Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.     

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.     

Temporal Distribution and Weather Correlates of Norway Rat (Rattus norvegicus) Infestations in the City of Madrid, Spain

Urban Norway rats are challenging pests, posing significant health and economic threats. Implementing ecologically based integrated rodent management (EBIRM) programmes relies primarily on the understanding of ecological relationships between rodents and their environments, with emphasis on the processes influencing rodent populations in the target ecosystem. We investigated the temporal distribution of urban Norway rat infestations in Madrid, Spain, and tested for the association of such infestations with temperature, relative humidity and precipitation by fitting a multivariate Poisson generalized linear model to a 3-year (2006–2008) daily time series of 4,689 Norway rat sightings. Norway rat infestations showed a marked seasonality, peaking in the summer. Most Norway rat sightings were reported on Mondays. Minimum temperature and relative humidity were positively associated with Norway rat infestation, whereas the association with precipitation was negative. The time series was adequately explained by the model. We identified previously unrecognized time periods that are more prone to Norway rat infestation than others and generated hypotheses about the association between weather, human outdoor activity, resource availability, rodent activity and population size. This provided local authorities engaged in preserving urban ecosystem health with basic research information to predict future rodent outbreaks and support the implementation of EBIRM programmes in urban areas.