CytofTree {cytometree} | R Documentation |

Binary tree algorithm for mass cytometry data analysis.

```
CytofTree(
M,
minleaf = 1,
t = 0.1,
verbose = TRUE,
force_first_markers = NULL,
transformation = c("asinh", "biexp", "log10", "none"),
num_col = 1:ncol(M)
)
```

`M` |
A matrix of size n x p containing mass cytometry measures of n cells on p markers. |

`minleaf` |
An integer indicating the minimum number of cells
per population. Default is |

`t` |
A real positive-or-null number used for comparison with the normalized AIC computed at each node of the tree. A higher value limits the height of the tree. |

`verbose` |
A logical controlling if a text progress bar is displayed during the execution of the algorithm. By default is TRUE. |

`force_first_markers` |
a vector of index to split the data on first.
This argument is used in the semi-supervised setting, forcing the algorithm to consider
those markers first, in the order they appear in this |

`transformation` |
A string indicating the transformation used among |

`num_col` |
An integer vector of index indicating the columns to be transform.
Default is |

First of all, data can be transformed using different transformations. The algorithm is based on the construction of 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.

An object of class 'cytomeTree' providing a partitioning of the set of n cells.

`annotation`

A`data.frame`

containing the annotation of each cell population underlying the tree pattern.`labels`

The partitioning of the set of n cells.`M`

The transformed matrix of mass cytometry.`mark_tree`

A two level list containing markers used for node splitting.`transformation`

Transformation used`num_col`

Indexes of columns transformed

Anthony Devaux, Boris Hejblum

```
data(IMdata)
# dimension of data
dim(IMdata)
# given the size of the dataset, the code below can take several minutes to run
if(interactive()){
# Don't transform Time et Cell_length column
num_col <- 3:ncol(IMdata)
# Build Cytoftree binary tree
tree <- CytofTree(M = IMdata, minleaf = 1, t = 0.1, transformation = "asinh", num_col = num_col)
# Annotation
annot <- Annotation(tree, plot = FALSE, K2markers = colnames(IMdata))
# Provide subpopulations
annot$combinations
}
```

[Package *cytometree* version 2.0.2 Index]