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Results for “Principal Component Analysis”

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PubMed1992

Morphology of the normal visual field in a population-based random sample: principal components analysis.

I applied principal components analysis to Humphrey central 24-2 threshold values from both eyes of 304 clinically normal persons selected by simple random sample from Barbados, WI. The first component, accounting for 62 per cent of the variation, is equivalent to the average threshold value within persons. The first eigenvector, when represented by grey scale maps depicting a pair of eyes, reveals that, as average threshold increases, the visual field rises and flattens, like an umbrella that, initially closed, is simultaneously opened and thrust upwards. I verify three numerical predictions based upon this umbrella description. Much less important sources of variation involve disparity between fellow eyes, and hemimeridional and other symmetric differences within eyes. I discuss briefly possible physiologic explanatory mechanisms.

Barbados↗

PubMed1989

Multicomponent analysis of near-infrared spectra of anesthetized rat head: (I). Estimation of component spectra by principal component analysis.

By measuring precise NIR spectra of anesthetized rat head, we determined principal components included in changes of the NIR spectra caused by acute hypoxia. There was apparent delay in reduction of cyt. aa3 as compared with Hb deoxygenation in the hypoxic period. With regard to the eigenvalues determined by principal component analysis, principal components in the NIR spectral changes caused by acute hypoxia were Hb and cyt. aa3, and contribution of the remaining biological materials to the changes in NIR spectra caused by acute hypoxia was considered to be negligible in quantitative multivariate analysis of Hb and cyt. aa3 in situ.

Animals↗

PubMed1975

Selecting key parameters in pharmaceutical formulations by principal component analysis.

The role of principal component analysis in the selection of pharmaceutical formulations is presented. The objective and the procedure of the analysis are discussed in detail. The technique was successfully applied to a system consisting of 10 response variables (tablet properties). Analysis of the results showed that the first component (dissolution) and components one and two together (dissolution and disintegration) contributed 95.4 and 99.3%, respectively, to the overall information about the formulations and that eight of 10 response parameters contributed nothing further to the overall information. The results obtained from this method of analysis may be found useful for achieving economy in both cost and time of measuring response. Principal component analysis also provides a basis for understanding the underlying mechanism of the system under consideration.

Computers↗

PubMed2001

Evaluation of dissolution profiles using principal component analysis.

The performance of principal component analysis (PCA) for the evaluation of dissolution profiles is examined and compared with other methods such as the similarity factor and the calculation of the area under the curve. Both simulated and real data from the pharmaceutical industry are used. The PCA scores plots of the dissolution curves provide information about the between- and within-batch variations. Differences in level or shape can be observed in the first two principal components (PCs). Irrelevant irregularities, which have a strong influence on the similarity factor, are neglected in PC1/PC2. To detect outliers in a set of dissolution curves, PCA was preferred above Hotelling's T2 test. In general, PCA is found to be a useful technique to examine dissolution data visually, but however, it does not contain criteria to decide if batches are similar or not. This can be done by combining PCA with the resampling with replacement or bootstrap method to construct confidence limits.

Solubility↗

PubMed2001

NMR spectral quantitation by principal component analysis.

The use of principal component analysis (PCA) for simultaneous spectral quantitation of a single resonant peak across a series of spectra has gained popularity among the NMR community. The approach is fast, requires no assumptions regarding the peak lineshape and provides quantitation even for peaks with very low signal-to-noise ratio. PCA produces estimates of all peak parameters: area, frequency, phase and linewidth. If desired, these estimates can be used to correct the original data so that the peak in all spectra has the same lineshape. This ability makes PCA useful not only for direct peak quantitation, but also for processing spectral data prior to application of pattern recognition/classification techniques. This article briefly reviews the theoretical basis of PCA for spectral quantitation, addresses issues of data processing prior to PCA, describes suitable and unsuitable datasets for PCA applications and summarizes the developments and the limitations of the method.

Magnetic Resonance Spectroscopy↗

PubMed2001

The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data.

By using principal components analysis (PCA) we demonstrate here that the information relevant to tumor line classification linked to the activity of 1375 genes expressed in 60 tumor cell lines can be reproduced by only five independent components. These components can be interpreted as cell motility and migration, cellular trafficking and endo/exocytosis, and epithelial character. PCA, at odds with cluster analysis methods routinely used in microarray analysis, allows for the participation of individual genes to multiple biochemical pathways, while assigning to each cell line a quantitative score reflecting fundamental biological functions.

Cell Movement↗

PubMed1992

Composite index of skeletal mass: principal components analysis of regional bone mineral densities.

Principal components analysis is a statistical method that is used to reduce and explore data to facilitate further analyses. This method was applied to bone mineral densities measured at seven sites in 109 black and 44 white women, ages 22-80, at an internal medicine clinic in urban Detroit. We excluded subjects with a history of diseases or drugs known to affect bone metabolism. Principal components analysis was used to summarize the interrelationship of the densities and yielded two major results. First, the seven site measurements were reduced to a single, composite index (PC1) of skeletal mass that accounted for 73% of the variation in density among subjects. PC1 had roughly equal weights among the sites. A second combination of the seven sites indicated that the contrast between axial and appendicular regional densities accounted for another 10% of the variation among subjects. In investigating the relationship of density to age, body mass index, and ethnic group, we found that the principal components composite index had a stronger correlation with age (r = -0.58) and with body mass index (r = 0.34) than almost all of the regional densities. Black-white differences were larger for the composite index than for any single site density. A multiple regression of the composite index on ethnicity, body mass index, and age yielded a larger R2 (0.46) than any of the individual site densities. The second principal component, although of theoretical interest, showed a minimal ability to discriminate among subjects using the three independent variables of this study.

Absorptiometry, Photon↗

PubMed1990

Characterization of premotor interneurones by their input patterns--application of principal component analysis to cat cervical interneurones.

Principal component analysis of input patterns of cat C6-C8 interneurones (300 cells) revealed that identified premotor interneurones (11 cells) activated from skin afferents and projecting to T1 motoneurones possessed a special input pattern, characterized by restricted distribution on the plane of the first (Prin 1) versus second (Prin 2) principal component (high positive values of both components). These premotor neurones were located mostly in laminae V-VI. Among other laminae V-VI cells descending in the lateral funiculus to T1 similar to such premotor neurones, there were cells distributed similarly on the Prin 1-2 plane. Further, a majority of interneurones antidromically activated from the T1 motor nucleus at low thresholds also showed a distribution on the plane similar to the premotor neurones. We suggest that premotor neurones of this input pattern constitute a major group among laminae V-VI premotor neurones projecting to T1.

Animals↗

PubMed1998

Internal motions of native lysozyme are more organized than those of mutants: a principal component analysis of molecular dynamics data.

Principal component analysis (PCA) of molecular dynamics simulations of hen egg white lysozyme and its mutants indicate that even small changes in the amino acid sequence alter considerably the internal molecular motions and that the internal motions are more organized in the native enzyme than in the mutants.

Animals↗

PubMed1997

Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition.

Principal component analysis (PCA) by singular value decomposition (SVD) may be used to analyze an epoch of a multichannel electroencephalogram (EEG) into multiple linearly independent (temporally and spatially noncorrelated) components, or features; the original epoch of the EEG may be reconstructed as a linear combination of the components. The result of SVD includes the components, expressible as time series waveforms, and the factors that determine how much each component waveform contributes to each EEG channel. By omission of some component waveforms from the linear combination, a new EEG can be reconstructed, differing from the original in useful ways. For example, artifacts can be removed and features such as ictal or interictal discharges can be enhanced by suppressing the remainder of the EEG. We developed a variation of this technique in which the factors that reconstruct the modified EEG from the original are stored as a matrix. This matrix is applied to multichannel EEG at successive times to create a new EEG continuously in real time, without redoing the time-consuming SVD. This matrix acts as a spatial filter with useful properties. We successfully applied this method to remove artifacts, including ocular movement and electrocardiographic artifacts. Removal of myogenic artifacts was much less complete, but there was significant improvement in the ability to visualize underlying activity in the presence of myogenic artifacts. The major limitations of the method are its inability to completely separate some artifacts from cerebral activity, especially when both have similar amplitudes, and the possibility that a spatial filter may distort the distribution of activities that overlap with the artifacts being removed.

Data Interpretation, Statistical↗

PubMed1985

Qualitative organic analysis. I. Identification of drugs by principal components analysis of standardized thin-layer chromatographic data in four eluent systems.

Principal component 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] provided a two-component model which accounts for 73% of the total variance. The "scores" plot allowed the restriction of the range of inquiry to a few candidates. This result is of great practical significance in analytical toxicology, especially when account is taken of the cost, the time, the analytical instrumentation and the simplicity of the calculations required by the method.

Chromatography, Thin Layer↗

PubMed2000

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.

Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.

Brain↗

PubMed1984

Principal-components analysis of prehistoric South Asian crania.

Principal-components analysis is used as an investigative procedure for establishing temporal, spatial and evolutionary-developmental changes in Homo sapiens skeletal specimens from prehistoric sites in South Asia. It is concluded that cranial variables which cluster hunter-gatherers within the sample are related to facial architecture with respect to robusticity and size. Older models presumably reflecting genetic affinities and racial classifications of prehistoric South Asians which were based upon univariate-bivariate statistical analyses are not supported by the results of this principal-components analysis.

Asia, Western↗

PubMed1992

[Classification of congenital superior oblique palsy based on principal component analysis].

An attempt was to classify unilateral congenital superior oblique palsy principal component analysis. Each principal component was calculated by taking a linear combination of an eigenvector of the correlation matrix with a standardized original variable. The variables selected for the analysis were vertical deviation in the nine diagnostic positions of 51 cases measured by a synoptometer. The cumulative contributive percent of principal components showed that 88.5% of the variation were accounted for by the first three principal components. The first principal component accounted for 56.7% of the variation in samples indicating the extent of superior oblique palsy in which vertical deviation increases or decreases proportionately. The second principal component accounted for 20.6% of the variation of samples indicating the extent of the incomitance of vertical deviation with a vertical change of gaze. The third principal component accounted for 11.1% of the variation in the sample indicating the extent of the vertical deviation with a horizontal change of gaze.

Adolescent↗

PubMed1997

Nonlinear principal components analysis of neuronal spike train data.

Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed.

Action Potentials↗

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↗

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↗

PubMed1996

[Fast and accurate numerical method for principal components analysis dealing with large image data sets].

The principal components analysis has been applied to various imaging studies in nuclear medicine. This technical report describes a fast and accurate numerical method of calculating eigenvalues and eigenvectors in the principal components analysis dealing with larger image data sets. The method employs both data transformation and matrix transpose of original data sets to calculate a variance-covariance or correlation matrix. The method was tested on actual image data sets using a common workstation, confirming faster execution time and efficient accuracy in comparison to a standard method.

Image Processing, Computer-Assisted↗