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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>.

copied from cf-post-staging / r-cytometree
Type Size Name Uploaded Downloads Labels
conda 2.5 MB | osx-64/r-cytometree-2.0.6-r44h813e631_0.conda  15 days and 17 hours ago 21 main
conda 2.5 MB | win-64/r-cytometree-2.0.6-r43hd8a2815_0.conda  15 days and 17 hours ago 25 main
conda 2.5 MB | win-64/r-cytometree-2.0.6-r44hd8a2815_0.conda  15 days and 17 hours ago 24 main
conda 2.5 MB | osx-64/r-cytometree-2.0.6-r43h813e631_0.conda  15 days and 17 hours ago 15 main
conda 2.5 MB | linux-64/r-cytometree-2.0.6-r44h3697838_0.conda  15 days and 17 hours ago 64 main
conda 2.5 MB | linux-64/r-cytometree-2.0.6-r43h3697838_0.conda  15 days and 17 hours ago 63 main

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