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PubMed2005

Negative impact of noise on the principal component analysis of NMR data.

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.

Adenosine Triphosphate↗
PubMed1987

Individualized principal component analysis of endocrine circannual variability.

The technique of principal component (PC) analysis (PCA) of multivariate observations is a method that allows dimension reduction of multivariate data for further analysis. It is here introduced as a means of selecting chronobiologically important variables that can be further studied by an analysis of variance. The use of PCA is illustrated for a study of major temporal sources of human endocrine variability. Contributions to temporal variability by seven steroidal and six nonsteroidal hormones are compared in samples available at 100-min intervals for 24 hr in three seasons for each of three clinically healthy individuals: an adolescent woman, a menstrually cycling woman, and a postmenopausal woman. On an individualized basis, it is ascertained that the first principal component, a new variable, is primarily determined by steroids and that PCA can single out variables displaying interseasonal (circannual) differences validated as statistically significant by a subsequent analysis of variance. The variables here scrutinized and identified as contributing to the PC, however, need not all differ with statistical significance along the scale of the seasons. The steroids contributing the first principal component are DHEA-S and an estrogen in all three individuals studied, cortisol and aldosterone in two of them, and 17-OH progesterone in one case.

Adolescent↗
PubMed1987

Principal component analysis for the identification of pollution sources in mussel survey by trace metals.

Principal component analysis has been applied to analyze the correlation matrix obtained from a 8 X 43 data matrix. The 8 trace metals are Mn, Co, Ni, Cu, Zn, Cd, Hg, Pb, which are contained in the soft part of mussels (Mytilus galloprovincialis Lamarck). Mussels were sampled from two sites in the Gulf of Trieste. In both samples, 76-78% of the total variance is explained by the four principal components. The orthogonally rotated factor matrix indicates that Co and Ni are bonded to the first principal component and Cd and Pb to the first (site 2) or second principal component (site 1). The origin of trace metals in the soft part of mussels from the Gulf of Trieste is discussed.

Animals↗
PubMedDate not supplied

Chemometric differentiation of raw and commercial milk by trace elements using principal component analysis.

Nine trace elements (Cr, Mn, Fe, Ni, Cu, Zn, Mo, Cd, and Pb) were determined in the dissolved ash of 36 samples of raw milk. The distribution of the concentration of each element was first investigated by means of a test of normality. The matrix of the correlation between the concentrations of the elements was then used as a starting matrix for principal component analysis. Nine variables were reduced to four principal components, accounting for 75% of the total variance. The biophilic elements Mn-Fe and Cu-Mo were positively associated with the first two principal components, while Cr was correlated to the third and Ni and Cd with the fourth principal component. Pb and Zn are both negatively correlated to the first principal component. Comparison with 42 samples of a commercial milk, by using a two-dimensional plot of the principal component scores, rendered possible the differentiation between raw and commercial milk.

Animals↗
PubMed1983

Principal components for allometric analysis.

Logarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables. Interpretation is logically limited to the variables included in analysis. Principal components analysis depends also on relatively uniform intercorrelations; given this, it serves satisfactorily as a method for summarizing many bivariate combinations. Unmodified major principal component coefficients cannot represent scaling to body weight; rather, they represent scaling to a composite size vector which usually is highly correlated with body size or weight but has an unspecified allometry. Thus, the concepts of proportionality and of isometry must be kept distinct.

Animals↗
PubMed2006

New method for spectral data classification: two-way moving window principal component analysis.

Two-way moving window principal component analysis (TMWPCA), which considers all possible variable regions by using variable and sample moving windows, is proposed as a new spectral data classification method. In TMWPCA, the similarity between model function and the index obtained by variable and sample moving windows is defined as "fitness". For each variable region selected by a variable moving window, the fitness is obtained through the use of a model function. By maximizing the fitness, an optimal variable region can be searched. A remarkable advantage of TMWPCA is that it offers an optimal variable region for the classification. To demonstrate the potential of TMWPCA, it has been applied to the classification of visible-near-infrared (Vis-NIR) spectra of mastitic and healthy udder quarters of cows measured in a nondestructive manner. The misclassification rate of TMWPCA has been compared with those of other chemometric methods, such as principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and principal discriminant variate (PDV). TMWPCA has yielded the lowest misclassification rate. The result indicates that TMWPCA is a powerful tool for the classification of spectral data.

Journal Article↗
PubMed1994

Harmonic and anharmonic aspects in the dynamics of BPTI: a normal mode analysis and principal component analysis.

A comparison is made between a 200-ps molecular dynamics simulation in vacuum and a normal mode analysis on the protein bovine pancreatic trypsin inhibitor (BPTI) in order to elucidate the dual aspects of harmonicity and anharmonicity in the dynamics of proteins. The molecular dynamics trajectory is analyzed using principal component analysis, an effective harmonic analysis suited for comparison with the results from the normal mode analysis. The results suggest that the first principal component shows qualitatively different behavior from higher principal components and is associated with apparent barrier crossing events on an anharmonic conformational energy surface. The higher principal components appear to have probability distributions that are well approximated by Gaussians, indicating harmonicity. Eliminating the contribution from the first principal component reveals a great deal of correspondence between the 2 methods. This correspondence, however, involves a factor of 2, as the variances of the distribution of the higher principal components are, on average, roughly twice those found from the normal mode analysis. A model is proposed to reconcile these results with those from previous analyses.

Aprotinin↗
PubMed1982

On-line classification of arterial stenosis severity using principal component analysis applied to Doppler ultrasound signals.

Principal component analysis is a powerful method of feature extraction which can be applied to continuous-wave Doppler waveforms. A microprocessor system for the on-line calculation of the coefficients of principal components has been devised and tested in an experimental model. Doppler waveforms were obtained from positions distal to stenoses of known severity implanted in the iliac arteries of three dogs and classified into one of four groups. By reference to data from a previous series of experiments the microprocessor correctly classified 75% of stenoses. The remaining 25% were all classified as being one group more severe than they actually were.

Animals↗
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↗
PubMed2003

[Principal component analysis and integral methods of cerebral vascular hemodynamic parameters].

OBJECTIVE: To establish a predicting model for stroke according to cerebral vascular hemodynamic indexes and major risk factors of stroke. METHODS: Participants selected from a stroke cohort with 25,355 population in China. The first step was to carry out principal component analysis using CVHI. Logistic regression with principal component and main risk factors of stroke were then served as independent variables and stroke come on as dependent variables. The predictive model was established according to coefficient of regression and probability of each participant was also estimated. Finally, ROC curve was protracted and predictive efficacy was measured. RESULTS: The accumulative contribution rates of four principal components were 58.1%, 79.4%, 88.4% and 94.6% respectively. Seven variables were being selected into the equation with the first to fourth principal component as history of hypertension, age and sex. Area under ROC curve was 0.855 and optimal cut-off point was probability over 0.05. Sensitivity, specificity and accuracy of stroke prediction were 80.7%, 78.5% and 78.5% respectively. CONCLUSION: The model established by principal component and regression could effectively predict the incidence of stroke coming on.

Brain↗
PubMed1998

Feature extraction for on-line EEG classification using principal components and linear discriminants.

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

Brain Damage, Chronic↗
PubMed1988

Assessment of biological age by principal component analysis.

A method of assessing biological age by the application of principal component analysis is reported. Healthy individuals (462) randomly selected from about 6000 men who had taken a 2-day health examination were studied. Out of the 30 physiological variables examined in routine check-ups, 11 variables were selected as suitable for the assessment of biological age based on the results of factor analysis and the physiological meaning of each test. This variable set was then submitted to principal component analysis, and the 1st principal component obtained from this analysis was used as an equation for assessing one's biological age. However, the biological age calculated from this equation is expressed as a score, so the estimated score was transformed to years (biological age) using the T-score idea. The biological age estimated by this method is practically useful and theoretically valid in contrast with the multiple regression model, because this approach eliminates and overcomes the following 2 big problems of the multiple regression model: (1) the distortion of the individual biological age at the regression edges; and (2) a theoretical contradiction in that a perfect model will merely be predicting the subject's chronological age, not his biological age.

Adult↗
PubMed2004

Mapping major quantitative trait loci for postnatal growth in an intersubspecific backcross between C57BL/6J and Philippine wild mice by using principal component analysis.

A number of quantitative trait loci (QTLs) for postnatal growth have previously been reported in mice. As effects of the QTLs are usually small and similar to one another in magnitude, it is generally difficult to know which loci are major contributors to postnatal growth. We applied principal component analysis to a genome-wide search for QTLs affecting postnatal growth in body weight weekly recorded from 3 to 10 weeks of age in an intersubspecific backcross population of C57BL/6J inbred mice (Mus musculus domesticus) and wild mice (M. m. castaneus) captured in the Philippines, in order to discover new QTLs from a gene pool of the wild mice and uncover major loci underlying variation in postnatal growth. Principal component analysis classified phenotypic variation in body weights at different ages into two independent principal components: the first principal component (PC1) extracted information on the entire growth process and the second principal component (PC2) contrasted middle (3-6 weeks of age) with late (6-10 weeks) growth phases. Simple interval mapping and composite interval mapping revealed 10 significant QTLs with main effects on PC1 or PC2 on eight chromosomes. Of these, the six main-effect QTLs interacted epistatically with one another or three new additional QTLs on different chromosomal regions without main effects. Several of the identified QTLs with main effects and/or epistatic interaction effects appeared to be sex specific. These results suggest that the identified 13 QTLs, most of which affected the entire growth process, are very important contributors to complex genetic networks of postnatal growth.

Animals↗
PubMed2004

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

Amino Acid Sequence↗
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↗
PubMed1988

Multiple regression and principal components analysis of puberty and growth in cattle.

Multiple regression and principal components analyses were employed to examine relationships among pubertal and growth characters. Records used were from 424 bulls and 475 heifers produced by a diallel mating of Angus, Brahman, Hereford, Holstein and Jersey breeds. Characters studied were age, weight and height at puberty and measurements of weight and hip height from 9 to 21 mo of age; pelvic measurements of heifers also were included. Measurements of weight and height near 1 yr of age were related most highly to pubertal age, weight adn height. Larger size near 1 yr of age was associated with younger, larger animals at puberty. Growth rate was associated with pubertal characters before, but not after, adjustment for effects of breed-type. Principal components of the variation of pubertal and growth characters among animals were strongly related to both weight and height. The majority of the variation among breed-types was due to height. Characteristic vectors of principal components describing the variation of bulls and heifers were strikingly similar. The variance-covariance structure of pubertal characters was essentially the same for both sexes even though the mean values of the characters differed.

Animals↗
PubMed2004

A principal component analysis of multifocal pattern reversal VEP.

Multifocal visual evoked potentials (mfVEP) were recorded with three channels from 31 control subjects. A principal component analysis was applied to all local responses. The first principal component reversed polarity above and below the horizontal meridian in the case of the midline channel and across the vertical meridian in the case of the lateral channel. In addition, the first principal components of the responses around the vertical meridian were reversed in polarity compared to those around the horizontal meridian, consistent with the region near the vertical meridian lying outside the calcarine fissure. A model was proposed that allowed for the construction of a coronal section of V1 based on the distribution of the first principal component. This approach provides a means of deriving a V1 component from mfVEP recordings with only three recording channels.

Adult↗
PubMed1979

Craniofacial morphology: a principal component analysis.

A series of 56 measurements was derived from lateral cephalometric radiographs of a large sample of subjects. These measurements were subjected to a principal-component analysis which resulted in a series of six components (factors). These factors, represented in general terms and in rank order of their percentage sample variability were as follows: Factor 1. Vertical facial characteristics Factor 2. Anteroposterior aspects of facial morphology Factor 3. Midfacial and dental protrusion Factor 4. Relationship of the mandible and dentition to the profile Factor 5. Horizontal base-line relationships (internal or deep) Factor 6. Maxillary incisor relationships These principal components and the variables contained within them were shown to have sex and age interactions. A longitudinal study of the principal component changes with age was then undertaken. Demonstrable age changes were verified for Factors 1, 2, and 3, and Factors 1 and 3 were observed to show patterns of change which were statistically different from each other and the remaining principal components. An orthodontically treated sample of patients was also assessed for factor changes. Factors 1 and 2 were found to show statistically reliable changes resulting from treatment and/or growth. The remaining four factors showed no statistically supportable alteration. The data-reduction method involving a principal-component analysis would seem to have potential research and clinical applications.

Adolescent↗