Automated Cytometry Gating and Annotation
Given the hypothesis of a bimodal 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 bimodal normal mixture
distributions. Splitting is done according to a normalized difference of AIC
between the two families.

Chariff Alkhassim, Boris Hejblum, Anthony Devaux
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.
ggplot2, graphics, igraph, mclust, RColorBrewer, robustbase,
stats, grDevices, cowplot

Chariff Alkhassim, Boris Hejblum, Anthony Devaux, Van Hung Huynh Tran
ggplot2, graphics, igraph, mclust, robustbase, stats, cowplot

ggplot2, graphics, igraph, mclust, stats, cowplot, GoFKernel

Chariff Alkhassim, Boris P Hejblum, Anthony Devaux, Van Hung Huynh Tran
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>.
ggplot2, graphics, igraph, mclust, methods, stats, cowplot,
GoFKernel

c(person("Tim", "Becker", email = "tbecker@broadinstitute.org", role = "aut"),
person("Allen", "Goodman", email = "allen.goodman@icloud.com", role = "aut"),
person("Claire", "McQuin", email = "mcquin@broadinstitute.org", role = "aut"),
person("Mohammad", "Rohban", email = "mrohban@broadinstitute.org", role = "aut"),
person("Shantanu", "Singh", email = "shsingh@broadinstitute.org", role = c("aut", "cre"))
)
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>.
Tim Becker [aut],
Allen Goodman [aut],
Claire McQuin [aut],
Mohammad Rohban [aut],
Shantanu Singh [aut, cre]