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PubMed1985

Study of lung function data by principal components analysis.

As a rational approach to the many lung function tests available, we have subjected the results of a battery of six lung function measurements made in 458 coalminers to the statistical technique of principal components analysis. By this means the six test results were reduced to three principal components without important loss of information. The first component appeared to represent lung size and the second the degree of airflow obstruction, and the third detected impairment of gas transfer factor in excess of that explained by the first two components. The values of the first principal component, used to select men with abnormal lung function, identified more younger men with functional abnormalities than a method based on comparison of observed and predicted values of forced expiration volume in one second. The values of the second and third principal components were used to classify types of functional abnormality. It is concluded that this statistical technique provides a sensitive method of identifying men with unusual lung function, particularly younger men, in a population and can be used to define and quantify different aspects of lung function.

Adult↗
PubMedDate not supplied

Identification of drugs by principal components analysis of Rf data obtained by TLC in different eluent systems.

The retention factor of 54 drugs in eight eluent mixtures is reported. Principal component analysis (PCA) of these data provided a significant two-components model. These two parameters characteristic for each drug allowed an objective identification of unknown samples, provided they were included in the considered set. The analysis showed that the eluent mixtures cluster into three groups. The PCA model, using only three eluents (one for each group), was also able to restrict the range of inquiry to a few "candidates" and, in some cases, to allow unambiguous identification of the drug. These results, based on a simple and quick analytical determination (thin layer chromatography) and a reliable statistical procedure (PCA), appear to be of significant practical importance in the field of analytical toxicology.

Chromatography, Thin Layer↗
PubMed2001

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering.

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy o-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Algorithms↗
PubMed2000

Principal component analysis of the power developed in the flexion/extension muscles of the hip in able-bodied gait.

This study was undertaken to demonstrate how principal component analysis (PCA) can be used: (a) to detect the main functional structure of actions taken by hip extensors and flexors during two consecutive gait cycles of able-bodied subjects, and (b) to determine whether or not symmetrical behaviour exists between right and left hip muscle power activity. Twenty young, healthy male subjects walked along a 13 m path at a freely-chosen speed. Applying curve structure detection methods such as PCA to walking patterns can provide insight into the functional tasks accomplished by the lower limbs of able-bodied and disabled subjects. PCA was applied as a classification and curve structure detection method to hip sagittal muscle power calculated for the right and left lower limbs. Over 70% of the information provided by the first four principal components (PCs) was chosen for further biomechanical interpretation. PC1 for both right and left sides mainly described the action taken by the hip extensors/flexors corresponding to the vertical component of ground force on the respective limbs during mid-stance. Propulsion and limb preparation were identified as the second and third tasks attributed to right hip muscle power, while between limb co-ordination was recognised as the second and third functional tasks of the left hip extensors/flexors. Balance was identified as the fourth main functional contribution of the hip extensors/flexors at the right limb while for the left limb, these muscles were mainly responsible for preparing the limb to enter into new gait cycle. PCA was able to identify the four main functional contributions of hip sagittal muscle power during able-bodied gait. PCA was also able to examine the existence of functional asymmetry in gait by highlighting different task priorities at the hip level for the right and left lower limbs.

Adult↗
PubMed2004

Principal component analysis for reduction of ocular artefacts in event-related potentials of normal and dyslexic children.

OBJECTIVE: The aim of this study was to reduce ocular artefacts in single trial event-related potentials (ERPs) recorded in normal and in dyslexic children. METHODS: ERPs were recorded during passive and active reading of centrally presented alphabetic letters and non alphabetic symbols. EEG was recorded from 10 EEG locations using the 10-20 system. Diagonal EOG from the right eye was also recorded. Principal component analysis (PCA) was applied in order to reduce ocular artefacts: the first or the second principal component (PC) was subtracted when the correlation coefficient between the component and EOG was greater or equal to 0.9 or 0.95, respectively. Performance of the method was tested on simulated and real data, on both single and averaged trials, varying EOG amplitude and artefact transmission characteristics. RESULTS: Applying the method to real recordings from normal and dyslexic children, we obtained a significant increase in the number of useful trials. In normal children we retrieved 41.0% of the rejected trials in passive and 39.1% in active reading. In dyslexic children 36.7 and 32.2% of the rejected trials in passive and active reading could be included in the respective averages. CONCLUSIONS: The method allows an increase in the number of trials suitable for averaging, a great improvement in ERP quality and a reduction in the recording time.

Artifacts↗
PubMed2004

An automated algorithm for the computation of brain volume change from sequential MRIs using an iterative principal component analysis and its evaluation for the assessment of whole-brain atrophy rates in patients with probable Alzheimer's disease.

This article introduces an automated method for the computation of changes in brain volume from sequential magnetic resonance images (MRIs) using an iterative principal component analysis (IPCA) and demonstrates its ability to characterize whole-brain atrophy rates in patients with Alzheimer's disease (AD). The IPCA considers the voxel intensity pairs from coregistered MRIs and identifies those pairs a sufficiently large distance away from the iteratively determined PCA major axis. Analyses of simulated and real MRI data support the underlying assumption of a linear relationship in paired voxel intensities, identify an outlier distance threshold that optimizes the trade-off between sensitivity and specificity in the detection of small volume changes while accounting for global intensity changes, and demonstrate an ability to detect changes as small as 0.04% of brain volume without confounding effects of between-scan shifts in voxel intensity. In eight patients with probable AD and eight age-matched normal control subjects, the IPCA was comparable to the established but partly manual digital subtraction (DS) method in characterizing annual rates of whole-brain atrophy: resulting rates were correlated (Spearman rank correlation = 0.94, P < 0.0005) and comparable in distinguishing probable AD from normal aging (IPCA-detected atrophy rates: 2.17 +/- 0.52% per year in the patients vs. 0.41 +/- 0.22% per year in the controls [Wilcoxon-Mann-Whitney test P = 7.8 x 10(-4)]; DS-detected atrophy rates: 3.51 +/- 1.31% per year in the patients vs. 0.48 +/- 0.29% per year in the controls [P = 7.8 x 10(-4)]). The IPCA could be used in tracking the progression of AD, evaluating the disease-modifying effects of putative treatments, and investigating the course of other normal and pathological changes in brain morphology.

Aged↗
PubMed2000

Principal component analysis of dynamical features in the peroxidase-oxidase reaction

Inherent variance due to oscillations in the peroxidase-oxidase (PO) reaction was studied using principal component analysis (PCA). The substrates were oxygen and reduced nicotinamide adenine dinucleotide (NADH). Horseradish peroxidase (HRP) catalyzed the reaction. The concentration of a cofactor, methylene blue (MB), was varied, and 2,4-dichlorophenol was kept constant. Increase in the NADH influx was used to change the reaction dynamics from periodic to chaotic. The reaction space was abstracted to the most significant, mutually independent, pairs of absorption and kinetic basis vectors (principal components). Typically, two significant principal components were extracted from the periodic time series and three from the chaotic data. The PCA models accounted for 70-97% of experimental variance. The greatest fraction of the total variance was accounted for in experiments exhibiting periodic dynamics and less than 25 nM MB. More MB induced an increased contribution of NADH to the PO oscillator variance, as did increased NADH influx. A simulated absorption time series, computed from a mass-action model of the chemistry, was analyzed by PCA as well. The comparison of simulation with experiment indicates that the chemical model renders the time series for HRP oxidation forms with fidelity, but incompletely represents NADH chemistry and other salient processes underlying the observed dynamics.

Journal Article↗
PubMed2002

Genetics of colitis susceptibility in IL-10-deficient mice: backcross versus F2 results contrasted by principal component analysis.

Interleukin-10-deficient (Il10(-/-)) mice on a C3H/HeJBir genetic background develop more severe colitis than those on a C57BL/6J background. We performed genome screens for quantitative trait loci (QTLs) regulating colitis susceptibility in this model system using two first backcross populations derived from these two strains. To reduce the complexity of this analysis, the information from numerous histologic phenotypes was summarized by principal component analysis. A similar approach was applied to previously published data from an F2 intercross (involving the same progenitor strains), which allowed us to ascertain all six previously reported cytokine-deficiency-induced colitis susceptibility loci (Cdcs1-6) with main and/or interacting effects on chromosomes 3, 1, 2, 8, 17, and 18. The colitogenic effect of Cdcs1 was confirmed in the backcross to C3H/HeJBir-Il10(-/-). Its effect was epistatically modified by another locus on chromosome 12. In addition, three main effect QTLs on chromosomes 4, 5, and 12 were identified in the backcross to C57BL/6J-Il10(-/-). Analyses of the modes of inheritance in these crosses revealed colitogenic contributions by both parental genomes. These findings show the complexity of inheritance underlying susceptibility to colitis and illustrate why detection of human inflammatory bowel disease loci has proven to be so difficult.

Animals↗
PubMed1996

Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net.

The classification of astrocytomas, astrocytomas with anaplastic foci and glioblastoma multiformes is not always straightforward because the tumors form a histological continuum. The use of principal component analysis (PCA) and neural nets in the classification of these tumors is explored. PCA was performed on 14 histological features recorded from 52 gliomas classified by the Radiation Therapy Oncology Group method (17 astrocytomas, 18 astrocytomas with anaplastic foci, 17 glioblastoma multiformes). Four of the 14 possible 'scores' derived from this analysis were selected to summarize the histological variability seen in all the tumors. These scores were mostly significantly different between tumor types and were thus used to successfully train a neural net to correctly classify these tumors. The first principal component (score) supported the use of increasing cellularity, mitoses, endothelial proliferation, and necrosis in differentiating between the tumor categories, but accounted for only 39% of the variability seen. Other histological features that were significant components of the other scores included the presence of multinucleated or giant cells, gemistocytes, atypical mitoses and changes in nuclear chromatin. Computer programs derived from the methodology described provide a way of standardizing glioma diagnosis and may be extended to assist with management decisions.

Astrocytoma↗
PubMed2004

Screening molecular associations with lipid membranes using natural abundance 13C cross-polarization magic-angle spinning NMR and principal component analysis.

We describe an NMR approach for detecting the interactions between phospholipid membranes and proteins, peptides, or small molecules. First, 1H-13C dipolar coupling profiles are obtained from hydrated lipid samples at natural isotope abundance using cross-polarization magic-angle spinning NMR methods. Principal component analysis of dipolar coupling profiles for synthetic lipid membranes in the presence of a range of biologically active additives reveals clusters that relate to different modes of interaction of the additives with the lipid bilayer. Finally, by representing profiles from multiple samples in the form of contour plots, it is possible to reveal statistically significant changes in dipolar couplings, which reflect perturbations in the lipid molecules at the membrane surface or within the hydrophobic interior.

Calcium-Binding Proteins↗
PubMed1989

Principal-Component Analysis of the Characteristics Desirable in Baker's Yeasts.

Twenty-seven properties considered to be required for good bakery products were examined in 56 industrial and 2 laboratory yeast strains. The data obtained were applied to principal-component analysis, one of the multivariate statistical analyses. The first and second principal components together were extracted, and these accounted for 77.7% of the variance. The first principal component was interpreted as the glycolytic activity of yeast in dough, and the second one was interpreted as the balance of leavening abilities in sweet and flour doughs from the factor loadings. The scattergram on the two principal components was effective in grouping the 58 yeast strains used.

Journal Article↗
PubMed2003

Effective dimensionality for principal component analysis of time series expression data.

Large-scale expression data are today measured for thousands of genes simultaneously. This development has been followed by an exploration of theoretical tools to get as much information out of these data as possible. Several groups have used principal component analysis (PCA) for this task. However, since this approach is data-driven, care must be taken in order not to analyze the noise instead of the data. As a strong warning towards uncritical use of the output from a PCA, we employ a newly developed procedure to judge the effective dimensionality of a specific data set. Although this data set is obtained during the development of rat central nervous system, our finding is a general property of noisy time series data. Based on knowledge of the noise-level for the data, we find that the effective number of dimensions that are meaningful to use in a PCA is much lower than what could be expected from the number of measurements. We attribute this fact both to effects of noise and the lack of independence of the expression levels. Finally, we explore the possibility to increase the dimensionality by performing more measurements within one time series, and conclude that this is not a fruitful approach.

Algorithms↗
PubMed2006

Identification of latent variables in a semantic odor profile database using principal component analysis.

Many classifications of odors have been proposed, but none of them have yet gained wide acceptance. Odor sensation is usually described by means of odor character descriptors. If these semantic profiles are obtained for a large diversity of compounds, the resulting database can be considered representative of odor perception space. Few of these comprehensive databases are publicly available, being a valuable source of information for fragrance research. Their statistical analysis has revealed that the underlying structure of odor space is high dimensional and not governed by a few primary odors. In a new effort to study the underlying sensory dimensions of the multivariate olfactory perception space, we have applied principal component analysis to a database of 881 perfume materials with semantic profiles comprising 82 odor descriptors. The relationships identified between the descriptors are consistent with those reported in similar studies and have allowed their classification into 17 odor classes.

Cluster Analysis↗
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↗
PubMed2006

A study on water adsorption onto microcrystalline cellulose by near-infrared spectroscopy with two-dimensional correlation spectroscopy and principal component analysis.

Water adsorption onto microcrystalline cellulose (MCC) in the moisture content (M(c)) range of 0.2-13.4 wt % was investigated by near-infrared (NIR) spectroscopy. In order to distinguish heavily overlapping O-H stretching bands in the NIR region due to MCC and water, principal component analysis (PCA) and generalized two-dimensional correlation spectroscopy (2DCOS) were applied to the obtained spectra. The NIR spectra in four adsorption stages separated by PCA were analyzed by 2DCOS. For the low M(c) range of 0.2-3.1 wt %, a decrease in the free or weakly hydrogen-bonded (H-bonded) MCC OH band, increases in the H-bonded MCC OH bands, and increases in the adsorbed water OH bands are observed. These results suggest that the inter- and intrachain H-bonds of MCC are formed by monomeric water molecule adsorption. In the M(c) range of 3.8-7.1 wt %, spectral changes in the NIR spectra reveal that the aggregation of water molecules starts at the surface of MCC. For the high M(c) range of 8.1-13.4 wt %, the NIR results suggest that the formation of bulk water occurs. It is revealed from the present study that approximately 3-7 wt % of adsorbed water is responsible for the stabilization of the H-bond network in MCC at the cellulose-water surface.

Adsorption↗
PubMed2005

The phase transition temperatures of a liquid crystal determined from FT-IR spectra explored by principal component analysis.

The FT-IR spectra of a thin layer of pure 4-chloro-2'-hydroxy-4'-pentyloxyazobenzene (CHPAB) were studied as a function of temperature. A detailed analysis of the intensity variations was performed by a method based on principal component analysis (PCA). It was shown that the phase transition temperatures obtained by means of PCA and those determined by differential scanning calorimetry (DSC), the most widely used technique in the field, were nearly identical. The PCA results revealed that the transition from solid to a liquid crystalline (LC) phase (smectic A) is more drastic phase transition in terms of infrared absorption changes. The nematic to isotropic phase transition is much less infrared sensitive. Very much smaller absorption changes are associated with the transition between the smectic and nematic mesophases. The pattern of the intensity changes strictly is correlated with the orientation of the CHPAB molecules towards the surface windows due to the surface-induced homeotropic alignment of LC molecules. The important role of hydrogen bonding interaction on the observed transition is disclosed.

Azo Compounds↗
PubMed2006

[Study of variables associated with skin cancer in Chile using principal component analysis].

BACKGROUND: The incidence of skin cancer in Chile has increased in recent years. OBJECTIVE: To associate variables with skin cancer in Chile through indices generated using multivariate descriptive statistical techniques. MATERIAL AND METHOD: During May 2004, information was gathered from demographic, meteorological and clinical data from Chile corresponding to fiscal year 2001, the latest complete, official information available for the country's Health Services as a whole. The variables developed by the following were studied: the National Statistics Institute (INE), the Ministry of Health (MINSAL), the Ministry of Planning and Cooperation (MIDEPLAN), the National Health Fund (FONASA), the Chilean Meteorological Directorate, Federico Santa María Technical University and the Directorate-General for Water. A Principal Component Analysis (PCA) was then performed on the data obtained. RESULTS: The first three principal components were selected, with a cumulative explained variance percentage of 54.48 %. The first principal component explains 24.92 % of the variance, and is related to climatic and geographic variables. The second principal component explains 15.77 % of the variance, and is mainly related to FONASA's beneficiary population and the poverty rate. The mortality rate from skin cancer runs significantly against this component. The third principal component explains 13.79 % of the variance, and is related to population characteristics, such as total catchment population, female population and urban population. CONCLUSION: Performing PCA is useful in studying the factors associated with skin cancer.

Adult↗