Given the hypothesis of a bi-modal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bi-modal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, 
    Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.

Typical morphological profiling datasets have millions of cells
    and hundreds of features per cell. When working with this data, you must
    clean the data, normalize the features to make them comparable across
    experiments, transform the features, select features based on their
    quality, and aggregate the single-cell data, if needed. 'cytominer' makes
    these steps fast and easy. Methods used in practice in the field are
    discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the
    field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.

Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) <doi:10.1016/0098-3004(93)90090-R>, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) <doi:10.1186/1471-2105-12-253>, Random Forest based on Breiman, L. (2001) <doi:10.1023/A:1010933404324>, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) <doi:10.1145/2939672.2939785>.

Supervised learning from a source distribution (with known segmentation into cell sub-populations) 
             to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly 
             estimate the different cell population proportions from a biological sample characterized with flow cytometry 
             measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from 
             different samples, thus accounting for possible mis-alignment of a given cell population across sample 
             (due to technical variability from the technology of measurements). Supervised learning technique based 
             on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a 
             mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2023) <doi:10.1214/22-AOAS1660>.