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)
cytometree is a package which performs automatic gating and
annotation of flow-cytometry data. On top of the CRAN help
also provide a
illustrating the functionalities of
cytometree algorithm rely on the construction of a binary
tree, the nodes of which represents cellular (sub)populations. At
each node, observed cellular markers are modeled by both a family of
normal and a family of normal mixture distributions and splitting of
cells into further subpopulations is decided according to a normalized
difference of AIC between the two families.
Given the unsupervised nature of such a binary tree, some of the available markers may not be used to find the different cell populations present in a given sample. So in order to recover a complete annotation, we propose a post processing annotation procedure which allows the user to distinguish two or three expression levels per marker.
The following article explains in more details how
cytometree: a binary tree algorithm for automatic gating in cytometry analysis, Cytometry: Part A, (in press), 2018.
The easiest way to get
cytometree is to install it from
Or to get the development version from GitHub:
– Chariff Alkhassim & Boris Hejblum
plot_cytopop()function for displaying populations counts
NEWS.mdfile to track changes to the package.