On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing

We have undertaken two-dimensional gel electrophoresis proteomic profiling on a series of cell lines with different recombinant antibody production rates. Due to the nature of gel-based experiments not all protein spots are detected across all samples in an experiment, and hence datasets are invariably incomplete. New approaches are therefore required for the analysis of such graduated datasets. We approached this problem in two ways. Firstly, we applied a missing value imputation technique to calculate missing data points. Secondly, we combined a singular value decomposition based hierarchical clustering with the expression variability test to identify protein spots whose expression correlates with increased antibody production. The results have shown that while imputation of missing data was a useful method to improve the statistical analysis of such data sets, this was of limited use in differentiating between the samples investigated, and highlighted a small number of candidate proteins for further investigation. (c) 2006 Elsevier B.V. All rights reserved.

A typology of community opportunity and vulnerability in metropolitan Australia

A multivariate model using hierarchical clustering and discriminant analysis is used to identify clusters of community opportunity and community vulnerability across Australia's mega metropolitan regions, Variables used in the model measure aspects of structural economic change, occupational change, human capital, income, unemployment, family/household disadvantage, and housing stress. A nine-cluster solution is used to categorise communities across metropolitan space. Significant between-city variations in the incidence of these clusters of opportunity and vulnerability are apparent, suggesting the emergence of marked differentiation between Australia's mega metropolitan regions in their adjustments to changing economic and social conditions. JEL classification: C49, R11, R12.

Patterns of resistance to angular leaf spot, anthracnose and common bacterial blight in common bean germplasm

Diseases and insect pests are major causes of low yields of common bean (Phaseolus vulgaris L.) in Latin America and Africa. Anthracnose, angular leaf spot and common bacterial blight are widespread foliar diseases of common bean that also infect pods and seeds. One thousand and eighty-two accessions from a common bean core collection from the primary centres of origin were investigated for reaction to these three diseases. Angular leaf spot and common bacterial blight were evaluated in the field at Santander de Quilichao, Colombia, and anthracnose was evaluated in a screenhouse in Popayan, Colombia. By using the 15-group level from a hierarchical clustering procedure, it was found that 7 groups were formed with mainly Andean common bean accessions (Andean gene pool), 7 groups with mainly Middle American accessions (Middle American gene pool), while 1 group contained mixed accessions. Consistent with the theory of co-evolution, it was generally observed that accessions from the Andean gene pool were resistant to Middle American pathogen isolates causing anthracnoxe, while the Middle American accessions were resistant to pathogen isolates from the Andes. Different combinations of resistance patterns were found, and breeders can use this information to select a specific group of accessions on the basis of their need.

Microarray expression profiling in melanoma reveals a BRAF mutation signature

We have used microarray gene expression pro. ling and machine learning to predict the presence of BRAF mutations in a panel of 61 melanoma cell lines. The BRAF gene was found to be mutated in 42 samples (69%) and intragenic mutations of the NRAS gene were detected in seven samples (11%). No cell line carried mutations of both genes. Using support vector machines, we have built a classifier that differentiates between melanoma cell lines based on BRAF mutation status. As few as 83 genes are able to discriminate between BRAF mutant and BRAF wild-type samples with clear separation observed using hierarchical clustering. Multidimensional scaling was used to visualize the relationship between a BRAF mutation signature and that of a generalized mitogen-activated protein kinase ( MAPK) activation ( either BRAF or NRAS mutation) in the context of the discriminating gene list. We observed that samples carrying NRAS mutations lie somewhere between those with or without BRAF mutations. These observations suggest that there are gene-specific mutation signals in addition to a common MAPK activation that result from the pleiotropic effects of either BRAF or NRAS on other signaling pathways, leading to measurably different transcriptional changes.

Gene expression in profiling in individual cases reveals consistent transcriptional changes in alcoholic human brain

Chronic alcohol exposure induces lasting behavioral changes, tolerance, and dependence. This results, at least partially, from neural adaptations at a cellular level. Previous genome-wide gene expression studies using pooled human brain samples showed that alcohol abuse causes widespread changes in the pattern of gene expression in the frontal and motor cortices of human brain. Because these studies used pooled samples, they could not determine variability between different individuals. In the present study, we profiled gene expression levels of 14 postmortem human brains (seven controls and seven alcoholic cases) using cDNA microarrays (46 448 clones per array). Both frontal cortex and motor cortex brain regions were studied. The list of genes differentially expressed confirms and extends previous studies of alcohol responsive genes. Genes identified as differentially expressed in two brain regions fell generally into similar functional groups, including metabolism, immune response, cell survival, cell communication, signal transduction and energy production. Importantly, hierarchical clustering of differentially expressed genes accurately distinguished between control and alcoholic cases, particularly in the frontal cortex.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

Clustering Process Analysis in a Large-Size Dropshaft and in a Hydraulic Jump

Recent efforts in the characterization of air-water flows properties have included some clustering process analysis. A cluster of bubbles is defined as a group of two or more bubbles, with a distinct separation from other bubbles before and after the cluster. The present paper compares the results of clustering processes two hydraulic structures. That is, a large-size dropshaft and a hydraulic jump in a rectangular horizontal channel. The comparison highlighted some significant differences in clustering production and structures. Both dropshaft and hydraulic jump flows are complex turbulent shear flows, and some clustering index may provide some measure of the bubble-turbulence interactions and associated energy dissipation.

Saturation mutagenesis of the beta subunit of the human granulocyte-macrophage colony-stimulating factor receptor shows clustering of constitutive mutations, activation of ERK MAP kinase and STAT pathways, and differential beta subunit tyrosine phosphoryl

The high-affinity receptors for human granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-1 (IL-3), and IL-5 are heterodimeric complexes consisting of cytokine-specific alpha subunits and a common signal-transducing beta subunit (h beta c). We have previously demonstrated the oncogenic potential of this group of receptors by identifying constitutively activating point mutations in the extracellular and transmembrane domains of h beta c. We report here a comprehensive screen of the entire h beta c molecule that has led to the identification of additional constitutive point mutations by virtue of their ability to confer factor independence on murine FDC-P1 cells. These mutations were clustered exclusively in a central region of h beta c that encompasses the extracellular membrane-proximal domain, transmembrane domain, and membrane-proximal region of the cytoplasmic domain. Interestingly, most h beta c mutants exhibited cell type-specific constitutive activity, with only two transmembrane domain mutants able to confer factor independence on both murine FDC-P1 and BAF-B03 cells. Examination of the biochemical properties of these mutants in FDC-P1 cells indicated that MAP kinase (ERK1/2), STAT, and JAK2 signaling molecules were constitutively activated. In contrast, only some of the mutant beta subunits were constitutively tyrosine phosphorylated. Taken together; these results highlight key regions involved in h beta c activation, dissociate h beta c tyrosine phosphorylation from MAP kinase and STAT activation, and suggest the involvement of distinct mechanisms by which proliferative signals can be generated by h beta c. (C) 1998 by The American Society of Hematology.

Derivatives of 1,3,5-triamino-1,3,5-trideoxy-cis-inositas versatile pentadentate ligands for protein labeling with Re-186/188. Prelabeling, biodistribution, and X-ray structural studies

The pentadentate H(3)bhci [1,3,5-trideoxy-1,3-bis((2-hydroxybenzyl)amino)-cis-inistol] and its bifunctionalized analogue H(3)bhci-glu-H [1,3,5-trideoxy-1,3-bis((2-hydroxybenzyl)amino)-5-glutaramido-cis-inositol] were synthesized, and their coordination chemistry was investigated with inactive rhenium, with no carrier added Re-188 and with carrier added Re-186. The neutral Re(V) complexes [ReO-(bhci)] and [ReO(bhci-glu-H)] are formed in good yields starting from [ReOCl3(P(C6H5)(3))(2)] or in quantitative yield directly from [(ReO4)-Re-186/188](-) in aqueous solution by reduction with Sn(II) or Sn(0). The X-ray structures of [ReO(bhci)] and [ReO(bhci-glu-H)] were elucidated revealing pentadentate side on coordination of the ligands to the Re=O core. The basic cyclohexane frame adopts a chair form in the case of [ReO(bhci)] and a twisted boat form in the case of [ReO(bhci-glu-H)]. [ReO(bhci)] crystallizes in the monoclinic space group C2/c with a = 27.425(3), b = 14.185(1), c = 19.047(2) Angstrom, and beta = 103.64(2)degrees and [ReO(bhci-glu-H)] in the monoclinic space group P2(1)/c with a = 13.056(3), b = 10.180(1), c = 22.378(5) Angstrom and beta = 98.205(9)degrees Both Re-188 complexes are stable in human serum for at least 3 days without decomposition. After injection into mice, [ReO(bhci-glu)](-) is readily excreted through the intestines, while [ReO(bhci)] is excreted by intestines, liver, and the kidneys. TLC investigations of the urine showed exclusively the complexes [ReO(bhci-glu-H)] and [ReO(bhci)], respectively, and no decomposition products. For derivatization of antibodies, the carboxylic group of [ReO(bhci-glu-H)] was activated with N-hydroxysuccinimide, which required unusually vigorous reaction conditions (heating). The anti colon cancer antibody mAb-35 [IgG and F(ab')(2) fragment] was labeled with [(ReO)-Re-186/188(bhci-glu)] to a specific activity of up to 1.5 mCi/mg (55 MBq/mg) with full retention of immunoreactivity. Labeling yields followed pseudo-first-order kinetics in antibody concentration with the ratio of rates between aminolysis and hydrolysis being about 2. Biodistributions of Re-186-labeled intact mAb-35 as well as of its F(ab')(2) fragment in tumor-bearing nude mice revealed good uptake by the tumor with only low accumulation of radioactivity in normal tissue.

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Geospatial clustering in data-rich environments: Features and issues

Geospatial clustering must be designed in such a way that it takes into account the special features of geoinformation and the peculiar nature of geographical environments in order to successfully derive geospatially interesting global concentrations and localized excesses. This paper examines families of geospaital clustering recently proposed in the data mining community and identifies several features and issues especially important to geospatial clustering in data-rich environments.