Nunchaku: Optimally partitioning data into piece-wise linear segments

When analysing two-dimensional data sets, scientists are often interested in regions where one variable depends linearly on the other. Typically they use an ad hoc method to do so. Here we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a data set into contiguous piece-wise linear segments. Our nunchaku algorithm is freely available. Focusing on microbial growth, we use nunchaku to identify the range of optical density where the density is linearly proportional to the number of cells and to automatically find the regions of exponential growth for both Escherichia coli and Saccharomyces cerevisiae. For budding yeast, we consequently are able to infer the Monod constant for growth on fructose. Our algorithm lends itself to automation and high throughput studies, increases reproducibility, and will facilitate data analysis for a broad range of scientists.