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 163 records · Page 9Linked to original sources
PubMed2005

Effect of multivariate process instability on principal component analysis: a case study.

With the rising use of principal component analysis/partial least squares (PCA/PLS) in the process analytical technology (PAT) initiative of the pharmaceutical industry, it seems appropriate to view that approach from a statistical process control (SPC) perspective. The purpose of this study was to demonstrate the effect of process instability (ie, state of statistical out-of-control) on use of PCA/PLS. The demonstrated differences in results should encourage PCA/PLS users to incorporate SPC as an active part of their process analytical control (PAC) toolkit to check for stability prior to drawing conclusions based on PCA/PLS analysis.

Chi-Square Distribution↗
PubMed2002

Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals.

The purpose of this research is twofold. First is to extend the work of Smith (1992, 1996) and Smith and Miao (1991, 1994) in comparing item fit statistics and principal component analysis as tools for assessing the unidimensionality requirement of Rasch models. Second is to demonstrate methods to explore how violations of the unidimensionality requirement influence person measurement. For the first study, rating scale data were simulated to represent varying degrees of multidimensionality and the proportion of items contributing to each component. The second study used responses to a 24 item Attention Deficit Hyperactivity Disorder scale obtained from 317 college undergraduates. The simulation study reveals both an iterative item fit approach and principal component analysis of standardized residuals are effective in detecting items simulated to contribute to multidimensionality. The methods presented in Study 2 demonstrate the potential impact of multidimensionality on norm and criterion-reference person measure interpretations. The results provide researchers with quantitative information to help assist with the qualitative judgment as to whether the impact of multidimensionality is severe enough to warrant removing items from the analysis.

Adolescent↗
PubMed2005

Methodological issues in determining the dimensionality of composite health measures using principal component analysis: case illustration and suggestions for practice.

During the early steps of the construction of composite health measures, principal component analysis (PCA) is commonly used to identify 'latent' factors that underlie observed variables and to determine the dimensionality of the instruments. The determination of the number of components to retain is critical to PCA: it markedly influences the factorial model identified and further conditions the validity of the constructed instrument. However, many researchers developing composite health measures seem to be unaware of the importance of this determination. The purposes of the paper are to illustrate (1) the variability of the factorial models obtained by using different published rules (n = 10) for determining the number of components to retain in PCA applied to two quality-of-life datasets, and (2) the value of a careful and diversified approach to the problem of the number of components to retain in PCA that we suggest, instead of the unsatisfactory 'rule-of-thumb' that many researchers use. This involves: (1) using robust rules (including parallel analysis and minimum average partial procedure) to generate a set of possible values for the number of components to retain, (2) repeating the analysis across samples, (3) comprehensively assessing the models obtained, and (4) considering complementary methods to PCA and especially confirmatory factor analysis.

Factor Analysis, Statistical↗
PubMed2004

T-RFLP combined with principal component analysis and 16S rRNA gene sequencing: an effective strategy for comparison of fecal microbiota in infants of different ages.

The fecal microbiota of two healthy Swedish infants was monitored over time by terminal restriction fragment length polymorphism (T-RFLP) analysis of amplified 16S rRNA genes. Principal component analysis (PCA) of the T-RFLP profiles revealed that the fecal flora in both infants was quite stable during breast-feeding and a major change occurred after weaning. The two infants had different sets of microbiota at all sampling time points. 16S rDNA clone libraries were constructed and the predominant terminal restriction fragments (T-RFs) were identified by comparing T-RFLP patterns in the fecal community with that of corresponding 16S rDNA clones. Sequence analysis indicated that the infants were initially colonized mostly by members of Enterobacteriaceae, Veillonella, Enterococcus, Streptococcus, Staphylococcus and Bacteroides. The members of Enterobacteriaceae and Bacteroides were predominant during breast-feeding in both infants. However, Enterobacteriaceae decreased while members of clostridia increased after weaning. T-RFLP in combination with PCA and 16S rRNA gene sequencing was shown to be an effective strategy for comparing fecal microbiota in infants and pointing out the major changes.

Base Sequence↗
PubMed2005

Evaluation of river water quality monitoring stations by principal component analysis.

The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.

Calcium↗
PubMed2006

Noise perturbation in functional principal component analysis filtering for two-dimensional correlation spectroscopy: its theory and application to infrared spectra of a poly(3-hydroxybutyrate) thin film.

A method based on noise perturbation in functional principal component analysis (NPFPCA) is for the first time introduced to overcome the noise interference problem in two-dimensional correlation spectroscopy (2D-COS). By the systematic addition of synthetic noise to the dynamic multivariate spectral data, the functional principal component analysis (FPCA) described in this report is able to accurately determine which eigenvectors are representing significant signals instead of noise in the original data. This feature is especially useful for the data reconstruction and noise filtering. Reconstructed data resulted from the smooth eigenvectors can produce much more reliable 2D correlation spectra by removing the correlation artifacts from noise, which in turn enable more accurate interpretation of the spectral variations. The usefulness of this method is demonstrated with a theoretical framework and applications to the 2D correlation analyses of both simulated data and temperature-dependent reflection-absorption infrared spectra of a poly(3-hydroxybutyrate) (PHB) thin film.

Absorption↗
PubMed2005

Analysis of glass fragments by laser ablation-inductively coupled plasma-mass spectrometry and principal component analysis.

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is used to differentiate glass samples with similar optical and physical properties based on trace elemental composition. Laser ablation increases the number of elements that can be used for differentiation by eliminating problems commonly associated with dissolution and contamination. In this study, standard residential window and tempered glass samples that could not be differentiated by refractive index or density were successfully differentiated by LA-ICP-MS. The primary analysis approach used is Principal Component Analysis (PCA) of the complete mass spectrum. PCA, a multivariate analysis technique, provides rapid analysis of samples without time-consuming pair-wise comparison of calibrated analyses or prior knowledge of the elements present in the samples. Probabilities for positive association of the individual samples are derived from PCA. Utilization of the Q-statistic with PCA allowed us to distinguish all samples within the set to a certainty greater than the 99% confidence interval.

Journal Article↗
PubMed2007

Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: a principal component analysis of the human visual system.

This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second-level analysis subsequent to model-based General Linear Model (GLM)-type functional activation mapping. Functional homogeneity of the functional magnetic resonance imaging (fMRI) time series within and between clusters was examined on several levels of the visual network, starting from the level of individual clusters up to the network level comprising two or more distinct visual regions. On each level, the number of significant components was identified and compared with the number of clusters in the data set. Eigenimages were used to examine the regional distribution of the extracted components. It was shown that voxels within individual clusters and voxels located in bilateral homologue visual regions can be represented by a single component, constituting the characteristic functional specialization of the cluster(s). If, however, PCA was applied to time series of voxels located in functionally distinct visual regions, more than one component was observed with each component being dominated by voxels in one of the investigated regions. The model of functional connections derived by PCA was in accordance with the well-known functional anatomy and anatomical connectivity of the visual system. PCA in combination with conventional activation mapping might therefore be used to identify the number of functionally distinct nodes in an fMRI data set in order to generate a model of functional connectivity within a neuroanatomical network.

Adult↗
PubMed1997

Registration of dynamic dopamine D2 receptor images using principal component analysis.

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P<0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly 123I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the chi2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences.

Algorithms↗
PubMed1995

Commentary and opinion: I. Principal component analysis, variance partitioning, and "functional connectivity".

We briefly review the need for careful study of "variance partitioning" and "optimal model selection" in functional positron emission tomography (PET) data analysis, emphasizing the use of principal component analysis (PCA) and the importance of data analytic techniques that allow for heterogeneous spatial covariance structures. Using an [15O]water dataset, we demonstrate that--even after data processing--the intrasubject signal component of primary interest in baseline activation studies constitutes a very small fraction of the intersubject variance. This small intrasubject variance component is subtly but significantly changed by using analysis of covariance instead of scaled subprofile model processing before applying PCA. Finally, we argue that the concept of "functional connectivity" should be interpreted very generally until the relative roles of inter- and intrasubject variability in both disease and normal PET datasets are better understood.

Analysis of Variance↗
PubMed2004

A principal components analysis self-organizing map.

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

Neural Networks, Computer↗
PubMedDate not supplied

Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia.

Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm.

Cluster Analysis↗
PubMed1994

A principal components analysis of the Dissociative Experiences Scale in a substance abuse population.

A principal components analysis with a varimax rotation was performed on the Dissociative Experiences Scale using a substance abuse population. Results based on a combination of the eigenvalue greater than 1 criterion and factor interpretability indicated that a four-factor solution best fit the data. Implications for interpreting the Dissociative Experiences Scale with substance abusers were discussed.

Adult↗
PubMed1987

The utility of principal component analysis for the image display of brain lesions. A preliminary, comparative study.

Principal component analysis (PCA), a common tool from multivariate statistical analysis, has been implemented into the computer display system of a MR imaging device. PCA allows the calculation of images in which the information in a defined region of interest inherent in the basic acquired images is condensed. PCA image calculation has been applied to acquired MR studies of 13 patients with brain lesions. The appearance of the brain lesions on the resultant PCA images was scored in comparison to the acquired images before and after administration of Gd-DTPA as well as to other calculated images including T1, T2, hydrogen density, and contrast-optimized images. The conspicuity of a lesion and the number of distinguishable components within a lesion were slightly superior on PCA than on the acquired images. PCA is an analytical tool for MR imaging that should be helpful in revealing information that is inherent in, but not readily visible on, standard acquired MR images.

Brain Neoplasms↗
PubMed1992

Unrestricted principal components analysis of brain electrical activity: issues of data dimensionality, artifact, and utility.

Principal components analysis (PCA) was performed on the 1536 spectral and 2944 evoked potential (EP) variables generated by neurophysiologic paradigms including flash VER, click AER, and eyes open and closed spectral EEG from 202 healthy subjects aged 30 to 80. In each case data dimensionality of 1500 to 3000 was substantially reduced using PCA by magnitudes of 20 to over 200. Just 20 PCA factors accounted for 70% to 85% of the variance. Visual inspection of the topographic distribution of factor loading scores revealed complex loadings across multiple data dimensions (time-space and frequency-space). Forty-two non-artifactual factors were successful in classifying age, gender, and a separate group of 60 demented patients by linear discriminant analysis. Discrimination of age and gender primarily involved EP derived factors, whereas dementia primarily involved EEG derived factors. Thirty-eight artifactual factors were identified which, alone, could not discriminate age but were relatively successful in discriminating gender and dementia. The need to parsimoniously develop real neurophysiologic measures and to objectively exclude artifact are discussed. Unrestricted PCA is suggested as a step in this direction.

Adult↗
PubMed1996

Principal components analysis of the Physical Self-Efficacy Scale for a black sample.

Principal components analysis of an intercorrelation matrix for the Physical Self-efficacy Scale in an all black sample of 320 mostly confirmed the original validation study of Ryckman, Robbins, Thornton, and Cantrell who used a wholly white sample; however, the analysis identified items with factor loadings at criterion on more than one factor, one item that loaded on a different subscale, and additional factors. It is unknown whether differences in this sample are attributable to race or other influences. Further investigation is suggested.

Adolescent↗