fgui: A Method for Automatically Creating Graphical User Interfaces for Command-Line R Packages

The fgui R package is designed for developers of R packages, to help rapidly, and sometimes fully automatically, create a graphical user interface for a command line R package. The interface is built upon the Tcl/Tk graphical interface included in R. The package further facilitates the developer by loading in the help files from the command line functions to provide context sensitive help to the user with no additional effort from the developer. Passing a function as the argument to the routines in the fgui package creates a graphical interface for the function, and further options are available to tweak this interface for those who want more flexibility.

Passing in Command Line Arguments and Parallel Cluster/Multicore Batching in R with batch

It is often useful to rerun a command line R script with some slight change in the parameters used to run it - a new set of parameters for a simulation, a different dataset to process, etc. The R package batch provides a means to pass in multiple command line options, including vectors of values in the usual R format, easily into R. The same script can be setup to run things in parallel via different command line arguments. The R package batch also provides a means to simplify this parallel batching by allowing one to use R and an R-like syntax for arguments to spread a script across a cluster or local multicore/multiprocessor computer, with automated syntax for several popular cluster types. Finally it provides a means to aggregate the results together of multiple processes run on a cluster

fgui: A Method for Automatically Creating Graphical User Interfaces for Command-Line R Packages

The fgui R package is designed for developers of R packages, to help rapidly, and sometimes fully automatically, create a graphical user interface for a command line R package. The interface is built upon the Tcl/Tk graphical interface included in R. The package further facilitates the developer by loading in the help files from the command line functions to provide context sensitive help to the user with no additional effort from the developer. Passing a function as the argument to the routines in the fgui package creates a graphical interface for the function, and further options are available to tweak this interface for those who want more flexibility

Running coupling and mass anomalous dimension of SU(3) gauge theory with two flavors of symmetric-representation fermions

We have measured the running coupling constant of SU(3) gauge theory coupled to Nf=2 flavors of symmetric representation fermions, using the Schrodinger functional scheme. Our lattice action is defined with hypercubic smeared links which, along with the larger lattice sizes, bring us closer to the continuum limit than in our previous study. We observe that the coupling runs more slowly than predicted by asymptotic freedom, but we are unable to observe fixed point behavior before encountering a first order transition to a strong coupling phase. This indicates that the infrared fixed point found with the thin-link action is a lattice artifact. The slow running of the gauge coupling permits an accurate determination of the mass anomalous dimension for this theory, which we observe to be small, gamma_m < 0.6, over the range of couplings we can reach. We also study the bulk and finite-temperature phase transitions in the strong coupling region.Comment: 17 pages, 16 figures. Substantial modifications to explain why the fat-link result for the beta function supersedes our thin-link result; also updated the phase diagram to reflect additional numerical work. Added references. Final versio

Parallel biocomputing

<p>Abstract</p> <p>Background</p> <p>With the advent of high throughput genomics and high-resolution imaging techniques, there is a growing necessity in biology and medicine for parallel computing, and with the low cost of computing, it is now cost-effective for even small labs or individuals to build their own personal computation cluster.</p> <p>Methods</p> <p>Here we briefly describe how to use commodity hardware to build a low-cost, high-performance compute cluster, and provide an in-depth example and sample code for parallel execution of R jobs using MOSIX, a mature extension of the Linux kernel for parallel computing. A similar process can be used with other cluster platform software.</p> <p>Results</p> <p>As a statistical genetics example, we use our cluster to run a simulated eQTL experiment. Because eQTL is computationally intensive, and is conceptually easy to parallelize, like many statistics/genetics applications, parallel execution with MOSIX gives a linear speedup in analysis time with little additional effort.</p> <p>Conclusions</p> <p>We have used MOSIX to run a wide variety of software programs in parallel with good results. The limitations and benefits of using MOSIX are discussed and compared to other platforms.</p

Comprehensive Approach to Analyzing Rare Genetic Variants

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic “step-up” approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants

Pesticide Use Changes in New York Vegetables: 1978 to 1998

Pesticide use patterns in 1978 and 1998 were compared for 15 vegetable crops grown in New York State. Insecticide use decreased in almost all vegetables over this period, with an overall decline of 65%. Total herbicide use declined 24%, while fungicide use increased 76%. Within crops, potatoes and onions received more than 60% of all pesticide use on vegetables. Large declines in pesticide use occurred in some crops and usually were associated with the substitution of low use-rate for high use-rate insecticides or herbicides. Strategies for future reductions in pesticide use are discussed