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Results for “Principal Component Analysis”

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PubMed2002

Principal component analysis of the T wave and prediction of cardiovascular mortality in American Indians: the Strong Heart Study.

BACKGROUND: Increased QT interval dispersion (QTd) is a proposed ECG marker of vulnerability to ventricular arrhythmias and of cardiovascular (CV) mortality. However, principal component analysis (PCA) of the T-wave vector loop may more accurately represent repolarization abnormalities than QTd. METHODS AND RESULTS: Predictive values of QTd and PCA were assessed in 1839 American Indian participants in the first Strong Heart Study examination. T-wave loop morphology was quantified by the ratio of the second to first eigenvalues of the T-wave vector by PCA (PCA ratio); QTd was quantified as the difference between maximum and minimum QT intervals. After 3.7+/-0.9 years mean follow-up, there were 55 CV deaths. In univariate analyses, an increased PCA ratio predicted CV mortality in women (chi2=7.8, P=0.0053) and men (chi2=9.5, P=0.0021). In contrast, increased QTd was a significant predictor of CV mortality in women (chi2=30.6, P<0.0001) but not in men (chi2=2.0, P=NS). In multivariate Cox analyses controlling for risk factors and rate-corrected QT interval, the PCA ratio remained a significant predictor of CV mortality in women (chi2=4.0 P=0.043) and men (chi2=6.4, P=0.011); QTd was a significant predictor in women only (chi2=11.0, P=0.0009). PCA ratios >90th percentile (32% in women and 24.6% in men) identified women with a 3.68-fold increased risk of CV mortality (95% CI, 1.54 to 8.83) and men with a 2.77-fold increased risk (95% CI, 1.18 to 6.49). CONCLUSIONS: Abnormalities of repolarization measured by PCA of the T-wave loop predict CV death in men and women, supporting use of PCA for quantifying repolarization abnormalities.

Arizona↗

PubMed1996

Quantitation of resonances in biological 31P NMR spectra via principal component analysis: potential and limitations.

This paper examines the potential and limitations of peak area quantitation of biological NMR spectra using principal component analysis (PCA), including its requirement for prior knowledge. The principles of the method are presented without in-depth mathematical treatment. PCA is illustrated for simulated data, 31P NMR spectra obtained consecutively over 1-2.5 days from perfused Rat-2 cells metabolizing the choline analogue phosphoniumcholine (Chop) and in vivo proton-decoupled, NOE-enhanced, three-dimensional CSI localized 31P NMR spectra of the liver of healthy volunteers. The results show that PCA can be used to quantitate strongly overlapping peaks without prior knowledge of the peak shapes or positions and to reconstruct spectra with significantly reduced noise variance. Two major limitations of PCA are presented: (1) PCA cannot separate peaks whose intensities are well correlated; (2) PCA is sensitive to differences in chemical shift and line-width of peaks between spectra. The discussion focuses on what knowledge of the biological and spectroscopic features of the samples and the principles of PCA is necessary for peak area quantitation via PCA.

Humans↗

PubMed1978

Principal component analysis of neural population response of knee joint proprioceptors in cat.

(1) A means of describing the response of neural populations based on principal component analysis is presented. The analysis produces response descriptions that indicate whether two states are distinguishable and suggest how to best distinguish between states. (2) Analysis of slowly adapting joint receptor data from the cat knee joint indicates that the joint receptors are capable of signalling limb position in the range from 150 degrees to 180 degrees of extension during an extension movement. They also provide information which indicates whether the tibia is twisted inward or outward in this range of angles. (3) The first principal component in the response to a constant velocity extension of 8 simultaneously active units described 86% of the total mean square discharge displayed by all 8 units. The first principal component is qualitatively similar to the responses observed in slowly adapting thalamic joint units.

Animals↗

PubMed2004

Adaptive consensus principal component analysis for on-line batch process monitoring.

As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.

Air Pollutants↗

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↗

PubMed2006

Degradation of malathion by Pseudomonas during activated sludge treatment system using principal component analysis (PCA).

Popular descriptive multivariate statistical method currently employed is the principal component analyses (PCA) method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen (DO) concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen (NH3-N) values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen (NO3-N) ranging between 1.301-2.940 mg/L and mean nitrate-nitrogen (NO3-N) ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components.

Biodegradation, Environmental↗

PubMed2006

Constructing socio-economic status indices: how to use principal components analysis.

Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

Data Collection↗

PubMed2004

Class separation of buildings with high and low prevalence of SBS by principal component analysis.

In this study, we were able to separate buildings with high and low prevalence of sick building syndrome (SBS) using principal component analysis. The prevalence of SBS was defined by the presence of at least one typical skin, mucosal and general (headache and fatigue) symptom. Data from the Swedish Office Illness Study describing the presence and level of chemical compounds in outdoor, supply, and room air, respectively, were evaluated together with information about the buildings in six models. When all data were included the most complex model was able to separate 71% of the high prevalence buildings from the low prevalence buildings. The most important variable that separates the high prevalence buildings from the low prevalence buildings was a more frequent occurrence or a higher concentration of compounds with shorter retention time in the high prevalence buildings. Elevated relative humidity in supply and room air and higher levels of total volatile organic compounds in outdoor and supply air were more common in high prevalence buildings. Ten building variables also contributed to the separation of the two classes of low and high prevalence buildings.

Air Pollutants↗

PubMed2006

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection.

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.

Analysis of Variance↗

PubMed2003

[Principal component analysis on ultrasound indexes of schistosomiasis and the assessment on prevalence rate].

OBJECTIVE: To explore the synthetical index for diagnosing schistosomiasis with ultrasound and to assess the prevalence rate with the index. METHODS: Ultrasound indexes of schistosomiasis Japonicum were analyzed by principal component analysis, and the synthetical indexes were assessed by ROC curve. RESULTS: Among the abnormal rates of the 6 indexes, the lowest was 1.6% comparing with the highest of 59.5%. Significant difference was noficed among the abnormal rates (chi(2) = 631.1, P < 0.01). The individual correlation of the six indexes to each other as will as with age distribution was significant (P < 0.05). The three principal components reflected the degree of pathological changes on liver and spleen. The first principal component was the factor reflecting the degree of liver pathological changes, and the second and third principal components reflected the degree of pathological changes on spleen. The synthetical index D(1) = 0.047X(1) + 0.428X(2) + 1.247X(3) + 0.095X(4) + 0.002X(5) + 0.213X(6) - 12.837 was found by adding the three weight principal components, and it's area under the ROC curve was 0.957. When -1.70 was taken as the critical value, the abnormal rate of population was 66.3%, close to the resident's actual prevalence rate 66.9%. CONCLUSION: Ultrasonography was considered as a method which could rapidly assessing the resident's prevalence rate in the endemic areas of schisitosomiasis Japonicum, and could also provide powerful information for development of strategy on chemotherapy.

Adolescent↗

PubMedDate not supplied

Principal component analysis of the conformational freedom within the EF-hand superfamily.

A database of nonredundant structures of EF-hand domains--i.e., pairs of helix-loop-helix motifs--has been assembled, and the six angles among the four helices re-determined. A principal component analysis of these angles allows us to use two such components (PC1 and PC2) to describe the system retaining 80% of the total variance. A PC2 against PC1 plot representation allows us to represent in a compact way the full range of structural diversity of EF-hand domains, their grouping into protein families, and the variation for each family upon calcium and peptide binding.

Amino Acid Motifs↗

PubMed2002

Genetic basis for systems of skeletal quantitative traits: principal component analysis of the canid skeleton.

Evolution of mammalian skeletal structure can be rapid and the changes profound, as illustrated by the morphological diversity of the domestic dog. Here we use principal component analysis of skeletal variation in a population of Portuguese Water Dogs to reveal systems of traits defining skeletal structures. This analysis classifies phenotypic variation into independent components that can be used to dissect genetic networks regulating complex biological systems. We show that unlinked quantitative trait loci associated with these principal components individually promote both correlations within structures (e.g., within the skull or among the limb bones) and inverse correlations between structures (e.g., skull vs. limb bones). These quantitative trait loci are consistent with regulatory genes that inhibit growth of some bones while enhancing growth of others. These systems of traits could explain the skeletal differences between divergent breeds such as Greyhounds and Pit Bulls, and even some of the skeletal transformations that characterize the evolution of hominids.

Animals↗

PubMed1985

The use of principal component analysis to resolve the spectra and kinetics of cytochrome c oxidase reduction by 5,10-dihydro-5-methyl phenazine.

The method of principal component analysis (PCA) was applied to the absorption-wavelength-time surfaces generated by rapid scanning stopped-flow spectrophotometry (RSSFS). The method was used to resolve the absorption surfaces generated during the reduction of cytochrome c oxidase by 5,10-dihydro-5-methyl phenazine (MPH) into the individual spectral shapes and time courses of the component chromophores. Two forms of resting cytochrome oxidase were used in these analyses: one that has its maximum absorption in the Soret region at 418 nm (418-nm species) and the other has its absorption maximum at 424 nm (424-nm species). A weighting scheme suitable for RSSFS data was developed. The optical absorption spectra obtained by W.H. Vanneste (1966, Biochemistry, 5:838-848) for the oxidase components were found to fit adequately as components of the experimental surfaces. Among these spectra were the oxidized forms of cytochromes a and a3 in the wavelength region 330-520 nm for the 418-nm species. Vanneste's spectral shape for the oxidized cytochrome a3 did not fit as a component in the spectrum of the 424-nm species. After accounting for the spectral shape of all components present, PCA provided a straightforward method for determining the separate time courses of each chromophore. We have found for both forms used that cytochrome a is reduced by MPH in the initial stages of the reaction, while cytochrome a3 is reduced in subsequent, slow phases. An important aspect of PCA is that it provided confirmation of the spectra of the various oxidase components without requiring the use of inhibitors or the use of simplifying mechanistic assumptions. The resolution of time profiles of strongly overlapping chromophores is also demonstrated.

Electron Transport Complex IV↗

PubMed1975

Principal component analysis of infraspecific variation in bacteria.

In certain types of ecological investigations it may be desirable to investigate infraspecific variation in bacteria. Principal component analysis is demonstrated to be satisfactory for this purpose. Hypothetical bacterial populations were used to show that such analysis can be used to compare collections of bacterial isolates taken at different times or from different sources. Alternatively, given n isolates, whether they represent a single bacterial population can be determined. The method is applied to authentic collections of bacteria in three separate analyses. The results are compatible with current taxonomic tenets.

Bacteria↗

PubMed2003

Use of principal component analysis to investigate the origin of heptadecenoic and conjugated linoleic acids in milk.

The aim of this paper was the application of principal component analysis (PCA) 1) to elucidate mutual metabolic relationships between milk fatty acids (FA) and 2) to illustrate the origin of milk FA, in particular C17:1 and cis-9,trans-11 conjugated linoleic acid. Data were combined from 3 experiments with lactating Holstein-Friesian cows offered diets based on grass or legume silage and concentrates. Loading plots of PCA based on milk FA concentrations showed 4 groups of milk FA, having similar precursors or metabolic pathways in the rumen and/or mammary gland: medium-chain saturated FA, de novo synthesized from acetate and beta-hydroxybutyrate; monoenoic milk FA, products of delta9-desaturase activity in the mammary gland; odd chain FA of rumen microbial origin and C18:0, n-6 C18:2, and n-3 C18:3 of dietary origin or the result of rumen biohydrogenation. Loading plots of PCA based on both milk and duodenal FA concentrations as well as on milk FA yields and duodenal FA flows further illustrated the importance of postabsorptive synthesis of the milk medium chain saturated and monoenoic FA and the direct absorption from the blood stream of odd chain FA, C18:0, n-6 C18:2, and n-3 C18:3. In all loading plots, milk oleic acid (C18:1) appeared intermediate between clusters of 18-carbon FA and monoenoic FA, illustrating its dual (dietary and endogenous production) origin. Milk C17:1 was suggested to be a desaturation product of C17:0, in common with other milk monoenoic FA. Finally, the PCA technique, based on milk FA patterns of one experiment, was applied to investigate factors determining cis-9,trans-11 conjugated linoleic acid concentrations in milk. Within the range of diets and cows studied here, we showed changes in cis-9,trans-11 conjugated linoleic acid to be mainly dependent on vaccenic acid supply and to a lesser extent on variation in desaturase activity.

3-Hydroxybutyric Acid↗

PubMed2005

Recursive principal components analysis.

A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack.

Humans↗

PubMed2004

Presentation of laboratory and sonoclot variables using principal component analysis: identification of hypo- and hypercoagulation in the HELLP syndrome.

The HELLP (Hemolysis, Elevated Liver enzyme, and Low Platelet) syndrome requires close monitoring of rapid changes in hemostasis. A bedside viscoelastic test--Sonoclot--was used together with coagulation, liver and hemolysis laboratory analyses in three parturients with the HELLP syndrome up to 10 days postpartum. Principal component analysis (PCA) was used to reduce the dimensionality of this multivariate problem and to visualize this process in a two-dimensional plot. It was possible to follow changes in these variables over time and to show how they changed in relation to 10 typical healthy parturients with normal laboratory and Sonoclot values as well as 20 simulated patients with hypo- or hypercoagubility. The effects of emergency delivery, correction of low plasma-antithrombin with plasma and antithrombin factor concentrate, plasma exchange and individualized dosages of low molecular weight heparin to counteract postpartum Sonoclot detected hypercoagulation were evaluated. The efficiency of each treatment strategy was visualized in the PCA plot by movement towards an area with normocoagulation. In conclusion, PCA of viscoelastic and laboratory coagulation analysis data facilitated the detection of both hypo- and hypercoagulative events and represents an alternative way to evaluate treatment strategies in patients with complex coagulative disorders, like the HELLP syndrome.

Adult↗

PubMed2003

Characterization of contaminated soil and groundwater surrounding an illegal landfill (S. Giuliano, Venice, Italy) by principal component analysis and kriging.

The characterization of a hydrologically complex contaminated site bordering the lagoon of Venice (Italy) was undertaken by investigating soils and groundwaters affected by the chemical contaminants originated by the wastes dumped into an illegal landfill. Statistical tools such as principal components analysis and geostatistical techniques were applied to obtain the spatial distribution of chemical contaminants. Dissolved organic carbon (DOC), SO4(2-) and Cl- were used to trace the migration of the contaminants from the top soil to the underlying groundwaters. The chemical and hydrogeological available information was assembled to obtain the schematic of the conceptual model of the contaminated site capable to support the formulation of major exposure scenarios, which are also provided.

Environmental Monitoring↗