Motivation: Recent flow and mass cytometers generate datasets of dimensions 20 to 40 and a million single cells. From these, many tools facilitate the discovery of new cell populations associated with diseases or physiology. These new cell populations require the identification of new gating strategies, but gating strategies become exponentially more difficult to optimize when dimensionality increases. To facilitate this step, we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity. Results: Hypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergate's outputs are consistent with fields' knowledge and sparser than those from a competing method. Availability and implementation: Hypergate is implemented in R and available on CRAN. The source code is published at http://github.com/ebecht/hypergate under an Open Source Initiative-compliant licence. Supplementary information: Supplementary data are available at Bioinformatics online.

Powerful methods for integrated analysis of multiple biological data sets must be developed to maximize researchers' ability to interpret them and acquire meaningful knowledge. Projection onto the most interesting statistical evidence (PROMISE) is a powerful statistical procedure that has been recently developed to incorporate a biological paradigm for the relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to perform an integrated analysis of pharmacokinetic, clinical, and genome-wide genotype data that incorporates a clinically relevant biological paradigm for pharmacokinetic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE can be a very powerful statistical tool for identifying genomic features that exhibit a pattern of association with multiple endpoint variables which is concordant with a practically useful biological paradigm.