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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↗

Craniofacial morphology: a principal component analysis.

A series of 56 measurements was derived from lateral cephalometric radiographs of a large sample of subjects. These measurements were subjected to a principal-component analysis which resulted in a series of six components (factors). These factors, represented in general terms and in rank order of their percentage sample variability were as follows: Factor 1. Vertical facial characteristics Factor 2. Anteroposterior aspects of facial morphology Factor 3. Midfacial and dental protrusion Factor 4. Relationship of the mandible and dentition to the profile Factor 5. Horizontal base-line relationships (internal or deep) Factor 6. Maxillary incisor relationships These principal components and the variables contained within them were shown to have sex and age interactions. A longitudinal study of the principal component changes with age was then undertaken. Demonstrable age changes were verified for Factors 1, 2, and 3, and Factors 1 and 3 were observed to show patterns of change which were statistically different from each other and the remaining principal components. An orthodontically treated sample of patients was also assessed for factor changes. Factors 1 and 2 were found to show statistically reliable changes resulting from treatment and/or growth. The remaining four factors showed no statistically supportable alteration. The data-reduction method involving a principal-component analysis would seem to have potential research and clinical applications.

Adolescent↗

Differentiation of bovine and porcine gelatins using principal component analysis.

Gelatin is a collagen derivative, which has a large application in the pharmaceutical, food and adhesive industries as well as photography. The large similarity in structure and properties of gelatins from different origins makes their differentiation difficult. Certain chemometric methods, such as principal component analysis (PCA), can help to classify and characterize gelatin components. In this study 14 bovine and 5 porcine gelatins were examined. The analysis procedure involved complete hydrolysis of samples by classic acid hydrolysis in order to release their amino acid residues. Separation and determination of amino acids was achieved by reversed-phase (RP) HPLC following pre-column derivatisation. Orthophtaldialdehyde (OPA) and 4-chloro-7-nitro benzofurazane (NBD-Cl) were used as derivatisation reagents. From the 20 peaks detected by HPLC analysis, one was very typical in bovine gelatin. Peak height, area, area percentage and width were used to make matrixes. Principal component analysis with the MATLAB program was used to differentiate these gelatins. PCA on matrix of height, width and total matrix were resulted in good differentiation between bovine and porcine gelatins.

Animals↗

Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer.

Performance evaluation of principal component analysis (PCA) of dynamic F-18-FDG-PET studies of patients with recurrent colorectal cancer. Principal component images (PCI) of 17 iteratively reconstructed data sets were visually and quantitatively evaluated. The F-18-FDG compartment model parameters were estimated using polynomial regression. All structures were present in PCI1. PCI2 was correlated with the vascular component and PCI3 with the tumor. The vessel density in the tumor was estimated with a correlation coefficient equal to 0.834. PCA supports the visual interpretation of dynamic F-18-FDG-PET studies, facilitates the application of compartment modeling and is a promising quantification technique.

Colorectal Neoplasms↗

Quantitative morphological study of microglial cells in the ischemic rat brain using principal component analysis.

Pathogenic stimuli induce alterations in the morphology of microglial cells. We analysed changes in lectin-stained cells on the 1st, 3rd, 7th or 14th day after transient global ischemia. Three areas differing in the degree of microglial reaction were selected for analysis: the upper cerebral cortex, the hippocampal CA1 area, and the hilus of the dentate gyrus. Nine morphological parameters, including fractal dimension, lacunarity, self-similarity range, solidity, convexity and form factor were determined. Then the resultant data were processed using principal component analysis (PCA). We found that the two first principal components together explained more than 73% of the observed variability, and may be sufficient both to describe the morphological diversity of the cells, and to determine the dynamics and direction of the changes. In both hippocampal areas, the transformation to hypertrophied and phagocytic cells was observed, but changes in the hilus were faster than in the CA1. In contrast, in the cortex, a microglial reaction was characterised by an increase in the complexity of processes. The results presented show that the quantitative morphological analysis can be an effective tool in research on the reactive behaviour of microglia and, particularly, in the detection of small and early changes in the cells.

Animals↗

Classification of macular and optic nerve disease by principal component analysis.

In this study, pattern electroretinography (PERG) signals were obtained by electrophysiological testing devices from 70 subjects. The group consisted of optic nerve and macular diseases subjects. Characterization and interpretation of the physiological PERG signal was done by principal component analysis (PCA). While the first principal component of data matrix acquired from optic nerve patients represents 67.24% of total variance, the first principal component of the macular patients data matrix represents 76.81% of total variance. The basic differences between the two patient groups were obtained with first principal component, obviously. In addition, the graphic of second principal component vs. first principal component of optic nerve and macular subjects was analyzed. The two patient groups were separated clearly from each other without any hesitation. This research developed an auxiliary system for the interpretation of the PERG signals. The stated results show that the use of PCA of physiological waveforms is presented as a powerful method likely to be incorporated in future medical signal processing.

Adult↗

Principal component analysis for the identification of pollution sources in mussel survey by trace metals.

Principal component analysis has been applied to analyze the correlation matrix obtained from a 8 X 43 data matrix. The 8 trace metals are Mn, Co, Ni, Cu, Zn, Cd, Hg, Pb, which are contained in the soft part of mussels (Mytilus galloprovincialis Lamarck). Mussels were sampled from two sites in the Gulf of Trieste. In both samples, 76-78% of the total variance is explained by the four principal components. The orthogonally rotated factor matrix indicates that Co and Ni are bonded to the first principal component and Cd and Pb to the first (site 2) or second principal component (site 1). The origin of trace metals in the soft part of mussels from the Gulf of Trieste is discussed.

Animals↗

Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates.

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin-resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease.

Cluster Analysis↗

Differential gene expression profiles and identification of the genes relevant to clinicopathologic factors in colorectal cancer selected by cDNA array method in combination with principal component analysis.

The clinical outcome of patients with colorectal cancer frequently varies even if they are at the same clinicopathologic stage. Alternative superior tumor markers of colorectal cancer are needed for prediction of clinical outcome. To clarify the regulatory factors in colorectal cancers, we examined differential expression profiles using cDNA macroarray technique with surgically resected specimens obtained from the patients with colorectal cancer. The gene profiles by an average-linkage hierarchical clustering analysis were found to be almost separable into two groups: tumor group and normal mucosa group. The relationship between several clinicopathologic factors and cancer related genes were investigated by using statistical analyses including principal component analysis (PCA). c-myc-binding protein MM-1, and c-jun proto-oncogene were identified as possible markers of tumor histology and clinical prognosis and early growth response protein 1 (EGR1) was selected to play an important role in progression of clinical stage. We conclude that, with PCA method, we successfully selected the genes relevant to clinicopathologic factors using limited population of clinical samples.

Aged↗

Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework.

Principal component analysis (PCA) is one of several structure-seeking multivariate statistical techniques, exploratory as well as inferential, that have been proposed recently for the characterization and detection of activation in both PET and fMRI time series data. In particular, PCA is data driven and does not assume that the neural or hemodynamic response reaches some steady state, nor does it involve correlation with any pre-defined or exogenous experimental design template. In this paper, we present a generalized linear systems framework for PCA based on the singular value decomposition (SVD) model for representation of spatio-temporal fMRI data sets. Statistical inference procedures for PCA, including point and interval estimation will be introduced without the constraint of explicit hypotheses about specific task-dependent effects. The principal eigenvectors capture both the spatial and temporal aspects of fMRI data in a progressive fashion; they are inherently matched to unique and uncorrelated features and are ranked in order of the amount of variance explained. PCA also acts as a variation reduction technique, relegating most of the random noise to the trailing components while collecting systematic structure into the leading ones. Features summarizing variability may not directly be those that are the most useful. Further analysis is facilitated through linear subspace methods involving PC rotation and strategies of projection pursuit utilizing a reduced, lower-dimensional natural basis representation that retains most of the information. These properties will be illustrated in the setting of dynamic time-series response data from fMRI experiments involving pharmacological stimulation of the dopaminergic nigro-striatal system in primates.

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine↗

Mapping major quantitative trait loci for postnatal growth in an intersubspecific backcross between C57BL/6J and Philippine wild mice by using principal component analysis.

A number of quantitative trait loci (QTLs) for postnatal growth have previously been reported in mice. As effects of the QTLs are usually small and similar to one another in magnitude, it is generally difficult to know which loci are major contributors to postnatal growth. We applied principal component analysis to a genome-wide search for QTLs affecting postnatal growth in body weight weekly recorded from 3 to 10 weeks of age in an intersubspecific backcross population of C57BL/6J inbred mice (Mus musculus domesticus) and wild mice (M. m. castaneus) captured in the Philippines, in order to discover new QTLs from a gene pool of the wild mice and uncover major loci underlying variation in postnatal growth. Principal component analysis classified phenotypic variation in body weights at different ages into two independent principal components: the first principal component (PC1) extracted information on the entire growth process and the second principal component (PC2) contrasted middle (3-6 weeks of age) with late (6-10 weeks) growth phases. Simple interval mapping and composite interval mapping revealed 10 significant QTLs with main effects on PC1 or PC2 on eight chromosomes. Of these, the six main-effect QTLs interacted epistatically with one another or three new additional QTLs on different chromosomal regions without main effects. Several of the identified QTLs with main effects and/or epistatic interaction effects appeared to be sex specific. These results suggest that the identified 13 QTLs, most of which affected the entire growth process, are very important contributors to complex genetic networks of postnatal growth.

Animals↗

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images.

Adult↗

Structure model of core proteins in photosystem I inferred from the comparison with those in photosystem II and bacteria; an application of principal component analysis to detect the similar regions between distantly related families of proteins.

A principal component analysis based on the physico-chemical properties of amino acid residues is developed to assign similar regions between distantly related families of proteins, taking account of the species diversities in respective families. The most important advantage of this analysis should be that it reflects different physico-chemical properties and thus can predict more detailed structural properties, including the transmembrane helices, than the hydropathy analysis. Its first application reconfirms the similarity between the core proteins of photosynthetic reaction center in purple bacteria and those of photosystem II, indicating that the low percentage of identical amino acid residues estimated previously between them is due to much allowance for amino acid substitutions in purple bacteria. The application of this analysis to the core proteins of photosystem I reveals that any of these proteins includes two domains, each showing high similarity to the amino acid sequences of core proteins in photosystem II and purple bacteria. A core structure model of A1 and A2 proteins folded into four layers of sheets of transmembrane helices is proposed to provide a molecular basis for the electron pathway suggested by spectroscopic experiments as well as for the interaction sites with plastocyanin, 9 kDa protein and LHC proteins.

Amino Acid Sequence↗

Principal components analysis of the Psychological Screening Inventory in a sample of substance abusers.

A principal components analysis of responses to the Psychological Screening Inventory from a sample of substance abusers was conducted. Subjects were 153 inpatients admitted to a midwestern program for chemical dependency treatment. Means for age and education were 28.0 yr. (SD = 8.1) and 11.4 (SD = 1.8), respectively. Analysis indicated that a two-factor solution best described these data. Factor 1 reflected over-all maladjustment, while Factor 2 was a measure of extroversion. Clinical utility of the inventory was discussed.

Adult↗

Principal components analysis competitive learning.

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.

Algorithms↗

Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods.

BACKGROUND: The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood. METHODS: Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA). RESULTS: The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far.

Animals↗

[Classification of pulmonary-function tests by principal component analysis].

The structure of pulmonary-function data obtained from 468 randomly selected subjects was analyzed. The subjects included patients with chronic bronchitis, bronchial asthma, chronic pulmonary emphysema, diffuse panbronchiolitis, and idiopathic interstitial pneumonitis, and normal health adults. From among the many possible indices of pulmonary function, 19 were chosen and were used as variables in principal component analysis. Six significant principal components (PCs) were extracted. The first three PCs accounted for 70% of the total information and were termed "ventilation," "volume," and "diffusion." The second three PCs accounted for 17% of the information and were termed "small airway," "lower airway," and "shape." Indices of ventilatory unevenness were not separated from indices of airway obstruction, and were included in the first PC. No other, unknown PC was detected with these pulmonary-function indices. The relationship among indices is displayed in a factor loading matrix, and pulmonary-function tests are classified statistically.

Adult↗

Monitoring of an industrial process by multivariate control charts based on principal component analysis.

The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves.

Fermentation↗