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PubMed2006

Principal component and linear discriminant analysis of T1 histograms of white and grey matter in multiple sclerosis.

Twenty-three relapsing remitting multiple sclerosis (RRMS) patients and 14 controls were imaged to produce normal-appearing white and grey matter T1 histograms. These were used to assess whether histogram measures from principal component analysis (PCA) and linear discriminant analysis (LDA) out-perform traditional histogram metrics in classification of T1 histograms into control and RRMS subject groups and in correlation with the expanded disability status score (EDSS). The histograms were classified into one of two groups using a leave-one-out analysis. In addition, the patients were scanned serially, and the calculated parameters correlated with the EDSS. The classification results showed that the more complex techniques were at least as good at classifying the subjects as histogram mean, peak height and peak location, with PCA/LDA having success rates of 76% for white matter and 68%/65% for grey matter. No significant correlations were found with EDSS for any histogram parameter. These results indicate that there is much information contained within the grey matter as well as the white matter histograms. Although in these histograms PCA and LDA did not add greatly to the discriminatory power of traditional histogram parameters, they provide marginally better performance, while relying only on data-driven feature selection.

Brain↗

PubMed2002

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles.

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis.

Animals↗

PubMed1995

Spectral coherence in normal adults: unrestricted principal components analysis; relation of factors to age, gender, and neuropsychologic data.

This paper demonstrates, by means of Principal Components Analysis (PCA), an objective approach to the reduction of large data sets produced by multichannel spectral coherence analyses. Coherence data, gathered from 371 normal healthy adults using Hjorth/Laplacian referencing during waking eyes-open and eyes-closed states, were analyzed by "unrestricted" PCA where neither spatial nor temporal variance was folded into among subject variance. There was substantial data reduction with our 4416 initial coherence variables for each state reduced to just 150 factors containing approximately 80% of the variance reflecting a 30 fold concentration of information content. Varimax rotation of the first 40 factors, encompassing 50% of the total variance for both states, revealed loading patterns primarily bilateral with no hemispheric bias, relationships primarily between distant single electrode pairs, (although a single electrode to multiple electrode pattern was also observed), and involvement of all spectral bands. Elemental left to right and anterior to posterior coherence patterns, often used on an a priori basis for coherence studies, were not evident among the rotated factor loading patterns. On the basis of high loadings upon extra bipolar artifact channels, 32 factors accounting for approximately 40% of the variance were identified as reflecting artifactual coherence relationships. By multiple regression the 48 non-artifactual factor scores successfully predicted subject age. In general, coherence diminished with age, which may partly explain age-related EEG desynchronization in healthy adults. Coherence factors also predicted 6 of 10 neuropsychologic variables. Gender was successfully predicted by discriminant analysis. No global interpretations about coherence and gender or neuropsychologic function were possible, i.e., almost equal numbers of factors increased as decreased in males as females. PCA derived coherence factor scores are useful for subsequent statistical analyses, but their factor loading plots of cortical coupling may require more experience to fully interpret.

Adult↗

PubMed2006

Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method.

Several three-mode principal component models can be considered for the modelling of three-way, three-mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error.

Humans↗

PubMed2005

[Study on the optimal harvesting time of Rumex gmelini by analyzing the contents of principal components].

OBJECTIVE: To establish a method used for optimization of harvesting time and determine the best time for harvesting Rumex gmelini. METHOD: An HPLC method was applied to determinate the contents of seven active constituents(resveratrol, polydatin, chrysophanol 1-glucoside, nepodin, emodin, chrysophanol and physcion)of R. gmelini at different development stage. The result was analyzed by principal component analysis. RESULT: The accumulation of active constituents showed a regular pattern. CONCLUSION: The best harvesting time of R. gmelini is early July.

Anthraquinones↗

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↗

PubMed2005

Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems.

OBJECTIVES: We present a computer-aided diagnostic (CAD) system with textural features and image retrieval strategies for classifying benign and malignant breast tumors on various ultrasonic systems. Effective applications of CAD have used different types of texture analysis. Nevertheless, most approaches performed in a specific ultrasonic machine do not indicate whether the technique functions satisfactorily for other ultrasonic systems. This study evaluated a series of pathologically proven breast tumors using various ultrasonic systems. METHODS: Altogether, 600 ultrasound images of solid breast nodules comprising 230 malignant and 370 benign tumors were investigated. All ultrasound images were acquired from four diverse ultrasonic systems. The suspicious tumor area in the ultrasound image was manually chosen as the region-of-interest (ROI) subimage. Textural features extracted from the ROI subimage are supported in classifying the breast tumor as benign or malignant. However, the textural feature always behaves as a high-dimensional vector. In practice, high-dimensional vectors are unsatisfactory at differentiating breast tumors. This study applied the principal component analysis (PCA) to project the original textural features into a lower dimensional principal vector that summarized the original textural information. The image retrieval techniques were employed to differentiate breast tumors, according to the similarities of the principal vectors. The query ROI subimages were identified as malignant or benign tumors according to characteristics of retrieved images from the ultrasound image database. RESULTS: Using the proposed CAD system, historical cases could be directly added into the database without a retraining program. The area under the receiver-operating characteristics curve for the system was 0.970+/-0.006. CONCLUSION: The CAD system identified solid breast nodules with comparatively high accuracy in the different ultrasound systems investigated.

Area Under Curve↗

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↗

PubMed2005

Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts.

A new method for the assessment of the atrial fibrillatory wave (AFW) from the ECG is presented. This methodology is suitable for signals registered from Holter systems, where the reduced number of leads is insufficient to exploit the spatial information of the ECG. The temporal dependence of the bio-electrical activity were exploited using principal component analysis. The main features of ventricular and atrial activity were extracted, and several basis signals for each subspace were determined. Hence, the estimated (AFW) are reconstructed exclusively from the basis signals that formed the atrial subspace. Its main advantage with respect to adaptive template subtraction techniques was its robustness to variations in the QRST morphology, which thus minimised QRST residua. The proposed approach was first validated using a database of simulated recordings with known atrial activity content. The estimated AFW was compared with the original AFW, obtaining correlation indices of 0.774 +/- 0.106. The suitability of this methodology for real recordings was also proven, though its application to a set of paroxysmal AF ECGs. In all cases, it was possible to detect the main frequency peak, which was between 4.6 Hz and 6.9 Hz for the patients under study.

Algorithms↗

PubMed2006

Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.

Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.

Aged↗

PubMed2004

Evaluation of the salt accumulation process during inundation in water resource of Contas river basin (Bahia-Brazil) applying principal component analysis.

The high salinization in some reservoirs of the Contas river basin (Bahia-Brazil) has been erroneously attributed only to concentration by evaporation. However, recent studies of this basin have shown that in period of intense rainfalls, occur an increase of the saline concentration in the flowing rivers of the reservoirs. The application of statistical methods (cluster and principal components analysis) have shown that this fact can be attributed to the discharge of saline waters from the small reservoirs of every drained area, provoked by inundation, is also an important factor in the salinization process. Thus the study of the geochemical variables: Na(+), K(+), Ca(2+), Mg(2+), Cl(-), SO(4)(2-) and CO(3)(2-), showed one group formed by Na(+) and Cl(-), attributed to the discharge of saline water provoked by inundation from a small reservoir, and a second group constituted by Ca(2+), Mg(2+), K(+) and SO(4)(2-), due to an increase provoked by the evaporation in the salinization process.

Disasters↗

PubMed2006

Improvements in the search for potential biomarkers by proteomics: application of principal component and discriminant analyses for two-dimensional maps evaluation.

In this study, we evaluated if the application of multivariate analysis on the data obtained from two-dimensional protein maps could mean an improvement in the search for protein markers. First, we performed a classical proteomic study of the differential expression of serum N-glycoproteins in colorectal cancer patients. Then, applying principal component analysis (PCA) we assessed the utility of the 2-D protein pattern and certain subsets of spots as a tool to distinguish control and case samples, and tested the accuracy of the classification model by linear discriminant analysis (LDA). On the other hand we looked for altered spots by univariate statistics and then analysed them as a cluster by PCA and LDA. We found that those proteins combined presented a theoretical sensitivity and specificity of 100%. Finally, the spots with known protein identity were analysed by multivariate methods, finding a subgroup that behaved as the most obvious candidates for further validation trials.

Biomarkers↗

PubMed2004

Feature selection of stabilometric parameters based on principal component analysis.

This study addresses the challenge of identifying the features of the Centre of pressure (COP) trajectory that are most sensitive to postural performance, with the aim of avoiding redundancy and allowing a straightforward interpretation of the results. Postural sway in 50 young, healthy subjects was measured by a force platform. Thirty-seven stabilometric parameters were computed from the one-dimensional and two-dimensional COP time series. After normalisation to the relevant biomechanical factors, by means of multiple regression models, a feature selection process was performed based on principal component analysis. Results suggest that COP two-dimensional time series can be primarily characterised by four parameters, describing the size of the COP path over the support surface; the principal sway direction; and the shape and bandwidth of the power spectral density plot. COP one-dimensional time series (antero-posterior (AP) and medio-lateral (ML)) can be characterised by six parameters describing COP dispersion along the AP direction; mean velocity along the ML and AP directions; the contrast between ML and AP regulatory activity; and two parameters describing the spectral characteristics of the COP along the AP direction. On the basis of the results obtained, some guidelines are suggested for the choice of stabilometric parameters to use, with the aim of promoting standardisation in quantitative posturography.

Adult↗

PubMed1982

Principal components of craniofacial growth for white Philadelphia males and females between 6 and 22 years of age.

Three principal components, explaining 83 percent of the common variation for 999 males and females between 6 and 22 years of age, describe ontogenetic patterns of relationship for seven facial dimensions, including sella-nasion, sella-basion, nasion-prosthion, infradentale-menton, articulare-gnathion, gonion-gnathion, and articulare-gonion. Accounting for 65 percent of the variation, a general component associated with both size and shape defines size-required changes in proprotion during growth. Independent patterns of regional variation associated with alveolar remodeling (second component) and condylar growth (third component) describe specific sources of facial modification. Mean multivariate component scores reveal that sexual dimorphism, which progressively favors males over females with age, results from accumulating differences in size and related proportional changes in shape. The timing of the condylar growth spurt, as evident from variation in ramus height, produces secondary dimorphism which diminishes following the adolescent phase in males. Significant age effects are indicated for alveolar remodeling and mandibular growth of the condyle.

Adolescent↗

PubMed1979

On the independence of P300 and the CNV: a short critique of the principal components analysis of Donchin et al. (1975).

Donchin, Tueting, Ritter, Kutas and Heffley (1975) present evidence from a principal components analysis (PCA) that the CNV and P300 are independent. This short critique points out a number of erros in their PCA and presents a reworking of their analysis. A number of further aspects of Donchin et al.'s paper are also discussed. The general value of this contribution in drawing the attention of EP researchers to potential sources of error in the application of factor analysis is emphasized.

Contingent Negative Variation↗

PubMedDate not supplied

Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds.

Principal component analysis (PCA) is a favorite tool in chemometrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%.

Journal Article↗

PubMed2004

A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.

The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. We have used two-dimensional gel electrophoresis (2-DE), oligonucleotide microarrays and principal component analysis (PCA) to characterize the U937 cell line as a model system for the differentiation of monocytes into macrophages. A total of 226 differentially expressed proteins were found, of which 41 were selected by PCA for identification using matrix-assisted laser desorption/ionization tandem mass spectrometry. Based on the PCA results, three marker proteins were selected for confirmation of differential expression using Western blot and quantitative real time-PCR. The selected marker proteins were: gamma interferon inducible lysosomal thiol reductase, cathepsin D and adipocyte-fatty acid binding protein. All three proved to be good differentiation markers for macrophage maturation of U937 cells as well as peripheral blood-derived macrophages. The transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. These include osteospontin, matrix metalloproteinase 9, and HC-gp39. Our results show that the characteristics of U937 macrophages resemble those of inflammatory (exudate) macrophages, exemplified by the down-regulation of 5' nucleotidase and the up-regulation of leucine aminopeptidase mRNAs. In conclusion, using the powerful combination of transcriptomics, 2-DE and PCA, our results show that U937 cells differentiated by PMA treatment are an excellent model system for monocyte derived macrophage generation from blood.

Cell Differentiation↗

PubMed2003

Temperature-dependent near-infrared spectra of bovine serum albumin in aqueous solutions: spectral analysis by principal component analysis and evolving factor analysis.

Fourier transform near-infrared (FT-NIR) spectra have been measured for bovine serum albumin (BSA) in an aqueous solution (pH 6.8) with a concentration of 5.0 wt% over a temperature range of 45-85 degrees C. Not only conventional spectral analysis methods, such as second-derivative spectra and difference spectra, but also chemometrics, such as principal component analysis (PCA) and evolving factor analysis (EFA), have been employed to analyze the temperature-dependent NIR spectra in the 7500-5500 and 4900-4200 cm-1 regions of the BSA aqueous solution. Intensity changes of bands in the 7200-6600 cm-1 and 4650-4500 cm-1 regions in the difference spectra indicate variations of the hydration and secondary structure of BSA in the aqueous solution, respectively. The plot of a band intensity at 7080 cm-1 in the different spectra shows a clear turning point at 63 degrees C, revealing that a significant change in the hydration occurs at about 63 degrees C. The forward and backward eigenvalues (EVs) from EFA suggest that marked changes in the hydration and secondary structure of BSA take place in the temperature ranges of 61-65 degrees C and 59-63 degrees C, respectively. In addition, the temperature of 71 degrees C marked in the EFA plots may correspond to the onset temperature of increase in the intermolecular beta-sheet structure.

Animals↗