SEARCH · PubMed Health

Results for “cluster analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37Linked to original sources

PubMed1984

Fully automated and fast image analysis of autoradiographs with a TAS-Leitz. Determination of size, Feulgen fluorescence and grain counts of individual nuclei and their evaluation by a simplified cluster analysis.

A fully automatic analysis system based on television image analysis was developed to measure simultaneously three parameters in individual nuclei of microscopic autoradiographs prepared from mouse jejunal crypt cell squashes and ascites tumor cell smears: size, Feulgen fluorescence and reflection from silver grains. A dark light camera with an image intensified silicon tube (RCA-ISIT), an automatic scanning stage and an autofocus device were fitted to a Leitz-TAS microscope. The camera permitted localization of Feulgen stained nuclei and measurement of area and light intensity by means of incident of light fluorescence in the red. After automatic changes of the Opak-illuminator silver grains were determined by means of polarized incident light reflected from the grains in the blue. A 25 X oil objective (aperture 0.75) yielded sufficient resolution for measurements. The nadir between the proportions of labeled and unlabeled nuclei was calculated from the data of one specimen on a PDP-computer using a new algorithm based on the minimal variance of the logarithm of reflected light per nucleus. Labeling indices determined by visual grain counting and by automatic analysis of the autoradiographs were well correlated (r = 0.87 to 0.92). Visual grain counts/nucleus and reflected light/nucleus correlated well when individual nuclei were compared (r = 0.92 to 0.97) or means of labeled nuclei of various specimens prepared during a 5 year period (r = 0.90 to 0.93). Quenching of nuclear Feulgen fluorescence was minimal. The optimal labeling range is 30-100 grain counts/nucleus. The time interval between measurements of two specimens was 25 min for a squash of approximately 350 crypt cells within a 3 mm X 3 mm field, and 20 min for a meandering scan with 1,000 ascites tumor cells.

Animals↗

PubMed2006

Spatial clustering analysis in neuroanatomy: applications of different approaches to motor nerve fiber distribution.

Spatial organization of the nerve fibers in the peripheral nerves may be important for the studies of axonal regeneration, the degenerative nerve diseases and the construction of interfaces with peripheral nerves, such as nerve prostheses. Functional topography of motor axons related to the gastrocnemius muscle was revealed in the ventral spinal roots by retrograde tracing. Gastrocnemius muscles of adult rats were injected with the tracer Fluoro-Gold. After 3 days of survival the animals were sacrificed and their ventral roots were harvested, sectioned, and imaged on a fluorescence microscope. Maps of the traced fibers were automatically analyzed using a novel approach, local spatial clustering statistics, that tested for occurrences of clusters of motor fibers and visualized them. Thresholds indicating the presence of clustering at various scales of observation were computed based on series of Monte Carlo simulations of random spatial point patterns. Clusters were visualized by kernel interpolation. The approach was tested on simulated data and subsequently applied to the motor fiber maps in the ventral roots. Results revealed clustering of the motor fibers innervating the gastrocnemius muscle at the level of the L6 ventral spinal root. The analysis was validated using Voronoi tessellation and nearest neighbor analysis.

Animals↗

PubMed1983

Cluster analysis of the WAIS on brain-damaged patients.

In recognition of increased usage of cluster analytic techniques, the current study attempted to examine sampling bounds for tests and subjects on one cluster-analytic procedure. Specifically, the WAIS subtest scores from a heterogeneous group of subjects with CNS involvement were first, factor analyzed and then clustered by means of Ward's hierarchical procedure. This methodology had been previously applied to two different samples of aphasics and aphasia batteries. In both these aphasic studies, an underlying severity dimension of language dysfunction was found. In contrast, in the current study, salient feature groups were found. Moreover, these groups were distributed in a sequential or hierarchical model in terms of specific cognitive deficits. From these data, it is argued that two major parameters in determining the resulting cluster structures are the homogeneity/heterogeneity of the sample and test battery employed. Although such a statement is obvious, the theoretical ramifications are large, in that the cluster structure may be a function of test weighting or sample diversity, not the phenomenon of interest. However, the cluster structures found in the current study were consistent with historical models of cognitive dysfunction and also lend support to hierarchical models of sequential cognitive impairment. These findings are discussed in terms of their clinical and experimental ramifications with respect to grouping subjects in terms of WAIS profiles.

Adult↗

PubMed1998

The Wechsler Adult Intelligence Scale (WAIS), but not the Defence Mechanism Test (DMTm), separates schizophrenics and normal controls in a factorial cluster analysis.

Ten schizophrenic patients and ten healthy control subjects matched with respect to sex, age and education were tested by a psychological test battery including WAIS, WCST, FAS and a modified version of the tachistoscopic Defence Mechanism Test (DMTm). In a Q-factor analysis two factors were derived in the analysis of DMTm test scores. The distribution of cases among these factors was wholly at random. On the other hand, when analysing WAIS scores, five factors were derived and schizophrenic cases as well as control subjects were almost unequivocally clustered by different factors. It is argued that also if an unequivocal categorisation of cases had been achieved in the analysis of DMTm data, such a finding might well have been interpreted as an effect of anomalies in cerebral structures assumed to be of critical importance in the filtering of signals in the stream of visual perception. The existence of such anomalies in schizophrenics is now well established by neuroimaging as well as postmortem studies, and findings are also well in accordance with phenomenological and physiological data. The failure of DMTm to separate schizophrenic and control subjects does thus make the second and important step in a discussion on validity entirely superfluous, namely whether signs recorded really measure what they are assumed to measure, in this case defence mechanisms in a psychoanalytic sense.

Adult↗

PubMed2006

Cluster analysis for DNA methylation profiles having a detection threshold.

BACKGROUND: DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data. RESULTS: We compare performance of existing methodology (such as k-means) with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly respect the supposed biological realities of the situation. CONCLUSION: The performance of analysis methods depends upon how well the assumptions of those methods reflect the properties of the data being analyzed. Differing technologies will lead to data with differing properties, and should therefore be analyzed differently. Consequently, it is prudent to give thought to what the properties of the data are likely to be, and which analysis method might therefore be likely to best capture those properties.

Artificial Intelligence↗

PubMed2005

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PURPOSE: To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. MATERIALS AND METHODS: Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. RESULTS: The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. CONCLUSION: More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.

Algorithms↗

PubMed2026

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.

MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.

Proteomics↗

PubMed2004

Cerebral microdialysis of patients with severe traumatic brain injury exhibits highly individualistic patterns as visualized by cluster analysis with self-organizing maps.

OBJECTIVE: To analyze patterns of cerebral microdialysis in patients with traumatic brain injury and, with a neural network methodology, investigate pattern relationships to intracranial pressure and cerebral perfusion pressure. DESIGN: Retrospective. SETTING: University hospital, adult neurosurgical intensive care unit. PATIENTS: Twenty-six patients with severe traumatic brain injury. All consecutive traumatic brain injured patients (Glasgow Coma Scale < or =8) with microdialysis monitoring, analyzing glutamate, lactate, pyruvate, and glucose in both penumbral and nonpenumbral tissue. INTERVENTIONS: None; patients received the unit's standard neurointensive care procedure. MEASUREMENTS AND MAIN RESULTS: We used 2084 hrs of complete microdialysis data sets (eight markers) to train Kohonen self-organizing maps. The self-organizing map algorithm is a data-clustering method that reduces high-dimensional information to a two-dimensional representation on a grid (map), retaining local relationships in the data. Maps were colored (overlaid) for intracranial pressure, cerebral perfusion pressure, and outcome, to explore relationships with underlying microdialysis patterns. The maps exhibited a striking clustering of patients, with unique microdialysis patterns that were recognizable throughout the analysis period. This also held true for most microdialysis patterns characteristic of ischemia. These patients with ischemic patterns can have good outcomes, suggesting a disparity between microdialysis values and severity of traumatic brain injury. CONCLUSION: Using an artificial neural network-like clustering technique, Kohonen self-organizing maps, we have shown that cerebral microdialysis, in traumatic brain injury, exhibits strikingly individualistic patterns that are identifiable throughout the analysis period. Because patients form their own clusters, microdialysis patterns, during periods of increased intracranial pressure or decreased cerebral perfusion pressure, will be found within these clusters. Consequently, no common pattern of microdialysis can be seen among patients within the range of our data. We suggest that these individualistic patterns reflect not only metabolic states of traumatic brain injury but also local gradients seen with small volume sampling. Future investigation should focus on relating these patterns, and movement within and from clusters, to metabolic states of the complex pathophysiology of traumatic brain injury.

Adolescent↗

PubMed2003

Shrinkage-based similarity metric for cluster analysis of microarray data.

The current standard correlation coefficient used in the analysis of microarray data was introduced by M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein [(1998) Proc. Natl. Acad. Sci. USA 95, 14863-14868]. Its formulation is rather arbitrary. We give a mathematically rigorous correlation coefficient of two data vectors based on James-Stein shrinkage estimators. We use the assumptions described by Eisen et al., also using the fact that the data can be treated as transformed into normal distributions. While Eisen et al. use zero as an estimator for the expression vector mean mu, we start with the assumption that for each gene, mu is itself a zero-mean normal random variable [with a priori distribution N(0,tau 2)], and use Bayesian analysis to obtain a posteriori distribution of mu in terms of the data. The shrunk estimator for mu differs from the mean of the data vectors and ultimately leads to a statistically robust estimator for correlation coefficients. To evaluate the effectiveness of shrinkage, we conducted in silico experiments and also compared similarity metrics on a biological example by using the data set from Eisen et al. For the latter, we classified genes involved in the regulation of yeast cell-cycle functions by computing clusters based on various definitions of correlation coefficients and contrasting them against clusters based on the activators known in the literature. The estimated false positives and false negatives from this study indicate that using the shrinkage metric improves the accuracy of the analysis.

Algorithms↗

PubMed1975

A cluster analysis of some bacteria in the water column of Green Lake, Washington.

Mass inoculation and computer clustering techniques were used in an abbreviated procedure to group similar bacteria isolated from four depths in the water column of Green Lake, Washington. Four groups of bacteria were differentiable and were catagorized as orange-yellow Flavobacterium-Cytophaga, yellow Flavobacterium-Cytophaga, Vibrio-Aeromonas-, and Pseudomonas-like. Some of the groups were most prevalent in certain sample depths in the water column.

Aeromonas↗

PubMed1996

Subtypes of incarcerated delinquents constructed via cluster analysis.

The MMPI and Jesness Inventory were used to construct groupings of juvenile delinquents homogeneous with respect to personality characteristics. Subjects were 191 incarcerated juvenile delinquents, aged 12 to 19. All participants had been adjudicated on one or more serious offenses. Five subtypes of delinquents were identified. They included an alienated, unsocialized group, an insecure-anxious group, a group reporting limited difficulties with peers but alienated from adults, a group with no apparent emotional disturbance, and a group with multiple elevations. Significant differences between clusters with regard to self-reported explosiveness, suicidality, gang activities, substance abuse, history of sexual abuse, and delusions and hallucinations were identified. Discriminant function analysis was then used to support the generalizability of the classification system to an independent sample. Eighty per cent of the second sample were accurately classified. The subtypes identified are compared to delinquent classification systems developed previously.

Adult↗

PubMed1987

Cluster analysis of Minnesota Multiphasic Personality Inventory (MMPI) profiles in a chronic headache population.

Minnesota Multiphasic Personality Inventory (MMPI) profiles from 123 chronic headache sufferers (48 migraine, 47 muscle contraction, 28 mixed) were cluster analyzed in order to replicate results from other pain populations and to examine differences among the clusters along headache parameters and demographic variables. Four homogeneous subgroups were obtained and a multiple discriminant analysis was successful in correctly classifying 97.56% of the headache subjects. Obtained clusters closely resembled those evidenced in previous pain research. Results indicated that diagnostic category was unrelated to cluster membership; however, frequency of headache occurrence was a differentiating factor. Implications for headache treatment and future research are discussed.

Adolescent↗

PubMed2006

Subgrouping outpatients of an environmental medicine unit using SCL-90-R and cluster analysis.

Between 1999 and 2003 all consecutive outpatients of the environmental medicine unit of the Charité hospital in Berlin were invited to participate in a study on environmentally related disorders. One hundred and sixty-nine of the patients completed a psychosocial questionnaire which comprised SCL-90-R and 14 other tests. When compared with clinical controls, SCL-90-R mean scores of the environmental patients (EP) were found to lie in between those of inpatients of a psychosomatic clinic and melanoma aftercare patients; but they were, with exception of the somatisation dimension, much closer to the latter. Application of the TwoStep Cluster component of SPSS resulted in three subgroups with high, moderate and low scores which were significantly different (p<0.001) although separation was incomplete (87% correct identification; cross-validated discriminant analysis). With all subgroups, scores for somatisation, depression and obsessive compulsion were highest and those for phobic anxiety and psychoticism were lowest. SCL-90-R scores correlated well with results of the Freiburg Personality Inventory, Whiteley Index of hypochondriasis, the short form health questionnaire (SF-36), and the Composite International Diagnostic Interview (CIDI). Hierarchical grouping (average linkage between groups), performed with involvement of SCL-90-R data from literature, resulted in a dendrogram with three distinct groups and three outliers. EP with low SCL-90-R scores were assigned to a group which comprised also general populations (USA, Germany), allergy patients, and melanoma controls. Those with moderate SCL-90-R scores were placed in a group together with chronic pain patients, and 26 'environmentally ill' subjects. The third subgroup of EP formed a cluster with our psychosomatic controls, psychosomatic patients from another study, depressed people, and patients undergoing psychotherapy. The three outliers of the dendrogram, however, were SCL-90-R profiles obtained from persons with toxic waste exposure, neurotoxic workplace exposure or with solvent-induced chronic toxic encephalopathy.

Cluster Analysis↗

PubMed2005

Statistical approach in cluster analysis of two-dimensional quasicrystals.

An analytical formula for the structure factor of Penrose tiling in the cluster approach was derived and tested. Probability distributions obtained for each Penrose position allow the number of different atoms that can decorate the cluster to be found. Calculations were performed in the average-unit-cell approach for Gummelt's cluster of 33 atoms, divided into three independent groups of atoms, and a kite cluster of 17 atoms, divided into seven independent groups.

Journal Article↗

PubMed2007

Virtual definition of neuronal tissue by cluster analysis of multi-parametric imaging (virtual-dot-com imaging).

Individual mapping of cerebral, morphological, functionally related structures using MRI was carried out using a new multi-contrast acquisition and analysis framework, called virtual-dot-com imaging. So far, conventional anatomical MRI has been able to provide gross segmentation of gray/white matter boundaries and a few sub-cortical structures. By combining a handful of imaging contrasts mechanisms (T1, T2, magnetization transfer, T2* and proton density), we were able to further segment sub-cortical tissue to its sub-nuclei arrangement, a segmentation that is difficult based on conventional, single-contrast MRI. Using an automatic four-step image and signal processing algorithm, we segmented the thalamus to at least 7 sub-nuclei with high similarity across subjects and high statistical significance within subjects (p<0.0001). The identified sub-nuclei resembled the known anatomical arrangement of the thalamus given in various atlases. Each cluster was characterized by a unique MRI contrast fingerprint. With this procedure, the weighted proportions of the different cellular compartments could be estimated, a property available to date only by histological analysis. Each sub-nucleus could be characterized in terms of normalized MRI contrast and compared to other sub-nuclei. The different weights of the contrasts (T1/T2/T2*/PD/MT, etc.) for each sub-nuclei cluster might indicate the intra-cluster morphological arrangement of the tissue that it represents. The implications of this methodology are far-ranging, from non-invasive, in vivo, individual mapping of histologically distinct brain areas to automatic identification of pathological processes.

Adult↗

PubMed2006

Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.

BACKGROUND: Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. RESULTS: We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome--the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. CONCLUSION: The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest.

Algorithms↗

PubMed2001

Cluster analysis and promoter modelling as bioinformatics tools for the identification of target genes from expression array data.

Expression arrays yield enormous amounts of data linking genes, via their cDNA sequences, to gene expression patterns. This now allows the characterisation of gene expression in normal and diseased tissues, as well as the response of tissues to the application of therapeutic reagents. Expression array data can be analysed with respect to the underlying protein sequences, which facilitates the precise determination of when and where certain groups of genes are expressed. More recent developments of clustering algorithms take additional parameters of the experimental set-up into account, focusing more directly on co-regulated set of genes. However, the information concerning transcriptional regulatory networks responsible for the observed expression patterns is not contained within the cDNA sequences used to generate the arrays. Regulation of expression is determined to a large extent by the promoter sequences of the individual genes (and/or enhancers). The complete sequence of the human genome now provides the molecular basis for the identification of many regulatory regions. Promoter sequences for specific cDNAs can be obtained reliably from genomic sequences by exon mapping. In the many cases in which cDNAs are 5'-incomplete, high quality promoter prediction tools can be used to locate promoters directly in the genomic sequence. Once sufficient numbers of promoter sequences have been obtained, a comparative promoter analysis of the co-regulated genes and groups of genes can be applied in order to generate models describing the higher order levels of transcription factor binding site organisation within these promoter regions. Such modules represent the molecular mechanisms through which regulatory networks influence gene expression, and candidates can be determined solely by bioinformatics. This approach also provides a powerful alternative for elucidating the functional features of genes with no detectable sequence similarity, by linking them to other genes on the basis of their common promoter structures.

Algorithms↗

PubMed1993

The conformations of cyclic (1-->2)-beta-D-glucans: application of multidimensional clustering analysis to conformational data sets obtained by Metropolis Monte Carlo calculations.

Sets containing up to 1.3 x 10(6) energetically accessible conformations of linear (1-->2)-beta-D-glucan oligosaccharides were obtained by Metropolis Monte Carlo (MMC) calculations performed with the GEGOP (GEometry of GlycOProteins) program. Quantitative analyses of the data sets (which were expressed in terms of the glycosidic dihedral angle coordinates) were obtained by two different clustering methods: (i) the three-distance hierarchical clustering method (3-DM), published by Jure Zupan, and (ii) a nonhierarchical clustering method (Population-Density Projection, PDP) which, through a segmentation analysis of two-dimensional projections of the population-density surface, establishes a partitioning of conformational space into a set of "cluster regions", followed by a clustering step where each conformation of the data set is assigned to one of these regions. Computer programs (MCLUST and PDPCLUST) were developed to perform the 3-DM and PDP analyses, respectively. The two types of analysis provided very similar sets of conformational families (clusters), which could be expressed as combinations of distinct conformations of the glycosidic torsional angles (phi, psi) centered at (50 degrees, 10 degrees) for conformation A, (40 degrees, 160 degrees) for conformation B, (55 degrees, -160 degrees) for conformation B', and (170 degrees, 10 degrees) for conformation C. The analysis provided the populations of the families, along with relative rates for transitions between families. Examination of the frequencies of the A, B, and C glycosidic bond conformations with respect to their relative positions in the sequence revealed the tendency of the (1-->2)-beta-D-glucan to adopt conformational repeating structures of the general form [AnB], where n = 3 or 6. These repeating structures combine in an energetically cooperative fashion to give low-energy cyclic conformations having, for example C5 symmetry [AAAB]5 for the eicosamer, and C3 symmetry [AAAAAAB]3 for the heneicosamer.

Carbohydrate Conformation↗