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>.
Version: 2.0.6 Depends: R (≥ 3.1.0), Rcpp (≥ 0.12.11) Imports: ggplot2, graphics, igraph, mclust, methods, stats, cowplot, GoFKernel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, formatR, rmarkdown, viridis, testthat Published: 2025-07-24 DOI: 10.32614/CRAN.package.cytometree Author: Chariff Alkhassim [aut], Boris P Hejblum [cre, aut], Anthony Devaux [aut], Van Hung Huynh Tran [aut], Melany Durand [aut] Maintainer: Boris P Hejblum <boris.hejblum at u-bordeaux.fr> BugReports: https://github.com/sistm/Cytometree/issues License: LGPL-3 | file LICENSE URL: https://sistm.github.io/cytometree/, https://github.com/sistm/Cytometree/ NeedsCompilation: yes Citation: cytometree citation info Materials: README, NEWS CRAN checks: cytometree results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=cytometree to link to this page.
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