysis and Canonical Discriminant Variates, were applied to analyse the resultant networks.

Anesthesia↗
PubMed1995

Use of principal component analysis for the evaluation of the retention behaviour of monoamine oxidase inhibitory drugs on beta-cyclodextrin column.

The retention of 17 monoamine oxidase inhibitory drugs (proparlgylamine derivatives) were determined on a beta-cyclodextrin polymer (beta CDP)-coated silica column using ethanol-0.05 M K2HPO4 (6:4 v/v) as the eluent. The relative strength of interaction between the drugs and a water soluble beta-cyclodextrin polymer was determined by charge-transfer chromatography carried out on reversed-phase TLC layers. The relationship between capacity factors, physicochemical parameters and inclusion complex forming capacity of the monoamine oxidase inhibitory drugs were evaluated by stepwise regression analysis and by principal component analysis (PCA) followed by two-dimensional nonlinear mapping and varimax rotation. Calculations indicated that the retention of monoamine oxidase inhibitory drugs on beta CDP column is mainly governed by their steric and lipophylic parameters. Significant linear correlations were found between the corresponding coordinates of varimax rotation and two-dimensional nonlinear maps proving the suitability of both methods for the reduction of dimensionality of complicated data matrices.

Chemical Phenomena↗
PubMed1995

[Principal component analysis of body surface potential distribution and that clinical application].

The purpose of this study was to compress a huge amount of body surface potential data with principal component analysis. The first 6 principal components were extracted from data set obtained from both 25 normal subjects and 100 patients. The factor loading from the 1st to the 3rd principal component (PC) displayed dipolar distribution and that from the 4th to the 6th PC displayed multipolar distribution. In conclusion, the principal component analysis on body surface maps successfully condensed mapped data without significant loss of total variance and it was suggested that this method may be useful for detecting the presence of multipolar distribution.

Body Surface Potential Mapping↗
PubMed1994

Automatic analysis of sleep using two parameters based on principal component analysis of electroencephalography spectral data.

A computer program for the analysis of a sleep electroencephalogram (EEG) is presented. The method relies on two steps. First, a spectral analysis is performed for signals recorded from one or more electrode locations. Then, two EEG parameters are obtained by storing the spectral activity in a multidimensional space, whose dimension is reduced using principal component analysis (PCA) techniques. The main advantage of these parameters is in describing the process of sleep on a continuous scale as a function of time. Validation of the method was performed with the data collected from 16 subjects (8 young volunteers and 8 elderly insomniacs). Results showed that the parameters correlate highly with the hypnograms established by conventional visual scoring. This signal parametrisation, however, offers more information regarding the time course of sleep, since small variations within individual sleep stages as well as smooth transitions between stages are assessed. Finally, the concurrent use of both parameters provides an original way of considering sleep as a dynamic process evolving cyclically in a single plane.

Adult↗
PubMed2006

Quantitative genetic analysis of blood pressure reactivity to orthostatic tilt using principal components analysis.

Blood pressure (BP) reactivity to orthostatic tilt may be predictive of cardiovascular disease. However, the genetic and environmental influences on BP reactivity to tilt have not been well examined. Identifying different influences on BP at rest and BP during tilt is complicated by the intercorrelation among multiple measurements. In this study, we use principal components analysis (PCA) to reduce multivariate BP data into components that are orthogonal. The objective of this study is to characterize and examine the genetic architecture of BP at rest and during head-up tilt (HUT). Specifically, we estimate the heritability of individual BP measures and three principal components (PC) derived from multiple BP measurements during HUT. Additionally, we estimate covariate effects on these traits. The study sample consisted of 444 individuals, distributed across four large families. HUT consisted of 70 degrees head-up table tilting while strapped to a tilt table. BP reactivity (deltaBP) was defined as BP during HUT minus BP while supine. Three PC extracted from the PCA were interpreted as 'general BP' (PC1), 'pulse pressure' (PC2) and 'BP reactivity' (PC3). Variance components methods were used to estimate the heritabilities of resting BP, HUT BP, deltaBP, as well as the three BP PC. Significant (P<0.05) heritabilities were found for all BP measurements, except for systolic deltaBP at 1 and 3 min, and diastolic deltaBP at 2 min. Significant genetic effects were also found for the three PC. Each of these orthogonal components is significantly influenced by somewhat different sets of covariates.

Adolescent↗
PubMed2001

Is principal components analysis necessary to characterise dietary behaviour in studies of diet and disease?

OBJECTIVE: To assess the relative ability of principal components analysis (PCA)-derived dietary patterns to correctly identify cases and controls compared with other methods of characterising food intake. SUBJECTS: Participants in this study were 232 endometrial cancer cases and 639 controls from the Western New York Diet Study, 1986-1991, frequency-matched to cases on age and county of residence. DESIGN: Usual intake in the year preceding interview of 190 foods and beverages was collected during a personal interview using a detailed food-frequency questionnaire. Principal components analysis identified two major dietary patterns which we labelled 'healthy' and 'high fat'. Classification on disease status was assessed with separate discriminant analyses (DAs) for four different characterisation schemes: stepwise DA of 168 food items to identify the subset of foods that best discriminated between cases and controls; foods associated with each PCA-derived dietary pattern; fruits and vegetables (47 items); and stepwise DA of USDA-defined food groups (fresh fruit, canned/frozen fruit, raw vegetables, cooked vegetables, red meat, poultry, fish and seafood, processed meats, snacks and sweets, grain products, dairy, and fats). RESULTS: In general, classification of disease status was somewhat better among cases (54.7% to 67.7%) than controls (54.0% to 63.1%). Correct classification was highest for fruits and vegetables (67.7% and 62.9%, respectively) but comparable to that of the other schemes (49.5% to 66.8%). CONCLUSIONS: Our results suggest that the use of principal components analysis to characterise dietary behaviour may not provide substantial advantages over more commonly used, less sophisticated methods of characterising diet.

Case-Control Studies↗