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.

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

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