Baselines are often chosen by visual inspection of their effect on selected spectra. A more objective procedure for choosing baseline correction algorithms and their parameter values for use in statistical analysis is presented. When the goal of the baseline correction is spectra with a pleasing appearance, visual inspection can be a satisfactory approach. If the spectra are to be used in a statistical analysis, objectivity and reproducibility are essential for good prediction. Variations in baselines from dataset to dataset means we have no guarantee that the best-performing algorithm from one analysis will be the best when applied to a new dataset. This paper focuses on choosing baseline correction algorithms and optimizing their parameter values based on the performance of the quality measure from the given analysis. Results presented in this paper illustrate the potential benefits of the optimization and points out some of the possible pitfalls of baseline correction.

A pan-genome is defined as the set of all unique gene families found in one or more strains of a prokaryotic species. Due to the extensive within-species diversity in the microbial world, the pan-genome is often many times larger than a single genome. Studies of pan-genomes have become popular due to the easy access to whole-genome sequence data for prokaryotes. A pan-genome study reveals species diversity and gene families that may be of special interest, e.g because of their role in bacterial survival or their ability to discriminate strains.

Through the remarkable progress in technology, it is getting easier and easier to generate vast amounts of variables from a given sample. The selection of variables is imperative for data reduction and for understanding the modeled relationship. Partial least squares (PLS) regression is among the modeling approaches that address high throughput data. A considerable list of variable selection methods has been introduced in PLS. Most of these methods have been reviewed in a recently conducted study. Motivated by this, we have therefore conducted a comparison of available methods for variable selection within PLS. The main focus of this study was to reveal patterns of dependencies between variable selection method and data properties, which can guide the choice of method in practical data analysis. To this aim, a simulation study was conducted with data sets having diverse properties like the number of variables, the number of samples, model complexity level, and information content. The results indicate that the above factors like the number of variables, number of samples, model complexity level, information content and variant of PLS methods, and their mutual higher‐order interactions all significantly define the prediction capabilities of the model and the choice of variable selection strategy.