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Results for “cluster analysis”

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PubMed1997

Rationalizing back pain: the development of a classification system through cluster analysis.

OBJECTIVE: The clinical variations in undifferentiated back pain pose problems for those attempting to develop strategies for care. The objective of this work was to test a methodology for the experimental generation of clinical subgroups of patients with such a complaint, so as to assist more structured study of its natural history and response to treatment. DESIGN: Cluster analysis of dichotomous symptomatic variables from computer-based case histories of three patient cohorts. SETTING: Chiropractic and Orthopedic outpatient clinics. PATIENTS: Three cohorts of new patients with back pain whose symptoms were recorded in a highly standardized way using an interactive computer interview system. CRITERIA ASSESSED: Twenty-four aggravating, relieving and cyclic features of the patients' back complaints assessed for degrees of association and formation of reproducible clusters. RESULTS: Two main patient categories were discerned: one with mechanical features and one that was cyclic. Most patients were assignable to a group. Groupings were largely consistent across all three cohorts and were not related to patient demographics. CONCLUSION: Reproducible and easily-recognized clinical subgroups of back pain patients are possible by cluster analysis using dichotomous case-history variables. More definitive categorization should be obtainable by refining the variable selection and repeating the analysis for additional patient cohorts. These subgroups have the potential to increase the relevance of natural history studies and clinical trials to the day-to-day management of the problem.

Ambulatory Care Facilities↗

PubMed1980

[Individual characteristics and biological rhythms. I. Morning and evening subject typing based on cluster analysis].

The Horne and Hostberg questionnaire to differentiate "morning" and "evening" types was adopted to study the morningness-eveningness in I44 medical students. Furthermore the reliability of the proposed typology was assessed by using the cluster analysis, which partially confirmed the categories of Horne and Ostberg, since the clusters were characterized by the presence of subjects belonging to different types. No difference by sex was observed by either method. By means of the cluster analysis it was possible to identify 20% of evening and 40% of morning types.

Adult↗

PubMedDate not supplied

Cluster analysis of obsessive-compulsive spectrum disorders in patients with obsessive-compulsive disorder: clinical and genetic correlates.

BACKGROUND: Comorbidity of certain obsessive-compulsive spectrum disorders (OCSDs; such as Tourette's disorder) in obsessive-compulsive disorder (OCD) may serve to define important OCD subtypes characterized by differing phenomenology and neurobiological mechanisms. Comorbidity of the putative OCSDs in OCD has, however, not often been systematically investigated. METHODS: The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition , Axis I Disorders-Patient Version as well as a Structured Clinical Interview for Putative OCSDs (SCID-OCSD) were administered to 210 adult patients with OCD (N = 210, 102 men and 108 women; mean age, 35.7 +/- 13.3). A subset of Caucasian subjects (with OCD, n = 171; control subjects, n = 168), including subjects from the genetically homogeneous Afrikaner population (with OCD, n = 77; control subjects, n = 144), was genotyped for polymorphisms in genes involved in monoamine function. Because the items of the SCID-OCSD are binary (present/absent), a cluster analysis (Ward's method) using the items of SCID-OCSD was conducted. The association of identified clusters with demographic variables (age, gender), clinical variables (age of onset, obsessive-compulsive symptom severity and dimensions, level of insight, temperament/character, treatment response), and monoaminergic genotypes was examined. RESULTS: Cluster analysis of the OCSDs in our sample of patients with OCD identified 3 separate clusters at a 1.1 linkage distance level. The 3 clusters were named as follows: (1) "reward deficiency" (including trichotillomania, Tourette's disorder, pathological gambling, and hypersexual disorder), (2) "impulsivity" (including compulsive shopping, kleptomania, eating disorders, self-injury, and intermittent explosive disorder), and (3) "somatic" (including body dysmorphic disorder and hypochondriasis). Several significant associations were found between cluster scores and other variables; for example, cluster I scores were associated with earlier age of onset of OCD and the presence of tics, cluster II scores were associated with female gender and childhood emotional abuse, and cluster III scores were associated with less insight and with somatic obsessions and compulsions. However, none of these clusters were associated with any particular genetic variant. CONCLUSION: Analysis of comorbid OCSDs in OCD suggested that these lie on a number of different dimensions. These dimensions are partially consistent with previous theoretical approaches taken toward classifying OCD spectrum disorders. The lack of genetic validation of these clusters in the present study may indicate the involvement of other, as yet untested, genes. Further genetic and cluster analyses of comorbid OCSDs in OCD may ultimately contribute to a better delineation of OCD endophenotypes.

Adolescent↗

PubMed1994

Subtypes of autism by cluster analysis.

Multidisciplinary data from 166 children with autistic spectrum disorders were subjected to cluster analysis. Cross-validation between random halves of the sample showed acceptable consistency of the clustering method. Four clinically meaningful subtypes emerged from the analysis. They did not differ in demographic characteristics but did show, on average, distinct differences in behavioral and cognitive areas. Over half of the sample fell into a subtype described as typically autistic with abnormal verbal and nonverbal communication, aloofness, impaired social skills, and sensory disturbances. Another 19% were similarly autistic but with moderate to severe mental handicap. The remaining children formed two subtypes: a high-functioning Asperger-like group who were overactive and aggressive, and a small group who were impaired in social and language skills, had restricted interests, and a family history of learning problems. This study highlights important differences among children with autism and emphasizes relationships between cognitive functioning and subtypes of the disorder.

Attention Deficit Disorder with Hyperactivity↗

PubMed2003

Predicting progeny performance in common bean (Phaseolus vulgaris L.) using molecular marker-based cluster analysis.

Recovery of superior individuals from a cross based solely on the phenotypic characteristics ofsingle-plant selections is inefficient because some traits, like yield, have low heritabilities, or because it is difficult to create the correct conditions for selection, as with disease resistance. In contrast, molecular markers are highly heritable and unaffected by environmental conditions. The objective of this study was to investigate the potential of molecular markers to identify superior lines in a breeding population by examining relationships between genetic distances (GDs) and phenotypic data for eight agronomic and architectural traits (branch angle, height, hypocotyl diameter, lodging, maturity, upper pods, pods per plant, and yield) obtained from three locations over a two-year period. From an elite common bean (Phaseolus vulgaris L.) cross, 110 recombinant inbred lines (RILs) and the two parents were screened with 116 random amplified polymorphic DNA (RAPD) markers. Pairwise GD values were calculated between each line and a selected "target" (the parent 'OAC Speedvale') using the Jaccard method and correlated to the trait data. The correlations were low and non-significant for all traits, except for branch angle (r = 0.30), maturity (r = -0.25), and pods per plant (r = 0.35). The lines were also grouped according to their cluster-based GD from the target parent using UPGMA cluster analysis. Trait data of lines within groups were combined and correlated to cluster-based GD. Correlation values were large and significant for all traits. Additionally, one-half of the top 10 yielding lines and nearly one-third of the best phenotypically ranked lines were present within the 13% of lines clustered nearest the target. A selection method using marker-based cluster analysis (MBCA) is suggested to assist phenotypic selection by directing a breeder's attention to a subsample of the population containing a high proportion of superior lines.

Cluster Analysis↗

PubMed2002

Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling.

MOTIVATION: Recent advances in DNA microarray technologies have made it possible to measure the expression levels of thousands of genes simultaneously under different conditions. The data obtained by microarray analyses are called expression profile data. One type of important information underlying the expression profile data is the 'genetic network,' that is, the regulatory network among genes. Graphical Gaussian Modeling (GGM) is a widely utilized method to infer or test relationships among a plural of variables. RESULTS: In this study, we developed a method combining the cluster analysis with GGM for the inference of the genetic network from the expression profile data. The expression profile data of 2467 Saccharomyces cerevisiae genes measured under 79 different conditions (Eisen et al., PROC: Natl Acad. Sci. USA, 95, 14683-14868, 1998) were used for this study. At first, the 2467 genes were classified into 34 clusters by a cluster analysis, as a preprocessing for GGM. Then, the expression levels of the genes in each cluster were averaged for each condition. The averaged expression profile data of 34 clusters were subjected to GGM, and a partial correlation coefficient matrix was obtained as a model of the genetic network of S. cerevisiae. The accuracy of the inferred network was examined by the agreement of our results with the cumulative results of experimental studies.

Bayes Theorem↗

PubMed2000

Cluster analysis of AP-PCR generated DNA fingerprints of Vibrio vulnificus isolates from patients fatally infected after consumption of raw oysters.

Arbitrarily-primed-polymerase chain reaction (AP-PCR) DNA fingerprints were generated for 10 Vibrio vulnificus strains isolated from patients who became infected and died between 1993 and 1996 as a result of consuming raw oysters. Analysis of the DNA fingerprints with gel imaging and cluster analysis software revealed significant genetic heterogeneity among these strains, suggesting that V. vulnificus has a high degree of variation in its genomic organization, and that multiple pathogenic strains with greatly diverse genomic arrangements, rather than a single type of infective strain or serogroup, caused these infections.

Animals↗

PubMed1996

Outbreak of multi-drug resistant Staphylococcus aureus: a cluster analysis.

BACKGROUND: Outbreaks of multi-drug resistant Staphylococcus aureus over eight years were investigated to prevent future endemic. METHODS: Isolates of multiresistant S. aureus underwent a cluster analysis combined with canonical discriminant analysis using bacteriologic biotyping and sensitivity to 21 drugs. RESULTS: Of a total of 786 strains recovered from 155 in-patients, the specialty surgical ward (SW) exhibited 470 isolates (59.8%) and the general SW 214 (27.2%). Among six clusters formed, four clusters were predominant in the general SW. An ordination diagram from the canonical discriminant analysis revealed a distribution in which clusters were localized temporally (year) and spatially (ward). A yearly shift of clusters indicated emergence of a new phenotype of multiresistant S. aureus. CONCLUSIONS: The cluster analysis of isolates of multiresistant S. aureus using biotyping and sensitivity may supplement the classical method of tracing the spreading patterns of this microbe.

Anti-Bacterial Agents↗

PubMed1988

Cluster analysis of amino acid indices for prediction of protein structure and function.

The relationship among 222 published indices representing various physicochemical and biochemical properties of amino acid residues has been investigated by hierarchical cluster analysis. The clustering result is illustrated by the minimum spanning tree, which is conveniently divided into four regions: alpha and turn propensities, beta propensity, hydrophobicity and other physicochemical properties including, among others, bulkiness of amino acid residues. In addition, several subclasses of hydrophobicity scales have been identified: preference of inside and outside, accessible surface area, surrounding hydrophobicity and other mostly experimental scales including transfer free energy, partition coefficients, HPLC parameters and polarity. Representative amino acid indices are identified in each of these groups. The collection of amino acid indices is a useful resource for empirical analyses correlating sequence information with structural and functional properties of proteins. As an example, the indices that best reproduce the amino acid mutation data matrix are searched against this collection.

Amino Acids↗

PubMed2005

Instability of hierarchical cluster analysis due to input order of the data: the PermuCLUSTER solution.

Hierarchical agglomerative cluster analysis (HACA) may yield different solutions under permutations of the input order of the data. This instability is caused by ties, either in the initial proximity matrix or arising during agglomeration. The authors recommend to repeat the analysis on a large number of random permutations of the rows and columns of the proximity matrix and select a solution with the highest goodness-of-fit. This approach was implemented in an SPSS add-in, PermuCLUSTER, which can perform all HACA methods of SPSS. Analyses of 2 data sets show that (a) results are affected by input order, (b) instability in one method co-occurs with instability in other methods, and (c) some instability effects are more dramatic because they occur at higher agglomeration levels.

Cluster Analysis↗

PubMed2004

Cluster analysis of patients with ocular surface disease, blepharitis, and dry eye.

OBJECTIVE: To develop a classification system for blepharitis and dry eye based on a classification-tree model of a large group of subjects who were given a variety of objective physiologic tests. METHODS: We evaluated 513 subjects, some healthy and some with blepharitis and dry eye,with tests for tear volume, tear flow, and tear turnover and the Schirmer test for dry eye. Meibomian gland function was evaluated by meibomian gland lipid expression for lipid volume and lipid viscosity, evaporation, and eyelid transillumination for meibomian gland drop out. We subjected these data to cluster analysis and formulated a classification tree. MAIN OUTCOME MEASURE: The outcome measure of this study was the statistically valid groups of subjects with and without ocular surface symptoms identified by their physiologic characteristics. RESULTS: Cluster analysis most successfully grouped subjects by initially dividing them into 2 groups based on the presence or absence of gland drop out and then by lipid viscosity and volume, Schirmer test results, and evaporation. The analysis created 9 categories. This division created an objective classification system that was found to have clinical relevance. Normal subjects were distributed across several groups. CONCLUSIONS: Using a classification tree, blepharitis and dry eye can be classified with objective physiologic tests into clinically relevant groups that have common characteristics. The analysis establishes the central role of meibomian gland dysfunction in blepharitis and demonstrates the diverse characteristics of the normal population.

Adult↗

PubMed2003

Preliminary results of evaluation of progress in chemotherapy for childhood leukemia patients employing Fourier-transform infrared microspectroscopy and cluster analysis.

Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, but remarkable progress in methods of chemotherapy has increased the cure rate to 80%. The leukemic cells called blasts are eliminated within 7 days of chemotherapy. Clinically, the blast count is monitored directly with the use of blood smears on the basis of specific genetic markers and immunophenotyping methods such as flow cytometry. In this article, we present preliminary results, obtained with the use of Fourier-transform infrared microspectroscopy and cluster analysis, of an approach to monitoring the progress made with chemotherapy in 1 B-cell and 2 T-cell pediatric ALL patients. Our results indicated that the biological marker derived from the spectra did not provide accurate prediction of the progress made with chemotherapy. However, cluster analysis of FTIR-MSP spectra provided good classification of the samples with and without blasts, which correlate satisfactorily with clinical data. Extensive studies are required to substantiate our findings statistically which may have potential application of FTIM in the diagnosis and follow-up of various types of malignancies.

Algorithms↗

PubMed1997

Multivariate cluster analysis of dynamic iodine-123 iodobenzamide SPET dopamine D2 receptor images in schizophrenia.

This paper describes the application of a multivariate statistical technique to investigate striatal dopamine D2 receptor concentrations measured by iodine-123 iodobenzamide (123I-IBZM) single-photon emission tomography (SPET). This technique enables the automatic segmentation of dynamic nuclear medicine images based on the underlying time-activity curves present in the data. Once the time-activity curves have been extracted, each pixel can be mapped back on to the underlying distribution, considerably reducing image noise. Cluster analysis has been verified using computer simulations and phantom studies. The technique has been applied to SPET images of dopamine D2 receptors in a total of 20 healthy and 20 schizophrenic volunteers (22 male, 18 female), using the ligand 123I-IBZM. Following automatic image segmentation, the concentration of striatal dopamine D2 receptors shows a significant left-sided asymmetry in male schizophrenics compared with male controls. The mean left-minus-right laterality index for controls is -1.52 (95% CI -3.72-0.66) and for patients 4.04 (95% CI 1.07-7.01). Analysis of variance shows a case-by-sex-by-side interaction, with F=10.01, P=0. 005. We can now demonstrate that the previously observed male sex-specific D2 receptor asymmetry in schizophrenia, which had failed to attain statistical significance, is valid. Cluster analysis of dynamic nuclear medicine studies provides a powerful tool for automatic segmentation and noise reduction of the images, removing much of the subjectivity inherent in region-of-interest analysis. The observed striatal D2 asymmetry could reflect long hypothesized disruptions in dopamine-rich cortico-striatal-limbic circuits in schizophrenic males.

Benzamides↗

PubMed2002

Comparison of multiparameter flow cytometry with cluster analysis and immunohistochemistry for the detection of CD10 in diffuse large B-Cell lymphomas.

CD10 is a critical antigen for the distinction of follicle-center lymphoma from other B-cell lymphomas composed of small cells in fine-needle aspiration specimens, tissue core biopsies, and bone marrow. In addition, CD10 is expressed in a subset of diffuse large B-cell lymphomas (DLBCLs), where it may be an adverse prognostic indicator. We have previously demonstrated that CD10 expression detected by multiparameter flow cytometry (FC) with cluster analysis is highly sensitive and specific for follicle-center lymphoma in the differential diagnosis of small B-cell lymphomas. In this study, we assessed the utility of paraffin section immunohistochemistry (IHC) for CD10 compared with FC in a cohort of 50 DLBCLs. IHC for CD10 was technically successful in 47 of the 50 (94%) DLBCLs; 3 failed based on lack of internal CD10 reactivity. CD10 was expressed by FC in 20 of 47 DLBCLs (43%); CD10 was positive by IHC in 15 of these (75%). All 27 cases that were CD10(-) by FC were negative by IHC. The level of CD10 expression by FC in the 5 FC(+)/IHC(-) cases ranged from relatively dim to bright. Our results indicate 75% sensitivity and 100% specificity of CD10 expression by IHC compared with multiparameter FC with cluster analysis and a 6% technical failure rate.

Cluster Analysis↗

PubMed2002

Cluster analysis of maternal characteristics and perceptions of child behavior problems in a behavioral pediatrics practice.

Mothers bringing their children to a behavioral pediatrics clinic vary considerably in terms of concerns about their children, their own emotional status, and their sense of familial and social support. Knowledge of these factors may enhance differential diagnosis and advise treatment decisions. Mothers of 90 children ages 6-12 years completed the Child Behavior Checklist (CBCL), Mental Health Inventory (MHI), Dyadic Adjustment Scale (DAS), and Health Concerns Questionnaire before their initial appointment. Cluster analysis revealed four groups of mothers that varied in their apparent motivation for seeking assistance. These groups included advice-seeking mothers, mothers that had concerns about the medical well-being of their children, mothers that were overwhelmed by their current circumstances, and mothers whose concerns about their dyadic relationships may have been displaced onto their children. The study findings support the use of cluster analysis in clinical research. Future research could focus on the specific intervention needs of these different types of families.

Child↗

PubMed1996

The classification of bulimic eating disorders: a community-based cluster analysis study.

There is controversy over how best to classify eating disorders in which there is recurrent binge eating. Many patients with recurrent binge eating do not meet diagnostic criteria for other of the two established eating disorders, anorexia nervosa or bulimia nervosa. The present study was designed to derive an empirically based, and clinically meaningful, diagnostic scheme by identifying subgroups from among those with recurrent binge eating, testing the validity of these subgroups and comparing their predictive validity with that of the DSM-IV scheme. A general population sample of 250 young women with recurrent binge eating was recruited using a two-stage design. Four subgroups among the sample were identified using a Ward's cluster analysis. The first subgroup had either objective or subjective bulimic episodes and vomiting or laxative misuse; the second had objective bulimic episodes and low levels of vomiting or laxative misuse; the third had subjective bulimic episodes and low levels of vomiting or laxative misuse; and the fourth was heterogeneous in character. This cluster solution was robust to replication. It had good descriptive and predictive validity and partial construct validity. The results support the concept of bulimia nervosa and its division into purging and non-purging subtypes. They also suggest a possible new binge eating syndrome. Binge eating disorder, listed as an example of Eating Disorder Not Otherwise Specified within DSM-IV, did not emerge from the cluster analysis.

Adolescent↗

PubMed1995

Fuzzy cluster analysis of simple physicochemical properties of amino acids for recognizing secondary structure in proteins.

Fuzzy cluster analysis has been applied to the 20 amino acids by using 65 physicochemical properties as a basis for classification. The clustering products, the fuzzy sets (i.e., classical sets with associated membership functions), have provided a new measure of amino acid similarities for use in protein folding studies. This work demonstrates that fuzzy sets of simple molecular attributes, when assigned to amino acid residues in a protein's sequence, can predict the secondary structure of the sequence with reasonable accuracy. An approach is presented for discriminating standard folding states, using near-optimum information splitting in half-overlapping segments of the sequence of assigned membership functions. The method is applied to a nonredundant set of 252 proteins and yields approximately 73% matching for correctly predicted and correctly rejected residues with approximately 60% overall success rate for the correctly recognized ones in three folding states: alpha-helix, beta-strand, and coil. The most useful attributes for discriminating these states appear to be related to size, polarity, and thermodynamic factors. Van der Waals volume, apparent average thickness of surrounding molecular free volume, and a measure of dimensionless surface electron density can explain approximately 95% of prediction results. hydrogen bonding and hydrophobicity induces do not yet enable clear clustering and prediction.

Algorithms↗

PubMedDate not supplied

A cluster analysis to investigating nurses' knowledge, attitudes, and skills regarding the clinical management system.

Nurses' knowledge, attitudes, and skills regarding the Clinical Management System are explored by identifying profiles of nurses working in Hong Kong. A total of 282 nurses from four hospitals completed a self-reported questionnaire during the period from December 2004 to May 2005. Two-step cluster analysis yielded two clusters. The first cluster (n = 159, 56.4%) was labeled "negative attitudes, less skillful, and average knowledge" group. The second cluster (n = 123, 43.6%) was labeled "positive attitudes, good knowledge, but less skillful." There was a positive correlation in cluster 1 for nurses' knowledge and attitudes (rs = 0.28) and in cluster 2 for nurses' skills and attitudes (rs = 0.25) toward computerization. The study showed that senior and more highly educated nurses generally held more positive attitudes to computerization, whereas the attitudes among younger and less well educated nurses generally were more negative. Such findings should be used to formulate strategies to encourage nurses to resolve actual problems following computer training and to increase the depth and breadth of nurses' computer knowledge and skills and improve their attitudes toward computerization.

Cluster Analysis↗