ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

The Calculus of M-estimation in R with geex

M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set of user-specified, unbiased estimating equations. Examples from the M-estimation primer by Stefanski and Boos (2002) demonstrate use of the software. The package also includes a framework for finite sample variance corrections and a website with an extensive collection of tutorials

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

A Novel Bioinformatic Approach to Understanding Addiction

Finding the genetic markers that influence complex, multigenic substance addiction phenotypes has been an area of significant medical study. Understanding complex disease traits like addiction has been hampered by the lack of functional insights into novel variants to the human genome. We hypothesized that gene location plays a role in functional genomic neighborhoods. To test whether there is a relationship between opiate, dopamine, and GABA disease and population allele frequencies, we used genes obtained from addiction literature curated by the National Center for Biotechnology Information (NCBI). These addiction and metabolism focused search terms generated opiate, dopamine, and GABA addiction results (N=587 genes). These genes were then projected onto the genome to identify cluster regions of genetic importance for substance addiction. Clusters were defined as regions of the genome with more than six genes within a 1.5Mb linear genomic window. We identified seven hotspots located on chromosomes 4, 6 (2 clusters), 10, 11, and 19. Human polymorphism data was surveyed from the 1148 individuals comprising the 11 sample populations of the HapMap Project dataset. Our analyses demonstrate that when human populations are assessed, ten candidate addiction alleles were identified. Finally assessments of public genome wide association studies show long range linkages to canonical addiction genes. This study delineates a novel method to identify novel candidate addiction variants using a systems biology approach that relies on an interdisciplinary set of data, including genomic, pathway data, and population variation. Important connections to sociological and environmental data are discussed to contextualize addiction data