PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Principal Component Analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
PubMed1975

Selecting key parameters in pharmaceutical formulations by principal component analysis.

The role of principal component analysis in the selection of pharmaceutical formulations is presented. The objective and the procedure of the analysis are discussed in detail. The technique was successfully applied to a system consisting of 10 response variables (tablet properties). Analysis of the results showed that the first component (dissolution) and components one and two together (dissolution and disintegration) contributed 95.4 and 99.3%, respectively, to the overall information about the formulations and that eight of 10 response parameters contributed nothing further to the overall information. The results obtained from this method of analysis may be found useful for achieving economy in both cost and time of measuring response. Principal component analysis also provides a basis for understanding the underlying mechanism of the system under consideration.

Computers↗
PubMedDate not supplied

Principal component analysis of nonlinear chromatography.

Principal component analysis (PCA) has been used for the modeling of nonlinear chromatography under overload conditions. A 10-fold range of crude erythromycin samples were loaded onto columns with different stationary-phase chemistries (2 polystyrene, 1 methacrylate) in direct proportion to the bed volumes. The elution profiles indicated slightly concave isotherms for the polystyrene supports and a convex Langmuirian isotherm for the methacrylic support used. The principal component models accounted for over 98% of the original variance in the data for all three columns and were able to give excellent models of complete chromatograms in the absence of first-principle models or physicochemical data. Correlations between sample mass and the principal component scores were made for each that were consistent for the column types despite the different geometries and stationary phases. Linear relationships with high correlation coefficients were observed when the scores of the same principal component were compared between columns. Such correlations offer considerable potential for modeling of nonlinear chromatography.

Chromatography↗
PubMed2006

Comment on: "Energy landscape of a small peptide revealed by dihedral angle principal component analysis".

The dihedral angle principal component analysis method published recently by Mu, Nguyen, and Stock, is shown to produce distortions of the free energy landscape due to the neglect of constraints in the coordinates. It is further shown that these distortions can create artificial minima and energy barriers. The rugged energy landscape that the authors find for a small peptide chain might thus be an artifact of their method.

Energy Transfer↗
PubMed2005

Negative impact of noise on the principal component analysis of NMR data.

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.

Adenosine Triphosphate↗
PubMed2001

Evaluation of dissolution profiles using principal component analysis.

The performance of principal component analysis (PCA) for the evaluation of dissolution profiles is examined and compared with other methods such as the similarity factor and the calculation of the area under the curve. Both simulated and real data from the pharmaceutical industry are used. The PCA scores plots of the dissolution curves provide information about the between- and within-batch variations. Differences in level or shape can be observed in the first two principal components (PCs). Irrelevant irregularities, which have a strong influence on the similarity factor, are neglected in PC1/PC2. To detect outliers in a set of dissolution curves, PCA was preferred above Hotelling's T2 test. In general, PCA is found to be a useful technique to examine dissolution data visually, but however, it does not contain criteria to decide if batches are similar or not. This can be done by combining PCA with the resampling with replacement or bootstrap method to construct confidence limits.

Solubility↗
PubMed2001

NMR spectral quantitation by principal component analysis.

The use of principal component analysis (PCA) for simultaneous spectral quantitation of a single resonant peak across a series of spectra has gained popularity among the NMR community. The approach is fast, requires no assumptions regarding the peak lineshape and provides quantitation even for peaks with very low signal-to-noise ratio. PCA produces estimates of all peak parameters: area, frequency, phase and linewidth. If desired, these estimates can be used to correct the original data so that the peak in all spectra has the same lineshape. This ability makes PCA useful not only for direct peak quantitation, but also for processing spectral data prior to application of pattern recognition/classification techniques. This article briefly reviews the theoretical basis of PCA for spectral quantitation, addresses issues of data processing prior to PCA, describes suitable and unsuitable datasets for PCA applications and summarizes the developments and the limitations of the method.

Magnetic Resonance Spectroscopy↗
PubMed2004

Metabolomic differentiation of Cannabis sativa cultivars using 1H NMR spectroscopy and principal component analysis.

The metabolomic analysis of 12 Cannabis sativa cultivars was carried out by 1H NMR spectroscopy and multivariate analysis techniques. Principal component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples by principal component 1 (PC1) and principal component 3 (PC3) in cannabinoid fraction. The loading plot of PC value obtained from all 1)H NMR signals shows that Delta9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) are important metabolites to differentiate the cultivars from each other. The discrimination of the cultivars could also be obtained from a water extract containing carbohydrates and amino acids. The level of sucrose, glucose, asparagine, and glutamic acid are found to be major discriminating metabolites of these cultivars. This method allows an efficient differentiation between cannabis cultivars without any prepurification steps.

Asparagine↗
PubMed2005

Accounting for probe-level noise in principal component analysis of microarray data.

MOTIVATION: Principal Component Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of high-dimensional datasets. However, in its standard form, it does not take into account any error measures associated with the data points beyond a standard spherical noise. This indiscriminate nature provides one of its main weaknesses when applied to biological data with inherently large variability, such as expression levels measured with microarrays. Methods now exist for extracting credibility intervals from the probe-level analysis of cDNA and oligonucleotide microarray experiments. These credibility intervals are gene and experiment specific, and can be propagated through an appropriate probabilistic downstream analysis. RESULTS: We propose a new model-based approach to PCA that takes into account the variances associated with each gene in each experiment. We develop an efficient EM-algorithm to estimate the parameters of our new model. The model provides significantly better results than standard PCA, while remaining computationally reasonable. We show how the model can be used to 'denoise' a microarray dataset leading to improved expression profiles and tighter clustering across profiles. The probabilistic nature of the model means that the correct number of principal components is automatically obtained.

Algorithms↗
PubMed2001

The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data.

By using principal components analysis (PCA) we demonstrate here that the information relevant to tumor line classification linked to the activity of 1375 genes expressed in 60 tumor cell lines can be reproduced by only five independent components. These components can be interpreted as cell motility and migration, cellular trafficking and endo/exocytosis, and epithelial character. PCA, at odds with cluster analysis methods routinely used in microarray analysis, allows for the participation of individual genes to multiple biochemical pathways, while assigning to each cell line a quantitative score reflecting fundamental biological functions.

Cell Movement↗
PubMed2005

Comparative study of face recognition techniques that use joint transform correlation and principal component analysis.

Face recognition based on principal component analysis (PCA) that uses eigenfaces is popular in face recognition markets. We present a comparison between various optoelectronic face recognition techniques and a PCA-based technique for face recognition. Computer simulations are used to study the effectiveness of the PCA-based technique, especially for facial images with a high level of distortion. Results are then compared with various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical-correlator-based neural networks, and pose-estimation-based correlation.

Algorithms↗
PubMed2006

New method for spectral data classification: two-way moving window principal component analysis.

Two-way moving window principal component analysis (TMWPCA), which considers all possible variable regions by using variable and sample moving windows, is proposed as a new spectral data classification method. In TMWPCA, the similarity between model function and the index obtained by variable and sample moving windows is defined as "fitness". For each variable region selected by a variable moving window, the fitness is obtained through the use of a model function. By maximizing the fitness, an optimal variable region can be searched. A remarkable advantage of TMWPCA is that it offers an optimal variable region for the classification. To demonstrate the potential of TMWPCA, it has been applied to the classification of visible-near-infrared (Vis-NIR) spectra of mastitic and healthy udder quarters of cows measured in a nondestructive manner. The misclassification rate of TMWPCA has been compared with those of other chemometric methods, such as principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and principal discriminant variate (PDV). TMWPCA has yielded the lowest misclassification rate. The result indicates that TMWPCA is a powerful tool for the classification of spectral data.

Journal Article↗
PubMed1992

Composite index of skeletal mass: principal components analysis of regional bone mineral densities.

Principal components analysis is a statistical method that is used to reduce and explore data to facilitate further analyses. This method was applied to bone mineral densities measured at seven sites in 109 black and 44 white women, ages 22-80, at an internal medicine clinic in urban Detroit. We excluded subjects with a history of diseases or drugs known to affect bone metabolism. Principal components analysis was used to summarize the interrelationship of the densities and yielded two major results. First, the seven site measurements were reduced to a single, composite index (PC1) of skeletal mass that accounted for 73% of the variation in density among subjects. PC1 had roughly equal weights among the sites. A second combination of the seven sites indicated that the contrast between axial and appendicular regional densities accounted for another 10% of the variation among subjects. In investigating the relationship of density to age, body mass index, and ethnic group, we found that the principal components composite index had a stronger correlation with age (r = -0.58) and with body mass index (r = 0.34) than almost all of the regional densities. Black-white differences were larger for the composite index than for any single site density. A multiple regression of the composite index on ethnicity, body mass index, and age yielded a larger R2 (0.46) than any of the individual site densities. The second principal component, although of theoretical interest, showed a minimal ability to discriminate among subjects using the three independent variables of this study.

Absorptiometry, Photon↗
PubMed1990

Characterization of premotor interneurones by their input patterns--application of principal component analysis to cat cervical interneurones.

Principal component analysis of input patterns of cat C6-C8 interneurones (300 cells) revealed that identified premotor interneurones (11 cells) activated from skin afferents and projecting to T1 motoneurones possessed a special input pattern, characterized by restricted distribution on the plane of the first (Prin 1) versus second (Prin 2) principal component (high positive values of both components). These premotor neurones were located mostly in laminae V-VI. Among other laminae V-VI cells descending in the lateral funiculus to T1 similar to such premotor neurones, there were cells distributed similarly on the Prin 1-2 plane. Further, a majority of interneurones antidromically activated from the T1 motor nucleus at low thresholds also showed a distribution on the plane similar to the premotor neurones. We suggest that premotor neurones of this input pattern constitute a major group among laminae V-VI premotor neurones projecting to T1.

Animals↗
PubMed1997

Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition.

Principal component analysis (PCA) by singular value decomposition (SVD) may be used to analyze an epoch of a multichannel electroencephalogram (EEG) into multiple linearly independent (temporally and spatially noncorrelated) components, or features; the original epoch of the EEG may be reconstructed as a linear combination of the components. The result of SVD includes the components, expressible as time series waveforms, and the factors that determine how much each component waveform contributes to each EEG channel. By omission of some component waveforms from the linear combination, a new EEG can be reconstructed, differing from the original in useful ways. For example, artifacts can be removed and features such as ictal or interictal discharges can be enhanced by suppressing the remainder of the EEG. We developed a variation of this technique in which the factors that reconstruct the modified EEG from the original are stored as a matrix. This matrix is applied to multichannel EEG at successive times to create a new EEG continuously in real time, without redoing the time-consuming SVD. This matrix acts as a spatial filter with useful properties. We successfully applied this method to remove artifacts, including ocular movement and electrocardiographic artifacts. Removal of myogenic artifacts was much less complete, but there was significant improvement in the ability to visualize underlying activity in the presence of myogenic artifacts. The major limitations of the method are its inability to completely separate some artifacts from cerebral activity, especially when both have similar amplitudes, and the possibility that a spatial filter may distort the distribution of activities that overlap with the artifacts being removed.

Data Interpretation, Statistical↗
PubMed1985

Qualitative organic analysis. I. Identification of drugs by principal components analysis of standardized thin-layer chromatographic data in four eluent systems.

Principal component analysis of standardized RF values in four eluent systems [ethyl acetate-methanol-30% ammonia (85:10:15), cyclohexane-toluene-diethylamine (65:25:10), ethyl acetate-chloroform (50:50) and acetone, with the plate dipped in potassium hydroxide solution] provided a two-component model which accounts for 73% of the total variance. The "scores" plot allowed the restriction of the range of inquiry to a few candidates. This result is of great practical significance in analytical toxicology, especially when account is taken of the cost, the time, the analytical instrumentation and the simplicity of the calculations required by the method.

Chromatography, Thin Layer↗
PubMed2004

Principal component analysis of physical, color, and sensory characteristics of chicken breasts deboned at two, four, six, and twenty-four hours postmortem.

The effects of various postchill deboning times on functional, color, yield, and sensory attributes of broiler breast meat were determined. Broiler breast muscles were deboned at 2, 4, 6, and 24 h postmortem, and pH, color change, cooking yield, shear force values, and sensory traits of the breast meat were recorded. Data were examined by multivariate data analysis, namely principal component analysis (PCA). Averages of 13 variables (pH, delta a*, shear force, and sensory attributes of cardboardy, wet feathers, springiness, cohesiveness, hardness, moisture release, particle size, bolus size, chewiness, and metallic aftertaste-afterfeel) decreased gradually as deboning time increased from 2 to 24 h, especially for shear values after 4 h of postmortem aging. Univariate correlation coefficients among 24 variables indicated several significant correlations. Warner-Bratzler shear force had high positive correlations with 5 sensory texture attributes (cohesiveness, hardness, particle size, bolus size, and chewiness). The parameters of pH, delta L*, delta a*, delta b*, and cooking yield were not obviously correlated with shear force values or any of the 18 sensory characteristics. PCA score plot showed no clear separation of the breast muscles deboned at different postmortem times, but it was still possible to differentiate them. The loading biplot suggested that 18 variables were effective in sample differentiation, including delta L*, shear force, cooking yield, 6 sensory flavor attributes (brothy, cardboardy, wet feathers, blood/serumy, salty, and sour), all sensory texture attributes except springiness, and all afterfeel-aftertaste properties.

Animals↗
PubMed2006

Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems.

Computer Simulation↗