Lambda the Ultimate

kinme says "From day one, KNIME has been developed using rigorous software engineering practices" which i certainly guess is a dig at R.

overall, just curious to think about how much visual programming can/not open up the field of statistics.

No visual interface will be flexible enough to replace the command line for those of us that know programming/scripting. On the other hand, no matter how powerful a command line interface is, for those that don't know programming, its very unlikely they will learn and use it.

Red-R was initiated while I was working on a graduate degree in computational biology and needed a way of communicating my work with others that had little or no computational background.

Red-R was really created to bridge the gap between these two groups. Do your work in R. Bring the R session into Red-R and create pipeline around your work that will allow others to interact with the data and results without requiring knowledge of R programming.

Large dataset analysis has become common place in biology. In a typical project a biologist will perform an experiment that results in millions of data points. A bioinformatist will take the data preprocess/normalize the data and do some data mining to find patterns of interest. At this point the real problem is what's interesting. Usually the biologist has a much better understanding of the system and so really they should be looking at the data to make conclusions.

In Red-R the bioinformatist can preform some preprocessing analysis (either in R or Red-R) connect a few widgets to these data to sort/filter and finally visual the results (tables/graphs). Now a biologist can start interacting with Red-R to change the sorting/filtering or add different visualizations. If the preprocessing was done in Red-R the biologist has all the parameters in a GUI and can alter these to really start exploring the data and analysis to see how the results change. In my experience this ability to interact led to better data interpretation and conclusions.

We have used this tool for mining microarray data. This involved data normalization/scaling, building a regression model, identifying genes that behave differently under experimental conditions and then looking for enrichment of specific biological pathways.

We are hoping that as some of these projects are published, the whole analysis will be shared as a Red-R file for better reproducibility and readability.