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Using cluster analysis of analytic strips optical densities the lithogenic bile heterogeneity has been confirmed and different types, possessing a distinction totality of spectral and biochemical figures has been revealed. This study demonstrated tendency in process of formation of gallstone in cholelithiasis.
The application of K-means (ISODATA) cluster analysis to flow cytometric data is described. The results of analyses of flow cytometric data for mixtures of fluorescent microspheres and samples of peripheral blood mononuclear cells are presented. A method for simultaneously displaying list mode data for any number of parameters, which had previously been applied to a continuous set of parameters such as multi-angle light scattering data, is used to present the results of cluster analysis on physically unrelated parameters; this method allows rapid evaluation of the success of subpopulation identification. The factors that influence automated identification of subpopulations are examined, and methods for determining optimal values for these factors are described.
In order to identify key biological pathways that can distinguish between primary breast cancers and their lymph node metastases, we employed gene expression profiling together with gene function-based clustering analysis. We first acquired gene expression profiles of 9 matched primary tumors and the corresponding metastases that contained at least 75% of tumor cells. Then, we applied a clustering algorithm to the preprocessed data. In order to focus on the most informative genes, we ranked all the genes individually based on their abilities to separate the primary breast tumor and metastases samples. Further, we separated these genes into six functional groups according to the Stanford SOURCE database: 'cell cycle,' 'apoptosis,' 'metabolism,' 'cell adhesion and migration,' 'signal transduction,' and 'transcriptional factor and DNA binding molecules.' Unsupervised clustering analysis using all of the 2,303 genes on the microarrays was not able to separate the primary and metastases samples. Clustering analysis using the most informative genes revealed that primary tumors were more tightly clustered, whereas the metastases samples were relatively heterogeneous. The clustering analysis with the genes belonging to different functional groups showed that different functional gene sets varied in their abilities to separate primary tumors and their metastases. Marked separations were found with genes involved in metabolism, signal transduction, cell cycle, and transcriptional factor and DNA binding molecules. In contrast, apoptosis and cell adhesion and migration genes did not provide a clear separation of the two groups of samples. These results suggest that metastatic cells have different metabolism and signal transduction activities, regulated by transcriptional events, from the primary tumor cells. The results also suggest that the altered cell adhesion and migration potentials that are required for tumors to metastasize already exist in the primary tumors as a whole.
A family clustering analysis of HBsAg, anti-HBs, anti-HBc positive and total infected persons in 148 families on a farm was carried out by the methods of G statistic, binomial distribution and negative binomial distribution. The results consistently showed that there was significant clustering of HBsAg carriers in families, whereas there were not clustering was seen an overall HBV infected persons in families. The clustering of HBsAg carriers in families is due probably to the effect of some genetic factors. The results of G statistic analysis indicate that anti-HBc also have significant clustering in families.
The purpose of this study was to obtain Class I malocclusion statistical subtypes by applying cluster analysis techniques, to assess clinical and cephalometric characteristics from different clusters, and to analyse sex and age effects on grouping patterns. Four-hundred-and-sixteen Spanish patients (243 females, 173 males) with Class I osseous and dental malocclusion (4 degrees > ANB > 0 degrees) between 8 and 16 years of age, with no previous orthodontic treatment, were analysed. Cluster analysis was applied to the information provided by 20 variables both clinical and cephalometric (Ricketts' analysis) per patient. The grouped individuals were defined in a statistically significant manner by a greater lower incisive proclination, greater lower labial protrusion, less dental crowding, and less pogonion-NB distance. So, only the protrusive traits were statistically expressed in the cluster analysis. The grouping pattern in Class I malocclusion was shown in a more defined form at younger age levels and disappeared with age. The clustering pattern was very similar in Class I malocclusion males and females.
Cluster analysis was employed with the intent of describing more homogeneous subgroups of learning disabled (LD) children. The parents and teachers of 29 children placed in special education as learning disabled completed questionnaires concerning demographic information regarding the families; birth, medical, and developmental histories regarding the children were also included. Variables for the analysis were those found, by means of discriminant analysis in an earlier study, to be significantly related to LD as opposed to regular education children. The presence of developmental delays (speaking or walking later than expected), birth order, and sex of the children were influential in the definition of the clusters.
A common problem in cluster analysis is the determination of a scale-free measure of distance between individuals. This paper presents a procedure for scaling measurements using a reference individual as a standard of comparison. The procedure is particularly useful in classification of the results of laboratory procedures, where a reference standard is routinely produced. An example is the clustering of patterns that result from crossed antigen-antibody electrophoresis for determining the phenotype of the serum protein alpha 1-antitrypsin. The procedure appears to remove extraneous variability while retaining the information necessary for classification.
This study compares an expert judgment process--minimal performance levels (MPL) using the Nedelsky and Ebel procedures--for setting cutoff scores for pass/fail on licensure examinations with an empirical approach--cluster analysis. Data from all three components of the Canadian Standard Assessment in Optometry (CSAO) examinations (knowledge, clinical judgment, and clinical skills) from 243 candidates were obtained. Results indicate that for the written components of the exams employing the Nedelsky method of MPL setting, there was a mean agreement of pass/fail of 81% with the cluster analysis approach on pass/fail categorization. For the performance exams using the Ebel method, the mean agreement of pass/fail with the cluster analysis was 93%. Thus the subjective approaches to setting cutoff scores (i.e., expert judgment methods) converge with the objective method (i.e., cluster analysis) of classifying test takers in the same categories.
The dose-response curves of benzidine derivatives calculated from Ames test data were analyzed by combining the least-squares method with cluster analysis. The calculations of the electronic structures of various benzidine derivatives have at the same time been performed by using the CNDO/2 method. The cluster analysis was carried out on various parameters concerning the electronic structure, such as total electron density, frontier electron density, the energy level of HOMO (highest occupied molecular orbital), and so on. It was found that the total electron density on the nitrogen atom gives a similar pattern of classification to that for mutagenic compounds in cluster analysis. In other words, it may be inferred that the nitrogen atoms of the benzidine derivatives play an important role in the metabolic activation of the compounds. Consequently, cluster analysis with a parameter of electronic structure should be a useful initial screening procedure for various analogous mutagenic compounds as well as carcinogenic compounds.
A new approach to cluster analysis of structures based on collective superpositions rather than pairwise superpositions is presented. The method is fast and rigorous and is illustrated by application to 21 structures derived from NMR experiments. Source code, suitable for most laboratory machines, is available from the author, and a CCP4 version is in preparation.
While there have been many applications of cluster analysis in psychiatric classification research, there are no studies in which cluster analysis is used to discover the taxonomic structure implicit in the DSM-III itself. In order to do so, the symptom index in the DSM-III-R manual was summarized in a two-way matrix of disorders by symptoms and then analyzed using a hierarchical classes model and companion algorithm (HICLAS) that permits overlap among classes. A novel feature of this model is that superordinate-subordinate relationships among diagnostic and symptom classes are explicitly represented. The HICLAS analysis revealed that there are several discrete symptom classes in DSM-III-R and that many psychiatric disorders can be modeled as combinations of one or more of these classes. The disorders associated with these symptom classes tend to fit the hierarchical classes model relatively well, particularly the mood disorders and the psychotic disorders. However, disorders such as adjustment, personality, and sexual disorder fit the model poorly or not at all. The results are in line with the conjecture that the taxonomic model implicit in DSM-III-R is a hybrid of discrete symptom classes and some other structure, perhaps a dimensional one.
This study demonstrates the utility of using cluster analysis to explore the heterogeneity of dual diagnosis populations so as to facilitate planning and implementation of individualized treatment programs. A sample of 467 persons admitted to a state psychiatric hospital with DSM-III-R psychiatric diagnoses and substance abuse problems were interviewed on the Addiction Severity Index (ASI) and other measures to assess psychological, social, and community functioning. Scores on seven ASI severity ratings (medical, employment, alcohol, drug, legal, family, and psychiatric functioning) were used to group patients into seven homogeneous subgroups using cluster analysis: best functioning, unhealthy alcohol abuse, functioning alcohol abuse, drug abuse, functioning polyabuse, criminal polyabuse, and unhealthy polyabuse. Cluster reliability and validity were demonstrated using split-half tests as well as cross-sectional and longitudinal analyses. Results illustrate the extreme heterogeneity of dual diagnosis and are suggestive of how individualized treatment programs can be matched to the particular needs of patients with dual diagnoses.
Synchrotron Fourier transform infrared (FTIR) microspectroscopy can explore molecular chemical features of the microstructure of feeds. The most straightforward method of data analysis is the mapping of specific functional group intensities and frequencies by peak heights and/or areas. However, this univariate statistical method does not always accurately identify functional group locations and concentrations, because the so-called "unique" bands for the peak area mapping have more or less inference with other nonunique bands in feed and plant tissues. The objective of this study was to use a multivariate analysis method--called agglomerative hierarchical cluster analysis (CLA)--to analyze infrared spectra for chemical imaging. The results show the CLA method gave satisfactory results and was conclusive in showing that it can discriminate and classify functional group differences existing in different structure regions. This approach (CLA for chemical imaging) places synchrotron FTIR microspectroscopy at the forefront of those techniques that could potentially be used in the rapid characterization of feed microstructure.
This study has investigated a panel of immunomarkers in non-small cell lung carcinoma (NSCLC). Unsupervised hierarchical clustering analysis was used to investigate the possibility of identifying different subgroups in NSCLC based on their molecular expression profile rather than morphological features. A tissue microarray consisting of 284 cases of NSCLC was constructed. Immunohistochemistry was used to detect the presence of 18 biomarkers including synaptophysin, chromogranin, bombesin, NSE, GFI1, ASH-1, p53, p63, p21, p27, E2F-1, cyclin D1, Bcl-2, TTF-1, CEA, HER2/neu, cytokeratin 5/6, and pancytokeratin. Univariate analysis of all 18 markers for prognostic significance was performed. Immunohistochemical scoring data for NSCLC were analysed by unsupervised hierarchical clustering analysis. Kaplan-Meier survival curves were plotted for the different cluster groups of lung tumours identified by this method. Analysis of the three different World Health Organization (WHO) subtypes (adenocarcinoma, squamous cell carcinoma, large cell carcinoma) of NSCLC individually showed that different markers were significant in different subtypes. For example, p53 and p63 were significant for squamous cell carcinoma (p = 0.007 and p = 0.03, respectively), whereas cyclin D1 and HER2/neu were significant prognostic markers for adenocarcinoma (p = 0.025 and p = 0.015, respectively). These markers were not significant prognostic predictors for NSCLC as a group. Hierarchical clustering analysis of NSCLC produced four separate cluster groups, although the vast majority of cases were found in two cluster groups, one dominated by squamous cell carcinoma and the other by adenocarcinoma. The clinical outcomes of cases from the four cluster groups were not significantly different. Prognostic indicators vary between different morphological subtypes of NSCLC. Unsupervised hierarchical clustering analysis, based on an extended immunoprofile, identifies two main cluster groups corresponding to adenocarcinoma and squamous cell carcinoma; cases of large cell carcinomas are assigned to one of these two groups based on their molecular phenotype.
A new technique integrating concepts from cluster analysis and information theory was applied to the classification of Michigan hospitals. First, a number of cost-related variables that describe the hospitals and their surroundings were used in a cluster analysis to produce a hierarchy of classifications. Then for each classification, the within-group entropy was computed for each group of hospitals and averaged over the classification. Finally, this average entropy was used as an aid to judgment in deciding which of the many classifications in the hierarchy yields the most reasonable groupings of hospitals.
A method for the detection and characterization of clusters of particles observed in section with the electron microscope is presented. Cluster analysis is performed by the division method described by Berthet et al. (1976). Starting from a single cluster, profiles from each electron micrograph are successively classified in sets containing an increasing number of clusters. The decrease in the mean free distance, lambda, between profiles in the clusters, is used for terminating the subdivision procedure. The function relating the mean free distance with the number of clusters is evaluated in each subdivision set. The actual number of clusters is selected on the basis of the slope of that function, at a point where lambda has a value close to the average profile diameter. The method assumes a convex shape for the clusters; the salient feature is that it provides a physical delineation of clusters in the section. Hence, an evaluation of some characteristics of clusters in the three-dimensional sample may be obtained by using standard stereological procedures. Characterization of the volume to which the individual particles of a population are eventually restricted can as a result be performed. Practical problems in the acquisition of the data needed for cluster analysis are discussed and a system using for that purpose a Quantimet 720 image analyser in a basic configuration, connected on line with a PDP 11/10 minicomputer, is presented. Application of the method is illustrated by the analysis of lysosomes in cultured hepatoma (HTC) cells, at the end of mitosis and during the S phase. Cluster analysis shows that in cells actively synthesizing DNA they are grouped in clusters representing 5.7% of the cellular volume. Moreover, the average number of particles per cluster falls from a minimum of thirteen at mitosis to only six at the S phase.
The current study sought to explore subtypes of adolescents within a sample of female juvenile offenders. Using the Millon Adolescent Clinical Inventory with 101 female juvenile offenders, a two-step cluster analysis was performed beginning with a Ward's method hierarchical cluster analysis followed by a K-Means iterative partitioning cluster analysis. The results suggest an optimal three-cluster solution, with cluster profiles leading to the following group labels: Externalizing Problems, Depressed/Interpersonally Ambivalent, and Anxious Prosocial. Analysis along the factors of age, race, offense typology and offense chronicity were conducted to further understand the nature of found clusters. Only the effect for race was significant with the Anxious Prosocial and Depressed Intepersonally Ambivalent clusters appearing disproportionately comprised of African American girls. To establish external validity, clusters were compared across scales of the Behavioral Assessment System for Children - Self Report of Personality, and corroborative distinctions between clusters were found here.
A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based on 4D-molecular similarity measures using cluster analysis. The compounds in each cluster subset are further divided into a training set and a test set. Predictive QASAR models are constructed for each cluster subset using the corresponding training sets. These QSAR models best predict test set compounds which are assigned to the same cluster subset, based on the 4D-molecular similarity measures, from which the models are derived. The results suggest that the specific properties governing blood-brain barrier permeability may vary across chemically diverse compounds. Partitioning compounds into chemically similar classes is essential to constructing predictive blood-brain barrier penetration models embedding the corresponding key physiochemical properties of a given chemical class.