A General Framework for Longitudinal Data through Marked Point Processes

This paper reviews a general framework for the modelling of longitudinal data with random measurement times based on marked point processes and presents a worked example. We construct a quite general regression models for longitudinal data, which may in particular include censoring that only depend on the past and outside random variation, and dependencies between measurement times and measurements. The modelling also generalises statistical counting process models. We review a non-parametric Nadarya-Watson kernel estimator of the regression function, and a parametric analysis that is based on a conditional least squares (CLS) criterion. The parametric analysis presented, is a conditional version of the generalised estimation equations of LIANG and ZEGER (1986). We conclude that the usual nonparametric and parametric regression modelling can be applied to this general set-up, with some modifications. The presented framework provides an easily implemented and powerful tool for model building for repeated measurements.     

Two- and Higher Point Correlation Functions in Proteins

Abstract

A method is presented for a more efficient sampling of the configurational space of proteins as compared to conventional sampling techniques such as molecular dynamics. The method is based on the large conformational changes in proteins revealed by the “essential dynamics” analysis. A form of constrained dynamics is performed, forcing the system to move along some of the essential coordinates. This results in a broader sampling of the essential subspace than in a comparable conventional molecular dynamics simulation without constraints. The new sampling method (essential dynamics sampling) was applied to the histidine-containing phosphocarrier protein HPr. The results indicate that the essential dynamics sampling method produces physically allowed structures, as estimated by the evaluation of many geometrical properties. In addition, a study of the motions in the essential subspace reveals a diffusion-like behavior.     

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

Method

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping.

Results

I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.     

Disease Prevention versus Data Privacy: Using Landcover Maps to Inform Spatial Epidemic Models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.     

Temporal Analogues to Spatial K Functions

In this article, the spatial statistic known as the K function is adapted for temporal processes and patterns. The (optimal) K-function estimator is used in a testing procedure to determine whether behavior patterns of exposed rats versus control rats are different. Specifically, the temporal analogue to the K function is given and an approximately optimal estimator is developed. Next, a testing procedure, to determine whether a group of point patterns is generated from complete temporal randomness, is given. Finally, a testing procedure, to compare pairwise two groups of point patterns to each other, is given. The testing procedures are illustrated with rat-behavior data from both a control-control experiment as well as an exposed-control experiment, where in the latter case a difference in behavior is known to exist.     

Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza

To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual’s activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.     

Creating Eclipses: Using Scale Models to Explore How Eclipses Happen

The importance of using proportional scaled models in teaching about eclipses to elementary- and middle-level students is presented in this article. The authors illustrate how using creative models to display the basic concepts of shadows, scale, and perspective can foster a deeper understanding of how eclipses occur. Three innovative, easy-to-construct, scaled models are described as effective tools to enhance students’ understanding of eclipses. The models include space perspective on cast shadows, Earth perspective on solar eclipses, and the Moon's orbital plane around Earth.     

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.     

Cocon: From NMR Correlation Data to Molecular Constitutions

The constitutional assignment of natural products by NMR spectroscopy is usually based on 2D NMR experiments like COSY, HSQC, and HMBC. In this paper, the resulting connectivity information is used as input for the new structure generating program Cocon which both improves and dramatically accelerates the process of constitutional assignment. Cocon allows to quantify the value of connectivity information (2D NMR correlation data) for structure elucidation problems. Applying Cocon, it is systematically evaluated to which degree the NMR experiments COSY, ¹H,¹³C-HMBC and 1,1-ADEQUATE constrain the number of constitutions compatible with the data sets of two secondary metabolites from marine sponges.Electronic Supplementary Material available.     

A Common Data Set and Framework for Representing Spatial Location Information in the Internet

Many organizations are currently working on how to express and provide location information to services and applications in the Internet. Each of them basically specifies their own way. This raises a problem – the various location information formats, services and applications will not be interoperable in the Internet. Interoperability can be achieved if there is a common way of expressing location information. This paper therefore proposes a common data set and an extensible framework of expressing location information in the Internet. The design aims at bridging various existing/proposed location data representation formats, as well as meeting the requirements of existing/proposed location-aware services.     

Imaging Barriers to Diffusion by Pair Correlation Functions

Molecular diffusion and transport are fundamental processes in physical, chemical, biochemical, and biological systems. However, current approaches to measure molecular transport in cells and tissues based on perturbation methods such as fluorescence recovery after photobleaching are invasive, fluctuation correlation methods are local, and single-particle tracking requires the observation of isolated particles for relatively long periods of time. We propose to detect molecular transport by measuring the time cross-correlation of fluctuations at a pair of locations in the sample. When the points are farther apart than two times the size of the point spread function, the maximum of the correlation is proportional to the average time a molecule takes to move from a specific location to another. We demonstrate the method by simulations, using beads in solution, and by measuring the diffusion of molecules in cellular membranes. The spatial pair cross-correlation method detects barriers to diffusion and heterogeneity of diffusion because the time of the correlation maximum is delayed in the presence of diffusion barriers. This noninvasive, sensitive technique follows the same molecule over a large area, thereby producing a map of molecular flow. It does not require isolated molecules, and thus many molecules can be labeled at the same time and within the point spread function.     

宏基因组技术在开拓天然产物新资源中的应用

微生物代谢产物具有巨大的化学多样性,是多种抗生素和其它药物的重要来源。由于现有培养手段的局限性,可培养的微生物不到微生物总数的1％,使绝大部分微生物资源的开发利用受到制约。近年来．直接提取环境样品中混合微生物总基因组DNA,利用可培养的宿主细菌构建宏基因组文库,通过筛选目的克隆,寻找活性代谢产物,取得瞩目进展。对这一新领域的研究进展结合我们的研究概况进行了简要综述。     

The Autocovariance Function for Marked Point Processes: A Comparison between Two Different Approaches

Exploratory analysis of marked point patterns has previously been conducted using two disjoint techniques, namely the mark correlation function and spectral analysis. Our purpose here is to present two alternative autocovariance estimators to the mark correlation function which not only apply in both planar and lattice situations, but which in the lattice case can also be considered in terms of the inverse Fourier transform of the spectrum. Moreover, they can be applied to isotropic or anisotropic marked point patterns. Various examples are presented to show how these estimators perform when applied to data sets possessing different kinds of mark structure, and a rank test procedure is proposed to enable the construction of empirical tests of hypothesis.     

基因芯片数据分析过程:从原始数据到生物学意义

基因芯片实验要得到可靠的生物学结论,必须基于优化的实验设计和科学的数据分析。讨论了与基因芯片数据分析方法相关的实验设计方面的几个问题,简述了差异表达分析、聚类分析及功能富集分析等分析方法及其进展,并介绍了部分软件及应用。     

Recombination as a Point Process along Sequences

Histories of sequences in the coalescent model with recombination can be simulated using an algorithm that takes as input a sample of extant sequences. The algorithm traces the history of the sequences going back in time, encountering recombinations and coalescence (duplications) until the ancestral material is located on one sequence for homologous positions in the present sequences. Here an alternative algorithm is formulated not as going back in time and operating on sequences, but by moving spatially along the sequences, updating the history of the sequences as recombination points are encountered. This algorithm focuses on spatial aspects of the coalescent with recombination rather than on temporal aspects as is the case of familiar algorithms. Mathematical results related to spatial aspects of the coalescent with recombination are derived.