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PubMed2001

Comparison of three methods for obtaining principal components from family data in genetic analysis of complex disease.

Three multivariate techniques used to derive principal components (PCs) from family data were compared for their ability to model family data and power to detect linkage. Using the simulated data from Genetic Analysis Workshop 12, the five quantitative traits were first adjusted for age, sex, and environmental factors 1 and 2. Then, standard PCs, PCs obtained from between-family covariance, and PCs obtained from within-family genetic covariance were derived and subjected to multivariate sib pair linkage analysis. The standard PCs obtained from the overall correlation matrix allowed identification of key features of the true genetic model more readily than did the other methods. For detection of linkage, standard PCs and PCs obtained from the between-family genetic covariance performed similarly in terms of both power and type 1 error, and both methods performed better than the PCs obtained from within-family genetic covariance.

Chromosome Mapping↗
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2005

Gene expression data classification with Kernel principal component analysis.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Journal Article↗
PubMed1994

Principal component analysis of dynamic positron emission tomography images.

Multivariate image analysis can be used to analyse multivariate medical images. The purpose could be to visualize or classify structures in the image. One common multivariate image analysis technique which can be used for visualization purposes is principal component analysis (PCA). The present work concerns visualization of organs and structures with different kinetics in a dynamic sequence utilizing PCA. When applying PCA on positron emission tomography (PET) images, the result is initially not satisfactory. It is illustrated that one major explanation for the behaviour of PCA when applied to PET images is that it is a data-driven technique which cannot separate signals from high noise levels. With a better understanding of the PCA, gained with a strategy of examining the image data set, the transformations, and the results using visualization tools, a surprisingly easily understood methodology can be derived. The proposed methodology can enhance clinically interesting information in a dynamic PET imaging sequence in the first few principal component images and thus should be able to aid in the identification of structures for further analysis.

Artifacts↗
PubMed2003

Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing.

In this study, a novel chemometric algorithm for improved evaluation of analytical data is presented and applied to three spectroscopic data sets obtained by different analytical methods. This so-called secured principal component regression (sPCR) was developed for detecting and correcting uncalibrated spectral features newly emerging in spectra after finalizing the PCR calibration, which may result in major concentration errors. Hence, detection and correction of uncalibrated features is essential. Furthermore, detected uncalibrated features provide qualitative information for sensing and process monitoring applications indicating problems in the process flow. After conventional PCR calibration, sPCR analyzes measurement data in two steps: The first step investigates whether the obtained data set is consistent with the calibration model or not. If spectroscopic features are found that cannot be modeled by the principal components, they are extracted from the measurement spectrum. This corrected spectrum is then evaluated by conventional PCR. In the Experimental Section, sPCR was successfully applied to three data sets obtained by different spectroscopic measurements in order to corroborate general applicability of the proposed concept. For each data set, one of several substances was excluded from the calibration acting in the sPCR assessment as uncalibrated absorber. The test sets consisted of disturbed and undisturbed samples. A total of 109 out of 110 test samples were correctly classified as disturbed or undisturbed by an uncalibrated absorber. It was confirmed that the extracted disturbance spectra are in accordance with the spectra of the uncalibrated analytes. The concentration results obtained with sPCR were found to be equivalent to conventional PCR results in the case of undisturbed samples and more precise for disturbed samples.

Journal Article↗
PubMed2001

Evaluation of the lipophilicity of bile acids and their derivatives by thin-layer chromatography and principal component analysis.

The lipophilic character of bile acids and their glyco- and tauro-conjugates was studied. The classical R(Mo) values were measured by means of reversed thin-layer chromatography using a mixture of methanol-water as the solvent system and compared with the factors scores obtained by principal component analysis based also onto the TLC-retention data. The reliability of the factor scores values as lipophilic indices are shown by their high correlation with the classical R(Mo) values. In addition, a better correlation was observed between scores corresponding to the first principal components and the partition coefficients (log P) of bile acids. Finally, the "lipophilicity chart" described by the first two components has the effect of separating compounds from each other most effectively from the congeneric aspect point of view.

Bile Acids and Salts↗
PubMedDate not supplied

Longitudinal principal components and non-linear regression models of early childhood growth.

The Jenss curve is a non-linear regression model which describes quite well the longitudinal length or weight measurements of an individual child from three months to six years. It is contrasted with modelling growth by longitudinal principal components analysis, an approach developed by Kent which is not restricted to any particular curve type or age range, and which can be thought of as providing the best fit among linear models having kappa parameters. Thus, in addition to being a model itself, it can be used to evaluate the success of other models. It is shown how this method differs from classical principal components. Data from Longitudinal Studies of Child Health and Development are used to explore these issues.

Child↗
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↗
PubMedDate not supplied

Data reduction of multichannel fields: global field power and principal component analysis.

Electroencephalographic data recorded for topographical analysis constitute multidimensional observations, and the present paper illustrates methods of data analysis of multichannel recordings where components of evoked brain activity are identified quantitatively. The computation of potential field strength (Global Field Power, GFP) is used for component latency determination. Multivariate statistical methods like Principal Component Analysis (PCA) may be applied to the topographical distribution of potential values. The analysis of statistically defined components of visually elicited brain activity is illustrated with data sets stemming from different experiments. With spatial PCA the dimensionality of multichannel data is reduced to only three components that account for more than 90% of the variance. The results of spatial PCA relate to experimental conditions in a meaningful way, and this method may also be used for time segmentation of topographic potential maps series.

Action Potentials↗
PubMedDate not supplied

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis.

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

Aged↗
PubMed2004

A principal components analysis self-organizing map.

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

Neural Networks, Computer↗
PubMed2001

Application of principal component analysis to the elucidation of perioral soft tissue movements during mastication.

We considered that elucidation of the movements of perioral soft tissue during mastication would be useful in evaluating masticatory movements. However, the evaluation of these movements is difficult because multivariable analysis is needed. In this study, we considered whether principal component analysis (PCA), a form of the multivariate analysis, can reduce the number of degrees of freedom (d.f.) of perioral skin movements (i.e. the time-series data with 45 d.f.). The subjects were 15 healthy persons with complete natural dentition. The chosen experimental food for this study was sufficiently softened chewing gum. Over 95% of the perioral soft tissue movements of healthy subjects with complete natural dentition during mastication could be expressed by PCA using the first three principal components (PCs). Therefore, perioral soft tissue movements in these subjects during mastication were considered to be spatially smooth. Moreover, time analysis of these movements was made possible by the application of proportion diagrams. The results of this study showed that the spatiostructural and temporal analyses of the movements of perioral soft tissue during mastication made possible by the application of PCA.

Adult↗
PubMed2000

EEG bands during wakefulness, slow-wave and paradoxical sleep as a result of principal component analysis in man.

Human electroencephalogram (EEG) has been divided in bands established by visual inspection that frequently do not correspond with EEG generators nor with functional meaning of EEG rhythms. Power spectra from wakefulness, stage 2, stage 4 and paradoxical sleep of 8 young adults were submitted to Principal Component Analyses to investigate which frequencies covaried together. Two identical eigenvectors were identified for stage 2 and stage 4: 1 to 8 Hz and 5 to 15 Hz (87.95 and 84.62 % of the total variance respectively). Two eigenvectors were extracted for PS: 1 to 9 Hz and 10 to 15 Hz (81.62% of the total variance). Three eigenvectors were obtained for W: with frequencies between 1 to 7 Hz, 7 to 11 Hz, and 12 to 15 Hz (78.32% of the total variance). Power for all frequencies showed significant differences among vigilance states. These results indicate that slow wave activity can oscillate at higher frequencies, up to 8 Hz, and that spindle oscillations have a wider range down to 5 Hz. No theta band was independently identified, suggesting either that delta and theta oscillations are two rhythms under the same global influence, or that the traditional division of theta band in the human cortical EEG is artificial. Alpha as a band was identified only during wakefulness. Principal component analysis upon spectral densities extracted broad bands different for each vigilance state and from traditional bands, consistent with functional significance of EEG and with frequencies of generators of rhythmic activity obtained in cellular studies in animals.

Adult↗
PubMed2007

Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: a principal component analysis of the human visual system.

This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second-level analysis subsequent to model-based General Linear Model (GLM)-type functional activation mapping. Functional homogeneity of the functional magnetic resonance imaging (fMRI) time series within and between clusters was examined on several levels of the visual network, starting from the level of individual clusters up to the network level comprising two or more distinct visual regions. On each level, the number of significant components was identified and compared with the number of clusters in the data set. Eigenimages were used to examine the regional distribution of the extracted components. It was shown that voxels within individual clusters and voxels located in bilateral homologue visual regions can be represented by a single component, constituting the characteristic functional specialization of the cluster(s). If, however, PCA was applied to time series of voxels located in functionally distinct visual regions, more than one component was observed with each component being dominated by voxels in one of the investigated regions. The model of functional connections derived by PCA was in accordance with the well-known functional anatomy and anatomical connectivity of the visual system. PCA in combination with conventional activation mapping might therefore be used to identify the number of functionally distinct nodes in an fMRI data set in order to generate a model of functional connectivity within a neuroanatomical network.

Adult↗
PubMed1999

Principal component elimination method for the improvement of S/N in evoked neuromagnetic field measurements.

In the study of magnetoencephalography, it is important to obtain evoked fields with good signal-to-noise ratios (S/N) and with a small number of epochs in averaging. The noises are considered to be mainly spontaneous neuromagnetic fields. In the present study, we propose a method to improve the S/N. The basic principle of this method is the elimination of a principal component (PC) of multichannel-recorded neuromagnetic fields, utilizing the synchronized characteristics of spontaneous rhythmic activities dominating the fields. The proposed method is, therefore, called the principal component elimination method (PCEM). PCEM was applied to neuromagnetic fields measured by a 37-channel superconducting quantum interference device system, on which computer-generated evoked fields were superposed, in order to examine possible improvement in S/N. It was found that elimination of the first PC could improve the S/N of the evoked fields. The improvement in S/N with elimination of the first PC, compared to conventional simple averaging, increased with increases in the number of epochs and reached more than 50% after averaging over 128 epochs. PCEM also reduced the number of epochs needed in averaging to about half of that needed in conventional simple averaging.

Adult↗
PubMed1999

Linkage of chromosome 1 markers to alcoholism-related phenotypes by sib pair linkage analysis of principal components.

Using the Collaborative Study on the Genetics of Alcoholism data and affected-sib-pair linkage methods, Reich et al. [1998] reported linkage of alcohol dependence to a region near D1S1588 on chromosome 1. In this paper, we assessed the ability of multivariate sib-pair linkage analysis of the neurophysiologic measurements (including age and sex) to evaluate evidence for linkage to chromosome 1. Principal components of 16 neurophysiologic measurements, plus age and sex, were analyzed separately using sib-pair linkage analysis, and a cumulative sum of the resulting t2-statistics computed at each point on the chromosome. The first four principal components, which accounted for 74% of the total variation, showed little or no evidence for linkage in the D1S1588 region, while the remaining components showed substantial evidence for linkage. We conclude that potentially important linkage results can be missed if investigators limit attention only to major sources of variability.

Age Factors↗
PubMed1993

Principal component analysis learning algorithms: a neurobiological analysis.

The biological relevance of principal component analysis (PCA) learning algorithms is addressed by: (i) describing a plausible biological mechanism which accounts for the changes in synaptic efficacy implicit in Oja's 'Subspace' algorithm (Int. J. neural Syst. 1, 61 (1989)); and (ii) establishing a potential role for PCA-like mechanisms in the development of functional segregation. PCA learning algorithms comprise an associative Hebbian term and a decay term which interact to find the principal patterns of correlations in the inputs shared by a group of units. We propose that the presynaptic component of this decay could be regulated by retrograde signals that are translocated from the terminal arbors of presynaptic neurons to their cell bodies. This proposal is based on reported studies of structural plasticity in the nervous system. By using simulations we demonstrate that PCA-like mechanisms can eliminate afferent connections whose signals are unrelated to the prevalent pattern of afferent activity. This elimination may be instrumental in refining extrinsic cortico-cortical connections that underlie functional segregation.

Algorithms↗
PubMed1993

[Principal components analysis].

OBJECTIVE: This study wants to identify factors or components latent besides health indicators from Spanish regions, and its graphic output. DESIGN: Observational study. SETTING: Twenty eight indicators from regions were used: mortality, morbidity, communicable and no-communicable diseases, diet, dwelling and sanitary resources. Measurement was made between 1980-1988. INTERVENTIONS: Principal component analysis has been applied to the indicators, reducing data dimension. MEASUREMENT AND MAIN RESULTS: Eight factors have been extracted, which explain 90% of the original information. This analysis, as can be seen from communnalities, represents correctly the set of original variables. The factors with more easily interpretation were: shortage sanitary resources, develop diet, mortality, chronic diseases and accidental. CONCLUSIONS: Only reduction data dimension could be justify the use of principal component analysis. Behind the agrupation of variables is mostly the socioeconomic background.

Health Status Indicators↗