CytoTree

CytoTree is an R package to implement cellular subpopulations identification, trajectory inference, pseudotime estimation and visualization for flow and mass cytometry data. This package is developed and maintained by JhuangLab at Shanghai Institute of Hematology.

See the tutorial of CytoTree, please visit Tutorial of CytoTree.

Use cases could be found at:

https://github.com/JhuangLab/CytoTree-dataset

You can view and clone the use cases of CytoTree on GitHub at by git clone https://github.com/JhuangLab/CytoTree-dataset

1 Introduction

Multidimensional single-cell-based flow and mass cytometry enable ones to analyze multiple single-cell parameters and identify cellular populations. Based on classical software for analyzing Flow Cytometry Standard (FCS) data such as flowSOM[1] and SPADE[2], methods for inferencing cellular trajectory during a biological process are very important. To objectively inference differential trajectory based on time courses FCS data, we present CytoTree, a trajectory inference and visualization toolkit for flow and mass cytometry data.

CytoTree can help you to perform four main types of analysis:

• Clustering. CytoTree can help you to discover and identify subtypes of cells.

• Dimensionality Reduction. Several dimensionality reduction methods are provided in CytoTree package such as Principal Components Analysis (PCA), t-distributed Stochastic Neighbor Embedding (tSNE), Diffusion Maps and Uniform Manifold Approximation and Projection (UMAP). CytoTree provides both cell-based and cluster-based dimensionality reduction.

• Trajectory Inference. CytoTree can help you to construct the cellular differential based on minimum spanning tree (MST) algorithm.

• Pseudotime and Intermediate states definition. The root cells need to be defined by users. The trajctroy value will be calculated based on Shortest Path from root cells and leaf cells using R igraph package. Subset FCS data set in CytoTree and find the key intermediate cell states based on trajectory value.

2 Installation

2.1 GitHub

This requires the devtools package to be pre-installed first.

# If not already installed
install.packages("devtools") 
devtools::install_github("JhuangLab/CytoTree")

library(CytoTree)

The link of CytoTree on GitHub can be visited at https://github.com/JhuangLab/CytoTree.

2.2 Bioconductor

This requires the BiocManager package to be pre-installed first.

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CytoTree")

The link of CytoTree on Bioconductor can be visited at https://bioconductor.org/packages/CytoTree/.

3 Quick start (Standard Workflow)

# Loading packages
suppressMessages({
library(ggplot2)
library(CytoTree)
library(flowCore)
library(stringr)
})

# Read fcs files
fcs.path <- system.file("extdata", package = "CytoTree")
fcs.files <- list.files(fcs.path, pattern = '.FCS$', full = TRUE)

fcs.data <- runExprsMerge(fcs.files, comp = FALSE, transformMethod = "none")

# Refine colnames of fcs data
recol <- c(`FITC-A<CD43>` = "CD43", `APC-A<CD34>` = "CD34", 
           `BV421-A<CD90>` = "CD90", `BV510-A<CD45RA>` = "CD45RA", 
           `BV605-A<CD31>` = "CD31", `BV650-A<CD49f>` = "CD49f",
           `BV 735-A<CD73>` = "CD73", `BV786-A<CD45>` = "CD45", 
           `PE-A<FLK1>` = "FLK1", `PE-Cy7-A<CD38>` = "CD38")
colnames(fcs.data)[match(names(recol), colnames(fcs.data))] = recol
fcs.data <- fcs.data[, recol]

day.list <- c("D0", "D2", "D4", "D6", "D8", "D10")
meta.data <- data.frame(cell = rownames(fcs.data),
                        stage = str_replace(rownames(fcs.data), regex(".FCS.+"), "") )
meta.data$stage <- factor(as.character(meta.data$stage), levels = day.list)

markers <- c("CD43","CD34","CD90","CD45RA","CD31","CD49f","CD73","CD45","FLK1","CD38")

# Build the CYT object
cyt <- createCYT(raw.data = fcs.data, markers = markers,
                   meta.data = meta.data,
                   normalization.method = "log",
                   verbose = TRUE)

# See information
cyt

# Standard workflow of CytoTree
cyt <- runCluster(cyt)
cyt <- processingCluster(cyt)
cyt <- runFastPCA(cyt)
cyt <- runTSNE(cyt)
cyt <- runDiffusionMap(cyt)
cyt <- runUMAP(cyt)
cyt <- buildTree(cyt)
cyt <- defRootCells(cyt, root.cells = 1)
cyt <- runPseudotime(cyt)
cyt <- defLeafCells(cyt, leaf.cells = 2)
cyt <- runWalk(cyt)

4 Reported bugs and solutions

If there is any error in installing or librarying the CytoTree package, please contact us via e-mail [email protected]

5 Reference

[1] Sofie Van Gassen, Britt Callebaut and Yvan Saeys (2019). FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. http://www.r-project.org, http://dambi.ugent.be.

[2] Qiu, P., et al., Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol, 2011. 29(10): p.886-91.