Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of Next-Generation Sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a `zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent Conditional Auto-Regressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.Comment: 25 pages; 3 tables, 6 figure

Dyadic and mediation analyses of coping with cardiovascular disease

AbstractThe purpose of this study was to investigate the relationship between attachment security and health outcomes of cardiac patients and their spouses. Dyadic coping and relationship quality were proposed to mediate this relationship. Participants were 72 couples in which one member of the couple was participating in cardiac rehabilitation. Results showed that participants with higher attachment avoidance perceived their general and mental health worse and were less likely to exercise. Patients with higher attachment avoidance perceived their partner as less supportive and this was negatively associated with their general and mental health. Spouses’ positive support and marital happiness partially mediated the relationship between their attachment anxiety and mental health. Patients with spouses with higher attachment anxiety exercised more; whereas spouses of patients with higher attachment anxiety exercised less

Idiopathic pulmonary fibrosis: Prognostic value of changes in physiology and six minute hallwalk.

Rationale and Hypothesis: Idiopathic pulmonary fibrosis is a fatal disease with a variable rate of progression. We hypothesized that changes in distance walked and quantity of desaturation during a six-minute-walk test (6MWT) would add prognostic information to changes in FVC or diffusing capacity for carbon monoxide. Methods: One hundred ninety-seven patients with idiopathic pulmonary fibrosis were evaluated. Desaturation during the 6MWT was associated with increased mortality even if a threshold of 88% was not reached. Baseline walk distance predicted subsequent walk distance but was not a reliable predictor of subsequent mortality in multivariate survival models. The predictive ability of serial changes in physiology varied when patients were stratified by the presence/absence of desaturation 88% during a baseline 6MWT. For patients with a baseline saturation 88% during a 6MWT, the strongest observed predictor of mortality was serial change in diffusing capacity for carbon monoxide. For patients with saturation 88% during their baseline walk test, serial decreases in FVC and increases in desaturation area significantly predicted subsequent mortality, whereas decreases in walk distance and in diffusing capacity for carbon monoxide displayed less consistent statistical evidence of increasing mortality in our patients. Conclusion: These data highlight the importance of stratifying patients by degree of desaturation during a 6MWT before attributing prognostic value to serial changes in other physiologic variables.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91940/1/2006 AJRCCM Idiopathic pulmonary fibrosis - Prognostic value of changes in physiology and six minute hallwalk.pd

Multimodality in galaxy clusters from SDSS DR8: substructure and velocity distribution

We search for the presence of substructure, a non-Gaussian, asymmetrical velocity distribution of galaxies, and large peculiar velocities of the main galaxies in galaxy clusters with at least 50 member galaxies, drawn from the SDSS DR8. We employ a number of 3D, 2D, and 1D tests to analyse the distribution of galaxies in clusters: 3D normal mixture modelling, the Dressler-Shectman test, the Anderson-Darling and Shapiro-Wilk tests and others. We find the peculiar velocities of the main galaxies, and use principal component analysis to characterise our results. More than 80% of the clusters in our sample have substructure according to 3D normal mixture modelling, the Dressler-Shectman (DS) test shows substructure in about 70% of the clusters. The median value of the peculiar velocities of the main galaxies in clusters is 206 km/s (41% of the rms velocity). The velocities of galaxies in more than 20% of the clusters show significant non-Gaussianity. While multidimensional normal mixture modelling is more sensitive than the DS test in resolving substructure in the sky distribution of cluster galaxies, the DS test determines better substructure expressed as tails in the velocity distribution of galaxies. Richer, larger, and more luminous clusters have larger amount of substructure and larger (compared to the rms velocity) peculiar velocities of the main galaxies. Principal component analysis of both the substructure indicators and the physical parameters of clusters shows that galaxy clusters are complicated objects, the properties of which cannot be explained with a small number of parameters or delimited by one single test. The presence of substructure, the non-Gaussian velocity distributions, as well as the large peculiar velocities of the main galaxies, shows that most of the clusters in our sample are dynamically young.Comment: 15 pages, 11 figures, 2 online tables, accepted for publication in Astronomy and Astrophysic

Development and Validation of the Behavioral Tendencies Questionnaire

At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms of approach, avoid, or equivocate (i.e., neither approach nor avoid). While there are numerous theories of personality, temperament, and character, few seem to take advantage of parsimonious taxonomy. The present study sought to implement this taxonomy by creating a questionnaire based on a categorization of behavioral temperaments/tendencies first identified in Buddhist accounts over fifteen hundred years ago. Items were developed using historical and contemporary texts of the behavioral temperaments, described as “Greedy/Faithful”, “Aversive/Discerning”, and “Deluded/Speculative”. To both maintain this categorical typology and benefit from the advantageous properties of forced-choice response format (e.g., reduction of response biases), binary pairwise preferences for items were modeled using Latent Class Analysis (LCA). One sample (n1 = 394) was used to estimate the item parameters, and the second sample (n2 = 504) was used to classify the participants using the established parameters and cross-validate the classification against multiple other measures. The cross-validated measure exhibited good nomothetic span (construct-consistent relationships with related measures) that seemed to corroborate the ideas present in the original Buddhist source documents. The final 13-block questionnaire created from the best performing items (the Behavioral Tendencies Questionnaire or BTQ) is a psychometrically valid questionnaire that is historically consistent, based in behavioral tendencies, and promises practical and clinical utility particularly in settings that teach and study meditation practices such as Mindfulness Based Stress Reduction (MBSR)

Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design

<p>Abstract</p> <p>Background</p> <p>Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.</p> <p>Results</p> <p>Residue pair scoring functions for fixed backbone protein design were derived using only backbone geometry. Unlike previous studies that used spherical harmonics to fit 2D angular distributions, Gaussian Mixture Models were used to fit the full 3D (position only) and 6D (position and orientation) distributions of residue pairs. The performance of the 1D (residue separation only), 3D, and 6D scoring functions were compared by their ability to identify correct threading solutions for a non-redundant benchmark set of protein backbone structures. The threading accuracy was found to steadily increase with increasing dimension, with the 6D scoring function achieving the highest accuracy. Furthermore, the 3D and 6D scoring functions were shown to outperform side chain-dependent empirical potentials from three other studies. Next, two computational methods that take advantage of the speed and pairwise form of these new backbone-only scoring functions were investigated. The first is a procedure that exploits available sequence data by averaging scores over threading solutions for homologs. This was evaluated by applying it to the challenging problem of identifying interacting transmembrane alpha-helices and found to further improve prediction accuracy. The second is a protein design method for determining the optimal sequence for a backbone structure by applying Belief Propagation optimization using the 6D scoring functions. The sensitivity of this method to backbone structure perturbations was compared with that of fixed-backbone all-atom modeling by determining the similarities between optimal sequences for two different backbone structures within the same protein family. The results showed that the design method using 6D scoring functions was more robust to small variations in backbone structure than the all-atom design method.</p> <p>Conclusions</p> <p>Backbone-only residue pair scoring functions that account for all six relative degrees of freedom are the most accurate and including the scores of homologs further improves the accuracy in threading applications. The 6D scoring function outperformed several side chain-dependent potentials while avoiding time-consuming and error prone side chain structure prediction. These scoring functions are particularly useful as an initial filter in protein design problems before applying all-atom modeling.</p

Tomato: a crop species amenable to improvement by cellular and molecular methods

Tomato is a crop plant with a relatively small DNA content per haploid genome and a well developed genetics. Plant regeneration from explants and protoplasts is feasable which led to the development of efficient transformation procedures. In view of the current data, the isolation of useful mutants at the cellular level probably will be of limited value in the genetic improvement of tomato. Protoplast fusion may lead to novel combinations of organelle and nuclear DNA (cybrids), whereas this technique also provides a means of introducing genetic information from alien species into tomato. Important developments have come from molecular approaches. Following the construction of an RFLP map, these RFLP markers can be used in tomato to tag quantitative traits bred in from related species. Both RFLP's and transposons are in the process of being used to clone desired genes for which no gene products are known. Cloned genes can be introduced and potentially improve specific properties of tomato especially those controlled by single genes. Recent results suggest that, in principle, phenotypic mutants can be created for cloned and characterized genes and will prove their value in further improving the cultivated tomato.

Dynamics of the NGC 4636 globular cluster system II. Improved constraints from a large sample of globular cluster velocities

We present new radial velocities for 289 globular clusters around NGC 4636, the southernmost giant elliptical galaxy of the Virgo cluster. The data were obtained with FORS2/MXU at the Very Large Telescope. Together with data analysed in an earlier study (Schuberth et al. 2006), we now have a sample of 460 globular cluster velocities out to a radius of 12 arcmin (60 kpc) available - one of the largest of its kind. This new data set also provides a much more complete angular coverage. Moreover, we present new kinematical data of the inner stellar population of NGC 4636. We perform an updated Jeans analysis, using both stellar and GC data, to better constrain the dark halo properties. We find a stellar M/L-ratio of 5.8 in the R-band, higher than expected from single stellar population synthesis. We model the dark halo by cored and cuspy analytical halo profiles and consider different anisotropies for the tracer populations. Properties of NFW halos lie well within the expected range of cosmological simulations. Cored halos give central dark matter densities, which are typical for elliptical galaxies of NGC 4636's luminosity. The surface densities of the dark matter halos are higher than those of spiral galaxies. We compare the predictions of Modified Newtonian Dynamics with the derived halo properties and find satisfactory agreement. Therefore NGC 4636 therefore falls onto the baryonic Tully-Fisher relation for spiral galaxies. The comparison with the X-ray mass profile of Johnson et al. (2009) reveals satisfactory agreement only, if the abundance gradient of hot plasma has been taken into account. This might indicate a general bias towards higher masses for X-ray based mass profiles in all systems, including galaxy clusters, with strong abundance gradients.Comment: 47 pages, 24 figures. Accepted for publication in Astronomy & Astrophysic

Neural Crest Cell Survival Is Dependent on Rho Kinase and Is Required for Development of the Mid Face in Mouse Embryos

Neural crest cells (NCC) give rise to much of the tissue that forms the vertebrate head and face, including cartilage and bone, cranial ganglia and teeth. In this study we show that conditional expression of a dominant-negative (DN) form of Rho kinase (Rock) in mouse NCC results in severe hypoplasia of the frontonasal processes and first pharyngeal arch, ultimately resulting in reduction of the maxilla and nasal bones and severe craniofacial clefting affecting the nose, palate and lip. These defects resemble frontonasal dysplasia in humans. Disruption of the actin cytoskeleton, which leads to abnormalities in cell-matrix attachment, is seen in the RockDN;Wnt1-cre mutant embryos. This leads to elevated cell death, resulting in NCC deficiency and hypoplastic NCC-derived craniofacial structures. Rock is thus essential for survival of NCC that form the craniofacial region. We propose that reduced NCC numbers in the frontonasal processes and first pharyngeal arch, resulting from exacerbated cell death, may be the common mechanism underlying frontonasal dysplasia

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone