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Results for “Principal Component Analysis”

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PubMed1998

Principal component analysis of minimal excitatory postsynaptic potentials.

'Minimal' excitatory postsynaptic potentials (EPSPs) are often recorded from central neurones, specifically for quantal analysis. However the EPSPs may emerge from activation of several fibres or transmission sites so that formal quantal analysis may give false results. Here we extended application of the principal component analysis (PCA) to minimal EPSPs. We tested a PCA algorithm and a new graphical 'alignment' procedure against both simulated data and hippocampal EPSPs. Minimal EPSPs were recorded before and up to 3.5 h following induction of long-term potentiation (LTP) in CA1 neurones. In 29 out of 45 EPSPs, two (N=22) or three (N=7) components were detected which differed in latencies, rise time (Trise) or both. The detected differences ranged from 0.6 to 7.8 ms for the latency and from 1.6-9 ms for Trise. Different components behaved differently following LTP induction. Cases were found when one component was potentiated immediately after tetanus whereas the other with a delay of 15-60 min. The immediately potentiated component could decline in 1-2 h so that the two components contributed differently into early (< 1 h) LTP1 and later (1-4 h) LTP2 phases. The noise deconvolution techniques was applied to both conventional EPSP amplitudes and scores of separate components. Cases are illustrated when quantal size (upsilon) estimated from the EPSP amplitudes increased whereas upsilon estimated from the component scores was stable during LTP1. Analysis of component scores could show apparent double-fold increases in upsilon which are interpreted as reflections of synchronized quantal releases. In general, the results demonstrate PCA applicability to separate EPSPs into different components and its usefulness for precise analysis of synaptic transmission.

Algorithms↗

PubMed2006

Automatic detection of emboli in the TCD RF signal using principal component analysis.

The transcranial Doppler (TCD) radio-frequency (RF) signal can provide additional information on events recorded during ultrasonic monitoring. Embolic signals appear as uniform and predictable shapes within the RF signal, enabling pattern recognition and image processing techniques to be used for their automated detection. This paper uses principal component analysis (PCA) to characterise the typical variation in embolic signal shape, within the RF signal, using training sets of in vitro and in vivo data. PCA techniques are then utilised to discriminate between previously unseen embolic and artifact signals. Although the results of this study show that the algorithms described in this paper do not yet have the accuracy required for their use in a clinical setting, it does demonstrate that this novel technique has the potential to be developed further.

Algorithms↗

PubMed2006

Correlations and complementarities in data and methods through Principal Components Analysis (PCA) applied to the results of the SPIn-Eco Project.

This paper demonstrates how the results from different methods can be interpreted on the basis of a statistical approach that can help find new hints in the evaluation of sustainability at the territorial level. The SPIn-Eco Project for the Province of Siena (Italy) is an example of an environmental sustainability assessment of an area using methods that are suitable for a large system: Ecological Footprint, Greenhouse Gas Inventory, Extended Exergy Analysis, Emergy Evaluation, and Remote Sensing. The calculation of many indicators, derived from these methods, has prompted us to use a statistical method (Principal Components Analysis, PCA) to understand the degree of similarity/congruence of the indicators (here we have examined 26 of them) and the possibility of recognizing patterns or clusters in the description of the 36 municipalities that compose the Province of Siena. Among the results, unexpectedly, emergy flow and the Ecological Footprint resulted as being completely uncorrelated, apparently due to the importance that the non-renewable part of the emergy holds in the evaluation. The municipalities of the province are considerably spread out over the graphs, even though that of Siena is quite far from the rest along the first dimension. In addition, we were able to distinguish between more homogeneous districts (sets of municipalities), such as Val di Merse and Val d'Orcia, and very diverse ones, such as Val d'Elsa and Val di Chiana.

Ecosystem↗

PubMed1989

Laminar cortical interactions during epileptic spikes studied with principal component analysis and physiological modeling.

The direct cortical responses (DCR) to electrical stimulation and electrically evoked interictal penicillin spikes (EIIS) were studied in the same rats using current source-density (CSD) analysis to directly compare regions of neuronal depolarization and hyperpolarization in neocortex. Principal component analysis (PCA) was further used to evaluate patterns of covariance in the CSD that were characteristic of interactions between pyramidal cell populations with spatially and temporally distinct transmembrane currents. A physical model was applied to the physiological interpretation of PCA results and the optimal model parameters used to estimate neuronal generators of recorded laminar field potentials. The data suggested that the DCR and EIIS were produced by the same neuronal circuit which could be represented by two anatomically distinct populations of pyramidal cells. The first of these populations was situated in the upper and middle layers (supragranular pyramidal neurons) and formed a dipolar CSD pattern that reversed polarity in layers II and III. The second deeper population (infragranular pyramidal neurons) extended throughout most of the cortical thickness and formed a dipolar CSD pattern that reversed polarity in layer V. We propose that excitatory intracortical connections of supragranular pyramidal cells may pathologically synchronize depolarization within the epileptic focus. In this way, supragranular pyramidal cells may provide a trigger mechanism for interictal spikes in neocortex.

Animals↗

PubMed2006

A study of polycyclic aromatic hydrocarbons concentrations and source identifications by methods of diagnostic ratio and principal component analysis at Taichung chemical Harbor near Taiwan Strait.

Fine (PM(2.5)) and Coarse (PM(2.5-10)) particulates concentrations of ambient air particle-bound polycyclic aromatic hydrocarbons (PAHs) were measured simultaneously from February 2004 to January 2005 at the Taichung Harbor (TH) sampling site near Taiwan of central Taiwan. Particle-bound polycyclic aromatic hydrocarbons (PAHs) were collected on quartz filters, the collected sample used soxhlet analytical method extracted with a dichloromethane (DCM)/n-hexane mixture (50/50, v/v) for 24h, and then the extracts were subjected to gas chromatography-mass spectrometric (GC-MS) analysis. The results indicated that vehicle emissions, coal combustion, incomplete combustion and pyrolysis of fuel and oil burning were the main source of PAHs near Taiwan Strait of central Taiwan. Diagnostic ratio and principal component analysis (PCA) were also used to characterize and identify PAHs emission source in this study.

Air Pollutants↗

PubMed2006

Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis.

AIMS/HYPOTHESIS: We devised a practical continuous score to assess the metabolic syndrome, and assessed whether this syndrome score predicts incident diabetes and cardiovascular disease. SUBJECTS AND METHODS: Among 5,024 participants of the Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) cohort, we defined a metabolic syndrome score by the first principal component (PC1), using only the correlations between continuous metabolic syndrome measures (glucose, waist circumference, triglycerides, and systolic blood pressure). This metabolic syndrome score was highly correlated with a similar score also including insulin and HDL cholesterol (r ( s )=0.94). Over 9 years of follow-up, incident diabetes and cardiovascular disease (CVD) were predicted by logistic regression using the simpler metabolic syndrome score. RESULTS: The means of the metabolic syndrome measures differed between men and women. Nevertheless, as the degree of variance explained and the PC1 coefficients were remarkably similar, we used a common metabolic syndrome score. The metabolic syndrome score explained 50% of the variance of the metabolic syndrome measures, and waist circumference had the highest correlation (0.59) with this score. Each standard deviation increase in the metabolic syndrome score was associated with a markedly increased age-adjusted risk of developing diabetes (odds ratios: men 3.4 [95% CI 2.6-4.4]; women 5.1 [3.6-7.2]) and with increased incident CVD of 1.7 (1.4-2.1) in men and 1.7 (1.0-2.7) in women. CONCLUSIONS/INTERPRETATION: Our results, which should be confirmed in other populations, suggest that it is possible to evaluate the risk of the metabolic syndrome in a pragmatic fashion with a continuous score, obtained from principal components analysis of the basic, continuous syndrome measures.

Adult↗

PubMed1985

Origin of the electroretinogram in the intact macaque eye--I. Principal component analysis.

Responses to 400 msec flashes of white light were recorded at various depths in the retina of the intact macaque eye. A statistical technique, principal component analysis (PCA), was used to isolate independent components from the LERG, using the changing contribution of a cell's response to the LERG with retinal depth. Two approaches were evaluated: first, PCA was performed on the complete LERG waveforms and, second, on small sections of the LERG. The first method yielded a component strongly resembling the receptor response, apart from some inconsistencies in the proximal layers, and a component in which the b-wave was the most prominent feature, but which still contained some other responses. Hence, the components were not suitable to describe the LERG in terms of responses of specific cell types. The second method uses PCA to determine whether or not the potential change within a small time window is accounted for by a single component. This method, which essentially uses the knowledge that different cell types respond with different delay times, yielded three components. These were identified as the receptor component, the b-wave and the d.c.-component. The voltage-depth profiles of these components were assessed.

Animals↗

PubMed2006

Principal component analysis of DNA oligonucleotide structural data.

The microstructure of a DNA helix is characterized by several base pair and base step parameters such as twist, rise, roll, propeller twist, etc., in addition to conformational parameters such as the backbone and the glycosidic torsion angles. Among these only a few, which are independent of all others and of each other, may be used to precisely characterize the helix. The problem however is to identify these independent parameters. We have used principal component analysis to identify a relatively small set of independent parameters, with which to characterize each DNA helix. We show that these principal components clearly discriminate between A and B DNA helical types. The calculations further suggest that the microstructure of a DNA helix is better characterized using dinucleotides.

Base Sequence↗

PubMed2003

Studies on organoleptic properties of food products from fresh egg and egg powder through principal component analysis.

The population which is below the poverty line is devoid of nutritious diet. Egg and milk are categorized as complete foods. The defensive organizations are situated in such remote places where fresh food material is not available. Keeping in view these problems, the study of organoleptic variables, viz., color, appearance, aroma, texture and taste in the food products of cake, omelet doughnut, coconut macaroon and mayonnaise from fresh egg and egg powder, was conducted. Principal component analysis was carried out. Organoleptic properties of doughnut prepared from egg powder were superior compared to fresh egg which had better sensory traits for coconut macaroon. The sensory traits like taste, texture and aroma were the most influential traits studied to pronouncing as a panel decision. It is proposed that fresh egg and egg powder should be preferred in the process of preparation of coconut macaroon and doughnut, respectively.

Color↗

PubMed2006

Principal components analysis corrects for stratification in genome-wide association studies.

Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.

Algorithms↗

PubMed1990

Use of principal components analysis to develop a composite score as a primary outcome variable in a clinical trial. The VA Cooperative Study Group on Cochlear Implantation.

This article describes the use of principal components analysis to derive a composite score from a battery of 24 audiologic tests. The composite score is being used as the primary outcome variable in a clinical trial comparing the efficacy of three cochlear implant devices for people with bilateral, profound hearing loss. The first principal component from the within-class pooled variance-covariance matrix over four time periods was chosen to establish the coefficients for the composite score. This component accounted for 61% of the total variance of the 24 audiologic tests. The first principal component had its largest coefficients associated with the most difficult audiologic tests. The mean composite score of all patients improved over time; some patients showed dramatic improvement. The changes in the composite score over time were also closely related to subjective impressions of implant performance by the patient, audiologist, and otolaryngologist.

Analysis of Variance↗

PubMedDate not supplied

The detection of abnormal neonatal cerebral haemodynamics using principal component analysis of the Doppler ultrasound waveform.

Doppler ultrasound recordings were made from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The maximum velocity waveforms were extracted using spectral analysis and further analyzed using the Pourcelot resistance index method and the principal component method. Principal component analysis was found to be superior to the currently widely used resistance index method and may significantly enhance the value of Doppler ultrasound for detecting abnormal cerebral haemodynamics in neonates.

Cerebral Arteries↗

PubMed2002

NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations.

We present a general procedure for automatic quantitation of a series of spectral peaks based on principal component analysis (PCA). PCA has been previously used for spectral quantitation of a single resonant peak of constant shape but variable amplitude. Here we extend this procedure to estimate all of the peak parameters: amplitude, position (frequency), phase and linewidth. The procedure consists of a series of iterative steps in which the estimates of position and phase from one stage of iteration are used to correct the spectra prior to the next stage. The process is convergent to a stable result, typically in less than 5 iterations. If desired, remaining linewidth variations can then be corrected. Correction of (typically) unwanted variations of these types is important not only for direct peak quantitation, but also as a preprocessing step for spectral data prior to application of pattern recognition/classification techniques. The procedure is demonstrated on simulated data and on a set of 992 (31)P NMR in vivo spectra taken from a kinetic study of rat muscle energetics. The proposed procedure is robust, makes very limited assumptions about the lineshape, and performs well with data of low signal-to-noise ratio.

Algorithms↗

PubMed2004

Peak purity determination with principal component analysis of high-performance liquid chromatography-diode array detection data.

A method is proposed for the determination of chromatographic peak purity by means of principal component analysis (PCA) of high-performance liquid chromatography with diode array detection (HPLC-DAD) data. The method is exemplified with analysis of binary mixtures of lidocaine and prilocaine with different levels of separation. Lidocaine and prilocaine have very similar spectra and the chromatograms used had substantial peak overlap. The samples analysed contained a constant amount of lidocaine and a minor amount of prilocaine (0.02-2 conc.%) and hence the focus was on determining the purity of the lidocaine peak in the presence of much smaller levels of prilocaine. The peak purity determination was made by examination of relative observation residuals, scores and loadings from the PCA decomposition of DAD data over a chromatographic peak. As a reference method, the functions for peak purity analysis in the chromatographic data system used (Chromeleon) were applied. The PCA method showed good results at the same level as the detection limit of baseline-separated prilocaine, outperforming the methods in Chromeleon by a factor of ten. There is a discussion of the interpretation of the result, with some comparisons with evolving factor analysis (EFA). The main advantage of the PCA method for determination of peak purity over methods like EFA lies in its simplicity, short time of calculation and ease of use.

Chromatography, High Pressure Liquid↗

PubMed1983

Principal component analysis: a suitable method for the 3-dimensional study of the shape, dimensions and orientation of dendritic arborizations.

Our study proposes an objective method of describing 3-dimensional dendritic arborizations of neurons in the best possible conditions. The method is based upon a particular exploitation of statistical "principal component analysis". For each arborization, 3 principal axes are calculated which are its axes of inertia. The first two axes define the "principal plane" of the arborization. The shape of the arborization is determined from the statistical distribution of its dendritic points along each of these axes. Shapes are quantified by using an "index of axialization" (a) and an "index of flatness" (p) both of which may vary from zero to 1. The dimensions of the arborization, "length" (1), "width" (w) and "thickness" (t) are also measured along the principal axes. Orientation of arborizations is quantified by considering the orientation of the first principal axis for axialized arborization (a close to 1) and/or the orientation of the principal plane for flattened arborizations (p close to 1). In both cases 2 angles (azimuth and polar angle) are calculated. For spherical arborizations (a and p close to 1), no orientation is significant. The significance level of the defined orientations is evaluated from the values of the shape indices. Several examples are illustrated and other existing methods are discussed.

Animals↗

PubMed2006

Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data.

Related organic impurities generally have approximately similar molar absorption coefficients (epsilon) due to their structural similarities. On the assumption that all peaks in an impurity profiling chromatogram have approximately the same maximum molar absorption coefficients (epsilon(max)) and the chromatogram contains one major peak and several much smaller ones, all of which are completely separated, integration of the summed score vectors from the principal component analysis (PCA) decomposition of high-performance liquid chromatography-diode array detection (HPLC-DAD) data will give areas that are quantitatively proportional to the actual content of the compounds. Due to the sequential nature of PCA, the first principal component (PC) will primarily be related to the main compound and all peaks showing a similar spectrum, while the second PC will be related to the impurities with a spectrum different from the main peak. Summing the two score vectors thus makes it possible to take account of different spectra in the score chromatogram, which make the method proposed give better quantitative estimates of the impurities than any single wavelength chromatogram. Multivariate curve resolution alternating least squares (MCR-ALS) is used for comparison. The results are presented for two examples of simulated HPLC-DAD data as well as for three examples of real HPLC-DAD data from impurity profiling. The results show that integration of the score chromatograms can handle differences in the unknown epsilon(max) of the peaks and take account of the different spectra of the impurity peaks, giving quantitative estimates of the content of the impurities that closely correspond to the reference values. The results obtained are also better than integration with the best possible separate wavelength. The method could be a straightforward approach to impurity profiling in order to obtain a good estimate of the content or relative response factors of small chromatographic impurity peaks without knowledge of their molar absorption coefficients and without any precalibration.

Chromatography, High Pressure Liquid↗

PubMedDate not supplied

Classification of environmental estrogens by physicochemical properties using principal component analysis and hierarchical cluster analysis.

A structurally diverse assortment of 60 environmental estrogens was divided into two main clusters ("A", "B") and a pair of subclusters ("C1", "C2") by applying principal component analysis to selected 1D and 2D molecular descriptors and subjecting the PCs to hierarchical cluster analysis. Although clustering was predicated solely on physicochemical properties, the dependence on particular physicochemical parameters of xenoestrogen binding affinities (pK(i)) to murine uterine cytosolic estrogen receptor (ER) proved greater for compounds within (sub)clusters than for compounds between (sub)clusters. Quantitative structure-binding affinity relationships derived using molecular descriptors and PCs suggested differences in the driving forces for xenoestrogen-ER binding for different (sub)clusters. The modeling power for xenoestrogen-ER binding affinities of a combination of TLSER and WHIM 3D indices was much greater than that of combinations of 1D and 2D molecular descriptors or the PCs derived therefrom. The clusterings obtained using PCs also proved applicable to the 3D-QSARs.

Chemical Phenomena↗

PubMed2006

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.

Amino Acids↗