Figure 6.

GLMdenoise outperforms other denoising methods. Using the DNB, we quantified the cross-validation accuracy of a variety of denoising methods on a large number of datasets. (A) Results for individual datasets. For each dataset, we summarize the performance of a method by plotting the median cross-validated R² value obtained under that method. Error bars indicate 68% confidence intervals and were obtained via bootstrapping. (B) Overall results. To summarize performance across datasets, we normalize the pattern of results from each dataset such that Standard GLM corresponds to 0 and the best-performing method corresponds to 1. We then compute the mean of this pattern across datasets (error bars indicate standard error of the mean). As an alternative performance summary, we count the number of datasets for which a given method achieves the best or nearly the best performance (specifically, the number of datasets for which the median performance of a method either is the best or provides at least 95% of the performance improvement provided by the best method). The number of datasets (out of 21 total datasets) is indicated in the legend.