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At least 217 records · Page 12Linked to original sources
PubMed2000

Principal component analysis of large layer density in Compton scattering measurements

A multivariate approach based on Principal Component Analysis (PCA) was used to analyze the energy distribution of n Monte Carlo simulated Compton scattered photon spectra describing the electron density of large layers. Three to five layers with different density distribution were used to test the algorithm; each layer was obtained by collecting 25 Compton spectra coming from sensitive volumes (SVs) where the complementary conditions of high and low density were realized (respectively full and void SV). The density variation inside a single layer is described by a two principal components (PCs) linear model that depicts the electron density of each SV: the layer density distribution appears to be correctly described even in the presence of very low signal-to-noise Compton spectra. Density profiles for layers at different depths were comparatively analyzed in order to show that, at least within one mean-free-path distance, it is possible to describe the layer density distribution by the PCA without any correction for the beam attenuation.

Journal Article↗
PubMed1981

Principal components and varimax-rotated components in event-related potential research: some remarks on their interpretation.

Some properties of principal components analysis (PCA) and simple structure rotation, which are relevant to the study of event-related potentials (ERPs), were examined both in theory and with simulated data. The analysis casts some doubt on whether it is useful and desirable to rotate the loading pattern of a PCA of ERPs to simple structure. The evidence presented is more in favour of the unrotated solution. In particular we were able to demonstrate with simulated data that a solution rotated to simple structure may lead to false conclusions about the functional independence of ERP peaks. Beyond this, the more general question is asked of whether principal components should be accepted as basic waveforms, i.e. as physiologically meaningful entities which, for example, represent different ERP generators.

Contingent Negative Variation↗
PubMed2001

Principal component analysis for clustering gene expression data.

MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.

Algorithms↗
PubMed2005

Identification of copper sources in urban surface waters using the principal component analysis based on aquatic parameters.

The goal of this work was to identify the sources of copper loads in surface urban waters using principal component analysis under the aquatic parameters data evaluation approach. Water samples from the Irai and Iguacu rivers were collected monthly during a 12-month period at two points located upstream and downstream of a metropolitan region. pH, total alkalinity, dissolved chloride, total suspended solids, dissolved organic matter, total recoverable copper, temperature, and precipitation data provided some reliable information concerning the characteristics and water quality of both rivers. Principal component analysis indicated seasonal and spatial effects on copper concentration and loads in both environments. During the rainy season, non-point sources such as urban run-off are believed to be the major source of copper in both cases. In contrast, during the lower precipitation period, the discharge of raw sewage seems to be the primary source of copper to the Iguacu River, which also exhibited higher total metal concentrations.

Cities↗
PubMed2005

Morphological classification of mandibular dental arch forms by correlation and principal component analyses.

To evaluate the morphology of dental arches, 53 (male: 29, female: 24) paired casts having normal dentitions and occlusion were selected from 396 (age: 18 to 26 years old; male: 257, female: 139) sets of dental study models. The mandibular dentitions were preliminarily classified as square, round-square, round and round V-shaped arches based on the conventional morphological descriptions. Midpoints of the incisor edge (I1R, I1L, I2R, & I2L), summits of the cuspids (CR & CL), buccal cusps of the premolars (P1R, P1L, P2R, & P2L), mesial buccal cusps of the first and second molars (M1R, M1L, M2R, & M2L), and the midpoint (A) of line I1R-I1L were designated as reference points. From A, let a vertical line intersected line M2R-M2L at reference point B. The line A-B intersected CR-CL at reference point E. We evaluated 1) the protrusion of the cuspids by 1. angle I2R-CR-P1R (angle R) + angle I2L-CL-P1L (angle L); 2) the curvature of the anterior teeth by 2. (A-B)/(CR-CL), 3. (180 degrees-angle(CR-A-CL), and 4. (A-E)/(CR-CL); 3) the length to width ratio of the dental arch by 5. (A-B)/(M2R-M2L); 4) the degree of roundness of the mandibular arch by estimation of 6. (rtheta5 - rtheta4)R + (rtheta5 - rtheta4)L; and 5) an item 7. for the differentiation of type I and type II round-square arches by relating the bilateral contour and position of break line P1-P2-M1-M2 (i) to line P1-M2 (ii). The data of items 1., 2., 3., 4., 5., and 6. were further standardized and summarized into three essential principal components: 1) the curvature of the anterior teeth, 2) the curvilinear contour of the dental arch, and 3) the length-to-width ratio of the dental arch. The results indicated that: 1) 36 cases (67.9%) of the mandibular dentitions were round-square arches which showed no prominent principal component. 11 cases (20.8%) were square arches and 6 cases (11.3%) were round V-shaped arches; no round arches was found in mandibular dentitions. 2) Statistical analysis indicated significant differences of items 3., 4. and 6. in various mandibular arches (Student's t-test). 3) By examination of the three principal components, significant differences of item 5. between the round V-shaped arches and square and round-square mandibular arches were evident (Student's t-test). The present study elucidated that morphology of the mandibular arch was determined by a parameters representing the curvature of anterior teeth (composed of items 2., 3. and 4., and another parameter (item 6.) representing roundness of the mandibular arch.

Adolescent↗
PubMed2004

Small interacting peptides. Part II: Interaction of cyclohexapeptides with immobilised model peptides. Comparison of infrared investigations, principal components analysis and force field calculations.

The interaction of cyclic peptides with surface-bound model peptides was investigated by ATR-FTIR spectroscopy, principal components analysis and force field calculations. Information about the interacting functional COOH, COO-, and NH3+ groups and the peptide backbone was gained through a set of cyclohexapeptides (seven of the type c(X1KX2KX3K) (K = L-lysine) and one of the type c(X1KX2KX3k) (k = D-lysine), which are interacting with L-arginine- or tripeptide-coated Si-ATR crystals. All measurements were performed in aqueous solutions. Spectra evaluation in the range 1800-1500 cm(-1) was done by band and principal components analysis (PCA). Only adsorbed molecules were present in these spectra. The coatings were investigated by ATR-FTIR spectroscopy too in order to characterise their functional groups. Based on this knowledge, the spectra of the interacting partners could be evaluated in relation to cyclohexapeptides and coatings. As a result, it was possible to identify the distinct differences in the bonding behaviour of the various peptides.

Amino Acid Sequence↗
PubMed1990

Intergroup and intrasubject principal component analysis of event-related potentials.

The purpose of this paper is to show that the familiar principal component analysis (PCA) of event-related potentials is identical to an easily formulated least squares method. This correspondence permits interpretation of several criticisms of PCA and clearer presentation of its strengths and shortcomings. Because data analysis based on PCA compares amplitudes of empirically derived components, it is necessary that the shape of the components be similar under the experimental conditions. We present and illustrate a statistical method for comparison of principal components across groups and between conditions within subjects.

Acoustic Stimulation↗
PubMed1992

Principal component analysis and exploratory factor analysis.

In this paper we compare and contrast the objectives of principal component analysis and exploratory factor analysis. This is done through consideration of nine examples. Basic theory is presented in appendices. As well as covering the standard material, we also describe a number of recent developments. As an alternative to factor analysis, it is pointed out that in some cases it may be useful to rotate certain principal components if and when that is appropriate.

Animals↗
PubMed1999

Spectral quantitation by principal component analysis using complex singular value decomposition.

Principal component analysis (PCA) is a powerful method for quantitative analysis of nuclear magnetic resonance spectral data sets. It has the advantage of being model independent, making it well suited for the analysis of spectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in phase or have implemented iterative methods to analyze spectra that are not perfectly phased. However, improper phasing or imperfect convergence of the iterative methods has resulted in systematic errors in the estimation of peak areas with PCA. Presented here is a modified method of PCA, which utilizes complex singular value decomposition (SVD) to analyze spectral data sets with any amount of variation in spectral phase. The new method is shown to be completely insensitive to spectral phase. In the presence of noise, PCA with complex SVD yields a lower variation in the estimation of peak area than conventional PCA by a factor of approximately 2. The performance of the method is demonstrated with simulated data and in vivo 31P spectra from human skeletal muscle.

Analysis of Variance↗
PubMed1999

Similarity relations of DNA and RNA polymerases investigated by the principal component analysis of amino acid sequences.

The principal component analysis based on the physicochemical properties of amino acid residues is applied to DNA and RNA polymerases to assign the sequence motifs for the polymerization activities of these proteins. After the reconfirmation of the sequence motifs of families A and B of DNA polymerases indicated previously, it elucidates the sequence motifs for the polymerization activity of DNA polymerase III (family C) by the similarity to the polymerization center of multimeric DNA dependent RNA polymerases. This identification proceeds to clarify the sequence motifs for polymerization activities of primases; eukaryotic and archaebacterial primases carry motifs similar to those of family C, while the motifs of eubacterial primase fall into the category of the motifs in family B DNA polymerases such as alpha, delta, epsilon and II. This finding means that DNA dependent RNA polymerases are also divided into groups corresponding to three families, A, B and C, because the monomeric DNA dependent RNA polymerases in phages are reconfirmed to carry sequence motifs similar to those of family A DNA polymerases. Furthermore, the three families of polymerization motifs are found to fall within the variation range of polymerization motifs displayed by many RNA dependent RNA polymerases, suggesting a close evolutionary relation between them. The sequence motifs for polymerization activities of reverse transcriptase and telomerase seem to be the intermediate between family A DNA polymerase and some RNA dependent RNA polymerases, e.g., from Leviviridae. On the contrary, the sequence fragments similar to the nucleotidyltransferase superfamily including DNA polymerase beta are not found in any RNA dependent RNA polymerase, suggesting their other lineage of polymerization motifs.

Amino Acid Sequence↗
PubMed2002

Analysis of the photoplethysmographic signal by means of the decomposition in principal components.

We study the plethysmographic signal using principal component analysis (PCA). By decomposing the signal using this method, we are able to regenerate it again, preserving in the process the functional relationships between the components. We have also found the relative contributions of each specific component to the signal. First return maps have been made for the series of residues of the decomposition. Further analysis using spectral methods has shown that the residues have a 1/f -like structure, which confirms the presence and conservation of this component in the signal and its relative independence with respect to the oscillating component (Hernández et al 2000 Rev. Cubana Inform. Medica 1 5). Our conclusions are that: (i) PCA is a good method to decompose the plethysmographic signal since it preserves the functional relationships in the variables, and this could be potentially useful in finding new clinically relevant indices; (ii) the 1/f process of the plethysmographic signal is preserved in the residues of the decomposed signal when PCA is used; (iii) clinically relevant parameters can potentially be obtained from photoplethysmographic signals when PCA is used.

Adult↗
PubMed1986

New indices for thyroid functional status, hormone binding, and peripheral hormone metabolism. Principal component analysis of 24,000 clinical data.

Principal component analysis of three thyroid function tests, thyroxine (T4), 3,5,3'-triiodothyronine (T3), and T3 uptake (T3U), was done using 24,000 data obtained from patients with a wide range of pathophysiologic conditions related to the thyroid. The three component scores were obtained as follows: Z1 = 2.62 square root T4 + 0.63 square root T3 + 3.18 square root T3U - 32.43; Z2 = 0.91 square root T4 + 0.24 square root T3 - 4.68 square root T3U + 20.14; and Z3 = 3.94 square root T4 + 0.95 square root T3 + 0.18 square root T3U - 1.53 (T4 micrograms/dL, T3 ng/dL, T3U%). The first component (Z1) represents an apparent axis to the direction of thyroid functional status. It provides a new metabolic index putting conventional free T4 and free T3 indices together. The second component (Z2) was found to be a sensitive indicator of abnormal hormone binding. It showed a close correlation with serum concentration of thyroxine-binding globulin. The third component (Z3) represents the degree of T3 predominance over T4. Computation of these scores will facilitate the diagnosis of atypical cases in which hyper-or hypothyroidism is complicated by abnormal peripheral hormone binding and/or metabolism.

Humans↗
PubMed1991

Principal component analysis of various respiratory function tests: the relationship between factor score and severity of pulmonary circulatory disorder in chronic obstructive pulmonary disease.

The relationship between pulmonary haemodynamics and values of various respiratory function tests was studied in patients with mild chronic obstructive pulmonary disease (COPD) and the following results were obtained. (1) The value of mean pulmonary artery pressure (mPAP) at rest in COPD patients was slightly elevated to 19.4 mmHg on average compared with our control value of less than 18 mmHg. (2) Analysis of the data of 11 routine respiratory function tests in 88 COPD patients extracted two principal components: an index of the expiratory function and an index for overinflation of the lung. (3) In individual patients, mPAP expressed the severity of pulmonary circulatory disorder roughly inverse to the factor score of the first principal component (index of expiratory function) but not to that of the second principal component (overinflation of the lung). (4) Discriminant analysis was performed in all 88 COPD patients according to data from the 11 respiratory function tests. The probability of mPAP being above or below 18 mmHg was 18.2%. (5) The relationship between the predicted EPOI value and the factor score was similar to that between mPAP and the factor score. EPOI (exercise pulmonary artery pressure-oxygen consumption index) was calculated with the following equation: EPOI = (mPAPex#-mPAPrest)/[VO2ex-VO2rest)/BSA##). On the other hand, EPOIpred was calculated with the prediction equation obtained from multiple linear regression (dependent variable; EPOI, independent variable; respiratory function).

Adult↗
PubMed2006

Evaluation of ischemic injury of the cardiac tissue by using the principal component analysis of an epicardial electrogram.

Monitoring and control of the heart tissue viability is of crucial importance during heart surgery operations. In most cases the heart tissue suffers from an ischemic injury that causes a decrease in the velocity of electrical excitation propagation in it and influences the shape of the excitation wave front that spreads over the injured area. It is reflected in a more complex shape of the registered epicardial electrogram as compared to normal. A method for quantitative evaluation of the complexity of the shape of the epicardial electrogram based on the principal component analysis is here proposed for evaluation of the ischemic injury of the cardiac tissue. A minimal, yet sufficient, number of the principal components (the optimal basis functions) for truncated expansion of the epicardial electrogram signals could be used as an estimate of signal complexity. The method for determination of such a minimal, yet sufficient, number of principal components were developed by using epicardial electrograms registered during in situ experiments on dogs in which local ischemia was evoked by ligation of a coronary vessel.

Animals↗
PubMed1995

The usefulness of the Laplacian in principal component analysis and dipole source localization.

Evoked potentials are difficult to analyze because multiple sources are active simultaneously. Principal component analysis and dipole localization are two techniques that have been used to disentangle overlapping sources. Both of these techniques have problems. Principal component analysis suffers from a rotation ambiguity. Dipole localization suffers from biases when the model used to derive the sources from the scalp potentials is misspecified. Using computer simulations we demonstrate that by applying both of these techniques to the Laplacian of the voltages rather than to the raw voltages the problems associated with the two techniques are reduced. Computer programs for the analyses are presented in an Appendix.

Brain Mapping↗
PubMed1997

Mapping of ventricular repolarization potentials in patients with arrhythmogenic right ventricular dysplasia: principal component analysis of the ST-T waves.

BACKGROUND: Nonuniform recovery of ventricular excitability has been demonstrated to facilitate the reentry circuits leading to the development of ventricular tachyarrhythmias. This can also occur in arrhythmogenic right ventricular dysplasia (ARVD). In fact, in patients with ARVD, abnormalities of ventricular repolarization are often observed on 12-lead ECGs, but their predictive value for the occurrence of malignant arrhythmias is yet to be established. Because body-surface potential mapping has been proved to be useful for the detection of heterogeneities in ventricular recovery even though they are not revealed by conventional 12-lead ECGs, we attempted to analyze repolarization potentials on the entire chest surface to find abnormalities that can be predictive of ventricular arrhythmias. METHODS AND RESULTS: Body-surface potential maps were recorded from 62 anterior and posterior thoracic leads in 22 patients affected by ARVD, 9 with episodes of sustained ventricular tachycardias (VT) and 13 without. Thirty-five healthy subjects were also studied as control subjects. The 62 chest ECGs were simultaneously recorded, digitally converted at a rate of 2000 Hz, and stored on a hard disk of a body-surface mapping computer system. In each subject, the QRST integral map was obtained by calculating at each lead point the algebraic sum of all instantaneous potentials, from the QRS onset to the T-wave end, multiplied by the sampling interval. In most ARVD patients, we observed a larger-than-normal area of negative values on the right anterior thorax. This abnormal pattern could be explained by a delayed repolarization of the right ventricle. Nevertheless, it was not related to the occurrence of VT in our patient population. To detect minor heterogeneities of ventricular repolarization, the principal component analysis was applied to the 62 ST-T waves recorded in each subject. We assumed that a low value of the first or of the first three components (components 1, 2, and 3) indicates a greater-than-normal variety of the ST-T waves, a likely expression of a more complex recovery process. The mean values of the first three components were not significantly different in ARVD patients and control subjects. Nevertheless, considering the two subsets of patients with and without VT, the values of component 1, components 1 + 2, and component 1 + 2 + 3 were significantly lower in the group of ARVD patients with VT. Values of component 1 < 69% (equal to 1 SD below the mean value for control subjects) were found in 6 of 9 VT patients and in 1 patient without VT (sensitivity, 67%; specificity, 92%). A low value of component 1 was the only variable significantly associated with the occurrence of VT. CONCLUSIONS: Principal component analysis provides a better quantitative assessment of the complexity of repolarization than other ECG measurements. When applied to ARVD patients, principal component analysis of the ST-T waves recorded from the entire chest surface revealed abnormalities not detected by conventional ECG that can be considered indexes of arrhythmia vulnerability.

Adolescent↗
PubMed1994

Principal component analysis of Fourier transform infrared and/or circular dichroism spectra of proteins applied in a calibration of protein secondary structure.

Gaining information on the secondary structure of a protein from its spectra is presented as a calibration problem. The secondary structures known from X-ray studies and the spectra of 21 proteins are represented by a linear model. Fourier transform infrared (FTIR) spectra from 1700 to 1600 cm-1, circular dichroism (CD) spectra from 178 to 260 nm, and combined spectra are used; the secondary structure classes of interest are alpha-helices, antiparallel beta-sheets, parallel beta-sheets, beta-turns, and "other." The calibration is solved in two steps: (i) the dependencies between the structures and the spectra of reference proteins are found using the least-squares estimator, and (ii) the secondary structure of a protein is predicted from its spectra using the information gained in the first step and principal component analysis. The problem of information content of the reference spectra is analyzed using the linearly independent pieces of information, the so-called principal components, provided by singular value decomposition. Attention is paid to a number of the principal components sufficient for the prediction, which may be less than the total number. A relative estimable parameter is used to determine unambiguously the number of the components corresponding to the minimum mean square error of the predictor. The analysis gives the solutions to this linear calibration relevant to the underlying protein problem, thus reducing subjective assessments as well as computations.

Calibration↗
PubMedDate not supplied

Regional cerebral blood volume measured by dynamic susceptibility contrast MR imaging in Alzheimer's disease: a principal components analysis.

Dynamic susceptibility contrast (DSC) MRI is an alternative to positron emission tomography (PET) and single photon emission computed tomography (SPECT) for the evaluation of cerebral hemodynamics in patients with Alzheimer's disease. DSC MRI allows the construction of high resolution images of cerebral blood volume (CBV) without the use of radionuclides or ionizing radiation. In this study, DSC MRI data were collected from 16 patients with probable Alzheimer's disease and 16 age-matched control subjects. Characteristic patterns of regional CBV variation were found using principal component analysis. Three such patterns were identified: a global variation pattern, an anterior-to-posterior CBV gradient, and a temporoparietal pattern. Group differences in the principal component scores associated with the global and temporoparietal patterns (P = .08 and P = .007, respectively) suggest that these deficits reflect characteristic CBV abnormalities in Alzheimer's disease. Using only these two scores, the Alzheimer's disease group was classified with a sensitivity of 81% and a specificity of 88%. Additionally, disease severity, as measured by the Mini-Mental State Examination (MMSE), was correlated significantly with the third principal component score (Pearson's r = .50, P = .05).

Aged↗