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PubMed2004

Evaluation of toxicity dose levels by cluster analysis.

Determination of 'no observed adverse effect level' (NOAEL) is one of the prime objectives of a repeated subchronic experiment. The data collected in such experiments are huge, and it is a difficult and time-consuming task to determine NOAEL. In such situations, cluster analysis could be used as a valuable tool. In the present paper the determination of NOAEL is demonstrated using the data of a 28-day repeated oral toxicity study carried out in rats. Groups (10/sex/group) of Crj:CD rats were administered the test substance at low, middle, high and top dose levels by gastric intubation daily for 28 days. A concurrent control group was also maintained. In-life measurements included general behavior, body weight, food and water consumption. At termination various hematological and biochemical parameters were determined in the blood of individual animals in all groups. Urinalysis was also carried out at termination. Animals were sacrificed for microscopic and macroscopic findings. Analysis of data showed that 40 measurements (data 1) in the treatment groups were different from the control and 48 (data 2) were not treatment-related. Cluster analysis was carried out separately on data 1 and combined data 1 and data 2. This study revealed that for better judgment of NOAEL performing a cluster analysis on the data which alone showed a significant difference compared to control is more advisable than performing the cluster analysis on all data collected in the study. The advantage of cluster analysis is that quantitative data and qualitative data can be analyzed in annexation. In addition, cluster analysis assists in the result of the univariate analysis and displays NOAEL at once quite obviously.

Administration, Oral↗

PubMed2005

Subtypes of autism by cluster analysis based on structural MRI data.

The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4+/-5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.

Autistic Disorder↗

PubMed2005

Cluster analysis in family psychology research.

This article discusses the use of cluster analysis in family psychology research. It provides an overview of potential clustering methods, the steps involved in cluster analysis, hierarchical and nonhierarchical clustering methods, and validation and interpretation of cluster solutions. The article also reviews 5 uses of clustering in family psychology research: (a) deriving family types, (b) studying families over time, (c) as an interface between qualitative and quantitative methods, (d) as an alternative to multivariate interactions in linear models, and (e) as a data reduction technique for small samples. The article concludes with some cautions for using clustering in family psychology research.

Algorithms↗

PubMed2005

[Analysis of Chinese drug beimu and its fake species with clustering analysis and FTIR spectra].

OBJECTIVE: To identify Chinese drug Beimu from its fake species. METHOD: IR spectra was obtained by Fourier Transform Infrared Spectroscopy (FTIR) and clustering analysis was adopted for authentication. RESULT: There were significant differences between Beimu and its fake species. CONCLUSION: The established method was quick, sensitive and accurate. It could be used for the differentiation of different species of Beimu materials and their fake species.

China↗

PubMed2005

Pattern of variables describing desaturator COPD patients, as revealed by cluster analysis.

STUDY OBJECTIVES: The aims of this study were to define, by cluster analysis, a pattern of clinical variables that differentiate desaturator (D) from nondesaturator (ND) patients affected by COPD, and to identify daytime variables that are predictive of nocturnal desaturation. PATIENTS: Fifty-one random, consecutive COPD outpatients (20 women; mean [+/- SD] age, 69.6 +/- 4.0 years) with mild daytime hypoxemia (Pao(2), 60 to 70 mm Hg) were enrolled into the study. Obstructive sleep apnea syndrome patients were excluded. MEASUREMENTS AND RESULTS: Lung volumes, arterial blood gas levels, and mean pulmonary artery pressure (MPAP) were measured, and nocturnal desaturation was evaluated with nighttime polygraphy. With least squares simple linear regression, the percentage of total recording time was highly correlated with a total nocturnal recording time of arterial oxygen saturation of < 90 mm Hg (T90) and MPAP (R = 0.84; R2= 71.20%); T90 was also highly correlated with daytime Paco2 (R = 0.70; R2= 48.96%). Multiple regression showed that T90 was highly correlated with both MPAP and Paco2 (R2= 97.75%). Hierarchical cluster analysis conducted with these three variables showed that D and ND patients differed in both nocturnal and daytime variables. The mean T90 was 30 +/- 3.5% in 19.2% and 8%, respectively, of the D and ND groups. Moreover, two D subgroups differing in MPAP and two ND subgroups differing in Paco2 were identified. CONCLUSIONS: D patients may be identified by a pattern of T90, MPAP, and Paco2 values, rather than by T90 alone, with the latter two variables being predictors of nocturnal desaturation severity.

Age Distribution↗

PubMed2005

Application of cluster analysis in prevention of coronary heart disease.

INTRODUCTION: Coronary heart disease is one of the principal causes of death and morbidity in the western world, and particularly in Portugal. OBJECTIVES: This study's aim was to investigate coronary disease risk factors, differentiating lifestyles and behavioral habits which are associated with onset of the disease. METHODS: The experimental population was divided into two groups: an experimental group (n=30)--male subjects, aged 40-75 years, who suffered a first coronary event in the previous 20 days; and a control group (n=30)--male subjects, aged 40-75 years, who presented no coronary problems. Individuals with a clinical history of any other chronic disease were excluded from the sample. Data were obtained through questionnaires. Data analysis consisted of both traditional statistical analysis (Student's t test) and cluster analysis. The latter technique enables behavioral patterns that will or will not induce coronary heart disease to be distinguished. RESULTS: The Student's t test revealed significant differences (p < or = 0.05) between the experimental and control groups for the following variables: nutrition and dietary habits, smoking, stress and psychosocial factors, hereditary factors and total risk factors. The risk level of all these factors was higher in the experimental group. Cluster analysis applied to 19 variables enabled three behavioral patterns to be identified that may induce the disease, characterized by high risk indices in specific variables, and one behavioral pattern that tends to prevent development of coronary heart disease, which is characterized by low levels of risk factors. CONCLUSIONS: Coronary heart disease appears to be related to lifestyle and habits. Analysis of the three high-risk behavioral patterns enabled priority areas to be established for preventive measures against coronary heart disease. These are: stress, irritability and depression, smoking, sedentary lifestyle and nutrition (excessive consumption of salt, sugar and alcohol).

Adult↗

PubMed1982

Cluster analysis to determine headache types.

Cluster analysis was used to separate 726 headache patients into clusters of patients with similar symptoms. This was done to answer two questions: what "naturally occurring' groups of patients can be found? And how do these groups correspond to traditional headache types? When only two clusters were required, the best two clusters were tension and migraine-like. However, eight clusters could also be distinguished, and the migraine group then became very small. The clusters were tested for clinical interpretability by having 12 physicians name and prescribe treatment for the clusters. The suggested treatment was similar to what patients had actually received in about 2/3 of the cases but was rather different for the remainder. Further, the outcomes associated with different treatments appeared to vary by cluster. This suggests that the current method for classifying and treating headaches may not be optimal.

Adult↗

PubMedDate not supplied

Characterization of BOLD activation in multi-echo fMRI data using fuzzy cluster analysis and a comparison with quantitative modeling.

A combination of multiple gradient-echo imaging and exploratory data analysis (EDA), i.e. fuzzy cluster analysis (FCA), is proposed for separation and characterization of BOLD activation in single-shot spiral functional magnetic resonance imaging (fMRI) experiments at 3 T. Differentiation of functional activation using FCA is performed by clustering pixel signal changes (DeltaS) as a function of echo time (TE). Further vascular classification is supported by the localization of activation and the comparison with a single-exponential decay model. In some subjects, an additional indication for large vessels within a voxel was found as oscillation of the fMRI signal difference vs echo time (TE). Such large vessels may be separated from small vessel activation and, therefore, our proposed procedure might prove useful if a more specific functional localization is desired in fMRI. In addition to the signal change DeltaS, DeltaT(2)*/T(2)* is significantly different between activated regions. Averaged over all eight subjects DeltaT(2)* is 1.7 +/- 0.2 ms in ROIs with the highest signal change characterized as containing large vessels, whereas in ROIs corresponding to microvascular environment average DeltaT(2)* values are 0.8 +/- 0.1 ms.

Brain↗

PubMed1993

[Application of cluster analysis to chemical taxonomy of medicinal material maidong].

The cluster analysis shows that the phytochemical constituents in genera Ophiopogon and Liriope are obviously different from each other and even different species of the same genus are not quite similar. Being consistent with the result of the traditional morphological classification, the cluster analysis may provide a chemical evidence for the phytochemical taxonomy and identification of medicinal material Maidong.

Chromatography, Thin Layer↗

PubMed1992

Cluster analysis in diagnosis.

The purpose of this paper is to survey the usefulness of cluster analysis in the special case of diagnoses. This complex topic is restricted, however, to the application on laboratory characteristics, separately or in connection with clinical data. The article is subdivided into three parts: (a) the fields of a possible use of cluster analysis, detection of diseases or subgroups of diseases, and data reduction by detection of structures; (b) a brief mathematical description of hierarchical and partitioning classification techniques (as a crucial point, the problems associated with these methods are discussed); (c) a critical review of 24 publications of the past 10 years concerning cluster analysis and diagnoses.

Clinical Laboratory Techniques↗

PubMed2004

Subgroups of physically abusive parents based on cluster analysis of parenting behavior and affect.

Cluster analysis of observed parenting and self-reported discipline was used to categorize 83 abusive parents into subgroups. A 2-cluster solution received support for validity. Cluster 1 parents were relatively warm, positive, sensitive, and engaged during interactions with their children, whereas Cluster 2 parents were relatively negative, disengaged or intrusive, and insensitive. Further, clusters differed in emotional health, parenting stress, perceptions of children, and problem solving. Children of parents in the 2 clusters differed on several indexes of social adjustment. Cluster 1 parents were similar to nonabusive parents (n = 66) on parenting and related constructs, but Cluster 2 parents differed from nonabusive parents on all clustering variables and many validation variables. Results highlight clinically relevant diversity in parenting practices and functioning among abusive parents.

Adult↗

PubMed1998

Cluster analysis of consensus water sites in thrombin and trypsin shows conservation between serine proteases and contributions to ligand specificity.

Cluster analysis is presented as a technique for analyzing the conservation and chemistry of water sites from independent protein structures, and applied to thrombin, trypsin, and bovine pancreatic trypsin inhibitor (BPTI) to locate shared water sites, as well as those contributing to specificity. When several protein structures are superimposed, complete linkage cluster analysis provides an objective technique for resolving the continuum of overlaps between water sites into a set of maximally dense microclusters of overlapping water molecules, and also avoids reliance on any one structure as a reference. Water sites were clustered for ten superimposed thrombin structures, three trypsin structures, and four BPTI structures. For thrombin, 19% of the 708 microclusters, representing unique water sites, contained water molecules from at least half of the structures, and 4% contained waters from all 10. For trypsin, 77% of the 106 microclusters contained water sites from at least half of the structures, and 57% contained waters from all three. Water site conservation correlated with several environmental features: highly conserved microclusters generally had more protein atom neighbors, were in a more hydrophilic environment, made more hydrogen bonds to the protein, and were less mobile. There were significant overlaps between thrombin and trypsin conserved water sites, which did not localize to their similar active sites, but were concentrated in buried regions including the solvent channel surrounding the Na+ site in thrombin, which is associated with ligand selectivity. Cluster analysis also identified water sites conserved in thrombin but not trypsin, and vice versa, providing a list of water sites that may contribute to ligand discrimination. Thus, in addition to facilitating the analysis of water sites from multiple structures, cluster analysis provides a useful tool for distinguishing between conserved features within a protein family and those conferring specificity.

Animals↗

PubMed2006

Cluster analysis as a method for determining size ranges for spinal implants: disc lumbar replacement prosthesis dimensions from magnetic resonance images.

STUDY DESIGN: Statistical analysis of clinical radiologic data. OBJECTIVE: To develop an objective method for finding the number of sizes for a lumbar disc replacement. SUMMARY OF BACKGROUND DATA: Cluster analysis is a well-established technique for sorting observations into clusters so that the "similarity level" is maximal if they belong to the same cluster and minimal otherwise. METHODS: Magnetic resonance scans from 69 patients, with no abnormal discs, yielded 206 sagittal and transverse images of 206 discs (levels L3-L4-L5-S1). Anteroposterior and lateral dimensions were measured from vertebral margins on transverse images; disc heights were measured from sagittal images. Hierarchical cluster analysis was performed to determine the number of clusters followed by nonhierarchical (K-means) cluster analysis. Discriminant analysis was used to determine how well the clusters could be used to classify an observation. RESULTS: The most successful method of clustering the data involved the following parameters: anteroposterior dimension; lateral dimension (both were the mean of results from the superior and inferior margins of a vertebral body, measured on transverse images); and maximum disc height (from a midsagittal image). These were grouped into 7 clusters so that a discriminant analysis was capable of correctly classifying 97.1% of the observations. The mean and standard deviations for the parameter values in each cluster were determined. CONCLUSIONS: Cluster analysis has been successfully used to find the dimensions of the minimum number of prosthesis sizes required to replace L3-L4 to L5-S1 discs; the range of sizes would enable them to be used at higher lumbar levels in some patients.

Adult↗

PubMed2006

Cluster analysis of immunohistochemical profiles in synovial sarcoma, malignant peripheral nerve sheath tumor, and Ewing sarcoma.

As a result of overlapping morphologic and immunohistochemical features, it can be difficult to distinguish synovial sarcoma, malignant peripheral nerve sheath tumor, and Ewing sarcoma/primitive neuroectodermal tumor in core biopsies. To analyze and compare immunohistochemical profiles, we stained tissue microarrays of 23 synovial sarcomas, 23 malignant peripheral nerve sheath tumors, and 27 Ewing sarcomas with 22 antibodies potentially useful in the differential diagnosis, and analyzed the data with cluster analysis. Stain intensity was scored as none, weak, or strong. For CD99, tumors with membranous accentuation were independently categorized. Cluster analysis sorted five groups, with like tumors clustering together. Synovial sarcoma clustered into two groups: one cytokeratin and EMA positive (n = 11), the other mostly cytokeratin negative, EMA positive, bcl-2 positive and mostly CD56 positive (n = 9). Malignant peripheral nerve sheath tumor clustered into two groups: one S100 positive, with nestin and NGFR positivity in most (n = 10), the other mostly S100 negative, and variably but mostly weakly positive for nestin and NGFR (n = 11). Ewing sarcomas clustered into a single group driven by membranous CD99 staining. Thirteen cases failed to cluster (outliers), while three Ewing sarcomas clustered into groups of other tumor types. Paired antibodies for each tumor type determined by visual assessment of cluster analysis data and statistical calculations of specificity, sensitivity, and predictive values showed that EMA/CK7 for synovial sarcoma, nestin/S100 for malignant peripheral nerve sheath tumor, and membranous CD99/Fli-1 for Ewing sarcoma yielded high specificity and positive predictive values. Cluster analysis also highlighted aberrant staining reactions and diagnostic pitfalls in these tumors. Hierarchical cluster analysis is an effective method for analyzing high-volume immunohistochemical data.

12E7 Antigen↗

PubMed2026

Whole exome sequencing and cluster analysis reveal that EPB41L4A mutation may trigger tooth agenesis.

OBJECTIVE: To detect and analyze the correlation between commonly mutated genes and known genes associated with tooth agenesis in patients with non-syndromic tooth agenesis. The aim is to explore new genes that may be associated with tooth agenesis, to provide a genetic reference for its prevention as well as for the clinical diagnosis and treatment of tooth agenesis. METHODS: Genomic DNA was extracted from the peripheral blood of 18 congenitally edentulous subjects, and related gene mutations were identified by whole-exome sequencing. The genes related to maxillofacial development and the known pathogenic gene sequences of congenital tooth agenesis were selected for local alignment analysis of pairwise sequences, and the metric relationship of related sequences was determined. Hierarchical and fuzzy clustering methods were used for cluster analysis. RESULTS: Hierarchical clustering and fuzzy clusterings yielded consistent results. The EPB41L4A gene clustered with a large number of well-known and well-defined genes associated with tooth agenesis. From the perspective of cluster analysis, it can be inferred that the genes clustered together generally have similar functions. CONCLUSION: EPB41L4A, which is involved in the Wnt pathway, may be a candidate gene warranting further investigation.

Humans↗

PubMed2004

A cluster analysis model for caries risk assessment.

Cluster analysis was applied to determine, the natural grouping of individuals, among sixty 8-10-year-old children, and to identify the most significant set of markers for risk assessment. The risk clusters were obtained with initial clinical and bacteriological measurements including dmf + DMFS, active caries, mutans streptococci and lactobacilli counts in plaque or saliva on two media, and Snyder's test results. The morbidity clusters were constructed with the final clinical indexes and incidence after 18 months (dependent variables). A risk cluster was identified that included the following significant initial variables; dmf + DMFS, active caries, counts of mutans streptococci from plaque on TSY20B and lactobacilli in saliva, and Snyder's test results. This set of markers identified 86% of the children at high risk who developed high morbidity, as well as 94% of children in the low-risk cluster who developed low or no caries. The results of this investigation provide the basis to develop a system for caries risk assessment.

Child↗

PubMed1997

Data-dependent interval partition of naturally ordered individuals by complete cluster analysis in epidemiological and cardiac data processing.

Cluster analysis cannot usually examine every possible clustering. However, some external constraint may reduce the number of clusterings to a practical amount. In this paper, we consider naturally ordered individuals and calculate the number of interval partitions among them, if a minimal width for each interval is demanded. Two examples illustrate the application of the method. We first determine optimal information preserving age intervals due to the frequencies of some diseases. In the second example, we find a subinterval of the intraventricular blood pressure curve suitable to determine the time constant of heart relaxation.

Adolescent↗

PubMed2004

Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma.

Prognostically relevant cluster groups, based on gene expression profiles, have been recently identified for breast cancers, lung cancers, and lymphoma. Our aim was to determine whether hierarchical clustering analysis of multiple immunomarkers (protein expression profiles) improves prognostication in patients with invasive breast cancer. A cohort of 438 sequential cases of invasive breast cancer with median follow-up of 15.4 years was selected for tissue microarray construction. A total of 31 biomarkers were tested by immunohistochemistry on these tissue arrays. The prognostic significance of individual markers was assessed by using Kaplan-Meier survival estimates and log-rank tests. Seventeen of 31 markers showed prognostic significance in univariate analysis (P < or = 0.05) and 4 markers showed a trend toward significance (P < or = 0.2). Unsupervised hierarchical clustering analysis was done by using these 21 immunomarkers, and this resulted in identification of three cluster groups with significant differences in clinical outcome. chi2 analysis showed that expression of 11 markers significantly correlated with membership in one of the three cluster groups. Unsupervised hierarchical clustering analysis with this set of 11 markers reproduced the same three prognostically significant cluster groups identified by using the larger set of markers. These cluster groups were of prognostic significance independent of lymph node metastasis, tumor size, and tumor grade in multivariate analysis (P=0.0001). The cluster groups were as powerful a prognostic indicator as lymph node status. This work demonstrates that hierarchical clustering of immunostaining data by using multiple markers can group breast cancers into classes with clinical relevance and is superior to the use of individual prognostic markers.

Adult↗