cytometree (version 2.0.2)

CytomeTree: Binary tree algorithm for cytometry data analysis.

Description

Binary tree algorithm for cytometry data analysis.

Usage

CytomeTree(M, minleaf = 1, t = 0.1, verbose = TRUE, force_first_markers = NULL)

Value

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 input matrix.

• mark_tree A two level list containing markers used for node splitting.

Arguments

M

A matrix of size n x p containing cytometry measures of n cells on p markers.

minleaf

An integer indicating the minimum number of cells per population. Default is 1.

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 force_first_markers vector, and forcing the split at every node. Default is NULL, in which case the clustering algorithm is unsupervised.

Author

Chariff Alkhassim, Boris Hejblum

Details

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.

Examples

head(DLBCL)

# number of cell event
N <- nrow(DLBCL)

# Cell events
cellevents <- DLBCL[, c("FL1", "FL2", "FL4")]


# Manual partitioning of the set N (from FlowCAP-I)
manual_labels <- DLBCL[, "label"]


# Build the binary tree
Tree <- CytomeTree(cellevents, minleaf = 1, t=.1)


# Retreive the resulting partition of the set N
Tree_Partition <- Tree$labels


# Plot node distributions
par(mfrow=c(1, 2))
plot_nodes(Tree)

# Choose a node to plot
plot_nodes(Tree,"FL4.1")

# Plot a graph of the tree
par(mfrow=c(1,1))
plot_graph(Tree,edge.arrow.size=.3, Vcex =.5, vertex.size = 30)

# Run the annotation algorithm
Annot <- Annotation(Tree,plot=FALSE)
Annot$combinations


# Compare to the annotation gotten from the tree
Tree$annotation


# Example of sought phenotypes
# Variable in which sought phenotypes can be entered in the form of matrices.
phenotypes <- list()

# Sought phenotypes:
## FL2+ FL4-.
phenotypes[[1]] <- rbind(c("FL2", 1), c("FL4", 0))

## FL2- FL4+.
phenotypes[[2]] <- rbind(c("FL2", 0), c("FL4", 1))

## FL2+ FL4+.
phenotypes[[3]] <- rbind(c("FL2", 1), c("FL4", 1))

# Retreive cell populations found using Annotation.
PhenoInfos <- RetrievePops(Annot, phenotypes)
PhenoInfos$phenotypesinfo

# F-measure ignoring cells labeled 0 as in FlowCAP-I.

# Use FmeasureC() in any other case.
FmeasureC_no0(ref=manual_labels, pred=Tree_Partition)



if(interactive()){

# Scatterplots.
library(ggplot2)

# Ignoring cells labeled 0 as in FlowCAP-I.
rm_zeros <- which(!manual_labels)

# Building the data frame to scatter plot the data.
FL1 <- cellevents[-c(rm_zeros),"FL1"]
FL2 <- cellevents[-c(rm_zeros),"FL2"]
FL4 <- cellevents[-c(rm_zeros),"FL4"]
n <- length(FL1)
Labels <- c(manual_labels[-c(rm_zeros)]%%2+1, Tree_Partition[-c(rm_zeros)])
Labels <- as.factor(Labels)
method <- as.factor(c(rep("FlowCap-I",n),rep("CytomeTree",n)))

scatter_df <- data.frame("FL2" = FL2, "FL4" = FL4, "labels" = Labels, "method" = method)
p <- ggplot2::ggplot(scatter_df,  ggplot2::aes_string(x = "FL2", y = "FL4", colour = "labels")) +
 ggplot2::geom_point(alpha = 1,cex = 1) +
 ggplot2::scale_colour_manual(values = c("green","red","blue")) +
 ggplot2::facet_wrap(~ method) +
 ggplot2::theme_bw() +
 ggplot2::theme(legend.position="bottom")
p

}