Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Principal Component Analysis”

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 19 recordsLinked to original sources
PubMed2004

Recovering dipole sources from scalp-recorded event-related-potentials using component analysis: principal component analysis and independent component analysis.

Principal component analysis (PCA) and independent component analysis (ICA) were examined in their ability to recover dipole sources from simulated data. Datasets of EEG segments were generated that contained cortical sources that were temporally overlapping or non-overlapping, and dipole sources with varying degree of spatial orthogonality. For temporal overlapping sources, both PCA and ICA resulted in components that required multiple-source equivalent current dipole models. The spatially overlapping sources affected the PCA method more than ICA, resulting in single PCA components in which all non-orthogonal sources were represented. For both PCA and ICA, dipole models with fixed-location dipoles successfully accounted for most of the variance in the component weights, even when the spatial or temporal overlap of the generating sources required multiple-dipole models.

Brain Mapping↗
PubMed2005

Gene selection for microarray data analysis using principal component analysis.

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure.

Gene Expression↗
PubMed2005

Complex-valued neural networks for nonlinear complex principal component analysis.

Principal component analysis (PCA) has been generalized to complex principal component analysis (CPCA), which has been widely applied to complex-valued data, two-dimensional vector fields, and complexified real data through the Hilbert transform. Nonlinear PCA (NLPCA) can also be performed using auto-associative feed-forward neural network (NN) models, which allows the extraction of nonlinear features in the data set. This paper introduces a nonlinear complex PCA (NLCPCA) method, which allows nonlinear feature extraction and dimension reduction in complex-valued data sets. The NLCPCA uses the architecture of the NLPCA network, but with complex variables (including complex weight and bias parameters). The application of NLCPCA on test problems confirms its ability to extract nonlinear features missed by the CPCA. For similar number of model parameters, the NLCPCA captures more variance of a data set than the alternative real approach (i.e. replacing each complex variable by two real variables and applying NLPCA). The NLCPCA is also used to perform nonlinear Hilbert PCA (NLHPCA) on complexified real data. The NLHPCA applied to the tropical Pacific sea surface temperatures extracts the El Niño-Southern Oscillation signal better than the linear Hilbert PCA.

Algorithms↗
PubMedDate not supplied

Evaluation of ground water monitoring network by principal component analysis.

Principal component analysis is a data reduction technique used to identify the important components or factors that explain most of the variance of a system. This technique was extended to evaluating a ground water monitoring network where the variables are monitoring wells. The objective was to identify monitoring wells that are important in predicting the dynamic variation in potentiometric head at a location. The technique is demonstrated through an application to the monitoring network of the Bangkok area. Principal component analysis was carried out for all the monitoring wells of the aquifer, and a ranking scheme based on the frequency of occurrence of a particular well as principal well was developed. The decision maker with budget constraints can now opt to monitor principal wells which can adequately capture the potentiometric head variation in the aquifer. This was evaluated by comparing the observed potentiometric head distribution using data from all available wells and wells selected using the ranking scheme as a guideline.

Data Interpretation, Statistical↗
PubMed2003

Partitioning large-sample microarray-based gene expression profiles using principal components analysis.

Principal components analysis (PCA) is useful for reproducing the total variation among hundreds or thousands of continuously-scaled variables with a much smaller number of unobservable variables called 'latent factors'. The CLUSFAVOR computer program was used to implement PCA for identifying groups of genes with similar expression profiles from a large number of genes used on DNA microarrays. This paper describes the principal components solution to the factor model of the correlation matrix R, calculation of eigenvalues and eigenvectors of R, extraction of factors, and calculation of factor loadings and identification of genes with similar loading patterns to construct groups of genes with similar expression profiles. With regard to extraction of factors, it was found that more than 90% of the total variance in input data could be accounted for by extracting factors whose eigenvalues exceed unity. Bipolar factors containing strong positive and negative loadings can also be used for identifying two unique groups of genes, since expression profiles of genes that load positive are unlike expression profiles of genes that load negative on the same factor. While PCA does not provide the absolute answer to a multidimensional problem, it nevertheless can provide a heuristic with which natural groupings of genes with similar expression profiles can be assembled. While cluster analysis essentially generates a single dendogram (tree branch) containing every gene in the input data, PCA can be used to assemble gene expression profiles that strongly correlate with the latent factors accounting for a majority of total variance. Example results for CLUSFAVOR computer program runs are provided.

Gene Expression Profiling↗
PubMed2005

Feature detection in motor cortical spikes by principal component analysis.

Principal component analysis was performed on recorded neural spike trains in rats' motor cortices when rats were involved in real-time control tasks using brain-machine interfaces. The rat with implanted microelectrode array was placed in a conditioning chamber, but freely moving, to decide which one of the two paddles should be activated to shift the cue light to the center. It is found that the principal component feature vectors revealed the importance of individual neurons and windows of time in the decision making process. In addition, one of the first principal components has much higher discriminative capability than others, although it represents only a small percentage of the total variance in the data. Using one to six principal components with a Bayes classifier achieved classification accuracy comparable to that obtained by a more sophisticated high performance support vector classifier.

Action Potentials↗
PubMed1987

Relationships between physico-chemical parameters and microbial groups in Manchego and Burgos cheeses studied by principal component analysis.

Principal component analysis was used to examine the correlations between two sets of variables, one representing physicochemical characteristics (pH, aw and NaCl, moisture and fat content) of Manchego (36 samples) and Burgos (36 samples) cheeses, and the other representing counts of several microbial groups (mesophiles, psychrotrophs, lactic acid bacteria, coliforms, enterococci, staphylococci and molds and yeasts). Thermonuclease content was also included. In addition to the expected relationships (NaCl content, moisture, aw, etc.), significant correlations between some compositional characteristics and levels of certain microorganisms were found. These correlations were dependent on the type of cheese. Thermonuclease content was positively related to enterococci and ripening (only in Manchego cheese). In contrast to former observations, no relationships were observed between coliforms and enterococci counts.

Cheese↗
PubMedDate not supplied

Interpretation of laboratory results using multidimensional scaling and principal component analysis.

Principal component analysis (PCA) and multidimensional scaling (MDS) are a set of mathematical techniques which uncover the underlying structure of data by examining the relationships between variables. Both MDS and PCA use proximity measures such as correlation coefficients or Euclidean distances to generate a spatial configuration (map) of points where distances between points reflect the relationship between individuals with their underlying set of data. Multidimensional scaling, when compared to PCA, gives more readily interpretable solutions of lower dimensionality and does not depend on the assumption of a linear relationship between variables. Both MDS and PCA were applied to electrolyte profiles of patients with acute renal failure and patients without apparent disease. The MDS was superior to PCA in separating renal patients from normal patients. The one-dimensional and two-dimensional solutions of MDS and PCA were compared.

Acute Kidney Injury↗
PubMed1996

Face processing: human perception and principal components analysis.

Principal components analysis (PCA) of face images is here related to subjects' performance on the same images. In two experiments subjects were shown a set of faces and asked to rate them for distinctiveness. They were subsequently shown a superset of faces and asked to identify those that had appeared originally. Replicating previous work, we found that hits and false positives (FPs) did not correlate: Those faces easy to identify as being "seen" were unrelated to those faces easy to reject as being "unseen." PCA was performed on three data sets: (1) face images with eye position standardized, (2) face images morphed to a standard template to remove shape information, and (3) the shape information from faces only. Analyses based on PCA of shape-free faces gave high predictions of FPs, whereas shape information itself contributed only to hits. Furthermore, whereas FPs were generally predictable from components early in the PCA, hits appeared to be accounted for by later components. We conclude that shape and "texture" (the image-based information remaining after morphing) may be used separately by the human face processing system, and that PCA of images offers a useful tool for understanding this system.

Adult↗
PubMed2006

Approaching PM(2.5) and PM(2.5-10) source apportionment by mass balance analysis, principal component analysis and particle size distribution.

A chemical characterization was carried out for PM(2.5) and PM(2.5-10) samples collected in a suburban area and the concentrations of 12 elements were determined in 8 size segregated fractions using a Berner Impactor. Two main objectives were proposed in this work: 1) to test for closure among chemical and gravimetric measurements of PM(2.5) and PM(2.5-10) and 2) evaluate the performance of Multilinear Regression Analysis (MLRA) and Mass Balance Analysis (MBA) in the determination of source contribution to Particulate Matter (PM) concentrations. The fraction unaccounted for by chemical analysis comprised on average 17% and 34% of gravimetric PM(2.5) and PM(2.5-10), respectively. The lack of closure in PM(2.5) and PM(2.5-10) mass (i.e., constituent concentrations not adding up to gravimetrically measured) could partly result from the presence of water associated with particles and errors in the estimation of unmeasured species. MLRA and MBA showed very similar results for the temporal variation of the source contributions. However, quantitatively important discrepancies could be observed, principally due to the lack of mass closure in PM(2.5) and PM(2.5-10). Both methods indicated that the major PM(2.5) aerosol mass contributors included secondary aerosol and vehicle exhaust. In the coarse fraction, marine and mineral aerosol contributions were predominant.

Aerosols↗
PubMed2006

Spectral simulation study on the influence of the principal component analysis step on principal component regression.

Principal component regression (PCR) is unique in that the principal component analysis (PCA) step is explicitly involved in the central part of the method. In the present paper, the PCA part is examined in order to study the influence of noise in spectra on PCR by spectral simulation. It has been suggested, as a result, that PCR calibration would have a large inaccuracy when the estimated number of basis factors analyzed by the eigenvalue method is less than that by cross-validation, which was studied by use of synthesized spectra. This instability is because the minute noise is largely enhanced by the PCA calculation via the normalization of loadings. At the same time, the noise enhancement by PCA has also been characterized to influence the estimation of basis factors.

Algorithms↗
PubMed2001

Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk.

Quantitative descriptive analysis was used to describe the key attributes of nine ultrapasteurized (UP) milk products of various fat levels, including two lactose-reduced products, from two dairy plants. Principal components analysis identified four significant principal components that accounted for 87.6% of the variance in the sensory attribute data. Principal component scores indicated that the location of each UP milk along each of four scales primarily corresponded to cooked, drying/lingering, sweet, and bitter attributes. Overall product quality was modeled as a function of the principal components using multiple least squares regression (R2 = 0.810). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring UP fluid milk product attributes that are important to consumers.

Animals↗
PubMed1995

Harmonicity and anharmonicity in protein dynamics: a normal mode analysis and principal component analysis.

A comparison of a normal mode analysis and principal component analysis of a 200-ps molecular dynamics trajectory of bovine pancreatic trypsin inhibitor in vacuum has been made in order to further elucidate the harmonic and anharmonic aspects in the dynamics of proteins. An anharmonicity factor is defined which measures the degree of anharmonicity in the modes, be they principal modes or normal modes, and it is shown that the principal mode system naturally divides into anharmonic modes with peak frequencies below 80 cm-1, and harmonic modes with frequencies above this value. In general the larger the mean-square fluctuation of a principal mode, the greater the degree of anharmonicity in its motion. The anharmonic modes represent only 12% of the total number of variables, but account for 98% of the total mean-square fluctuation. The transitional nature of the anharmonic motion is demonstrated. The results strongly suggest that in a large subspace, the free energy surface, as probed by the simulation, is approximated by a multi-dimensional parabola which is just a rescaled version of the parabola corresponding to the harmonic approximation to the conformational energy surface at a single minimum. After 200 ps, the rescaling factor, termed the "normal mode rescaling factor," has apparently converged to a value whereby the mean-square fluctuation within the subspace is about twice that predicted by the normal mode analysis.

Animals↗
PubMed1999

Obstructive sleep apnea: a principal component analysis.

A principal component analysis was performed on the cephalometric variables of 100 male obstructive sleep apnea (OSA) patients. Thirty cephalometric variables of cervicocraniofacial skeletal morphology were reduced to 8 principal components (PCs), which described 83.2% of the total variance. Sixteen cephalometric variables of hyoid bone position and head posture were reduced to 4 PCs, which described 85.5% of the total variance. Twenty cephalometric variables of upper airway soft tissue were reduced to 7 PCs, which described 83.7% of the total variance. These PCs described the actual characteristics of the OSA patients examined. For further analysis of PCs, stepwise multiple regression analysis was chosen. Two dependent variables of interest are the minimal distance of posterior pharyngeal airway space (PASmin) and the Apnea-Hypopnea Index (AHI). Seven PCs accounted for 79.4% of the variance of PASmin and 3 PCs accounted for 37.6% of the variance of AHI. Both principal component analysis and multiple regression analysis provide multivariate data analysis that is very useful in sorting out and clarifying the complexity of the interrelated cervicocraniofacial skeletal morphology and upper airway soft tissue in OSA patients.

Adult↗
PubMedDate not supplied

Exploring northeast African metric craniofacial variation at the individual level: a comparative study using principal components analysis.

A principal components analysis was carried out on male crania from the northeast quadrant of Africa and selected European and other African series. Individuals, not predefined groups, were the units of study, while nevertheless keeping group membership in evidence. The first principal component seems to largely capture "size" variation in crania from all of the regions. The same general morphometric trends were found to exist within the African and European crania, although there was some broad separation along a cline. Anatomically, the second principal component captures predominant trends denoting a broader to narrower nasal aperture combined with a similar shape change in the maxilla, an inverse relation between face-base lengths ("projection") and base breadths, and a decrease in anterior base length relative to base breadth. The third principal component broadly describes trends within Africa and Europe: specifically, a change from a combination of a relatively narrower face and longer vault, to one of a wider face and shorter vault; it shows the northeast quadrant Africans along a cline with the other Africans. Stated in relative terms, the northeastern Africans tend to exhibit narrower bases in relationship to more projecting faces, and broader nasal areas than Europeans, although there is range of variation. Relative to the other African groups, they have narrower nasal areas and narrower faces in relationship to vault length. The crania from the northeast quadrant of Africa collectively demonstrate the greatest pattern of overlap with both Europeans and other Africans. Variation was found to be high in all series but greatest in the African material as a whole. Individuals from different geographical regions frequently plotted near each other, revealing aspects of variation at the level of individuals that is obscured by concentrating on the most distinctive facial traits once used to construct "types." The high level of African interindividual variation in craniometric pattern is reminiscent of the great level of molecular diversity found in Africa. These results, coupled with those of Y chromosome studies, may help generate hypotheses concerning the length of time over which recent craniometric variation emerged in Africa.

Africa↗
PubMed2004

Use of principal component analysis in the frequency domain for mapping electroencephalographic activities: comparison with phase-encoded Fourier spectral analysis.

Principal component analysis (PCA) can separate multichannel electroencephalographic (EEG) epochs into linearly independent (temporally and spatially noncorrelated) components. Results of PCA include component time-series waveforms and factors representing the contribution of each component to each electrode; these factors may be displayed as contour maps representing the topographic distribution of each component. However, PCA often does not achieve the most useful separation of components. PCA may be performed in the frequency domain to potentially improve results. After inspecting principal components of the frequency spectra, spectral values in a selected frequency range are multiplied by a chosen factor to emphasize (or de-emphasize) these frequencies and PCA is redone, promoting the separation of different frequencies into different components. Phase-encoded Fourier spectral analysis (PEFSA) uses multichannel complex Fourier spectra (amplitude and phase) to obtain positive or negative (phase-encoded) potentials at each electrode for any selected frequency. These may be displayed as a contour map representing the topographic distribution of the selected frequency. Applying both techniques, we found that EEG activities of differing frequency were readily separated by PEFSA, while standard PCA often mixed activities with different frequencies into a single component. However, frequency-domain PCA gave a component whose spatial distribution well matched PEFSA results. PCA is superior to PEFSA for separating activities with overlapping frequencies but differing spatial distributions. Preservation of phase information is an advantage of PEFSA and PCA over topographic maps that represent only amplitude (or power) at a given frequency. PCA or PEFSA maps can serve as a starting point for source localization.

Brain↗
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↗
PubMed2006

Classification of soil samples according to their geographic origin using gamma-ray spectrometry and principal component analysis.

A principal component analysis (PCA) was used for classification of soil samples from different locations in Serbia and Montenegro. Based on activities of radionuclides ((226)Ra, (238)U, (235)U, (40)K, (134)Cs, (137)Cs, (232)Th and (7)Be) detected by gamma-ray spectrometry, the classification of soils according to their geographical origin was performed. Application of PCA to our experimental data resulted in satisfactory classification rate (86.0% correctly classified samples). The obtained results indicate that gamma-ray spectrometry in conjunction with PCA is a viable tool for soil classification.

Geography↗