knitr::opts_chunk$set(echo = TRUE, cache = FALSE, eval = TRUE,
warning = TRUE, message = TRUE,
fig.width = 6, fig.height = 5)Introduction
Although multidimensional single-cell-based flow and mass cytometry have been increasingly applied to microenvironmental composition and stem-cell research, integrated analysis workflows to facilitate the interpretation of experimental cytometry data remain underdeveloped. We present CytoTree, a comprehensive R package designed for the analysis and interpretation of flow and mass cytometry data. We applied CytoTree to mass cytometry and time-course flow cytometry data to demonstrate the usage and practical utility of its computational modules. CytoTree is a reliable tool for multidimensional cytometry data workflows and produces compelling results for trajectory construction and pseudotime estimation.
Overview of CytoTree workflow
The CytoTree package is developed to complete the majority of standard analysis and visualization workflow for FCS data. In CytoTree workflow, an S4 object in R is built to implement the statistical and computational approach, and all computational modules are integrated into one single channel which only requires a specified input data format.
CytoTree can help you to perform four main types of analysis:
Clustering.
CytoTreecan help you to discover and identify subtypes of cells.Dimensionality Reduction. Several dimensionality reduction methods are provided in
CytoTreepackage 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.
CytoTreecan 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
igraphpackage. Subset FCS data set inCytoTreeand find the key intermediate cell states based on trajectory value.
Quick start
# 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)## 2022-04-26 16:24:47 Number of cells in processing: 600## 2022-04-26 16:24:47 rownames of meta.data and raw.data will be named using column cell## 2022-04-26 16:24:47 Index of markers in processing## 2022-04-26 16:24:47 Creating CYT object.## 2022-04-26 16:24:47 Determining normalization factors## 2022-04-26 16:24:47 Normalization and log-transformation.## 2022-04-26 16:24:47 Build CYT object succeed## CYT Information:
## Input cell number: 600 cells
## Enroll marker number: 10 markers
## Cells after downsampling: 600 cells# Cluster cells by SOM algorithm
# Set random seed to make results reproducible
set.seed(1)
cyt <- runCluster(cyt, cluster.method = "som")## Mapping data to SOM# Do not perform downsampling
set.seed(1)
cyt <- processingCluster(cyt)
# run Principal Component Analysis (PCA)
cyt <- runFastPCA(cyt)
# run t-Distributed Stochastic Neighbor Embedding (tSNE)
cyt <- runTSNE(cyt)
# run Diffusion map
cyt <- runDiffusionMap(cyt)
# run Uniform Manifold Approximation and Projection (UMAP)
cyt <- runUMAP(cyt)
# build minimum spanning tree based on tsne
cyt <- buildTree(cyt, dim.type = "tsne", dim.use = 1:2)
# DEGs of different branch
diff.list <- runDiff(cyt)
# define root cells
cyt <- defRootCells(cyt, root.cells = c(28,26))
# run pseudotime
cyt <- runPseudotime(cyt, verbose = TRUE, dim.type = "raw")## 2022-04-26 16:24:53 Calculating Pseudotime.## 2022-04-26 16:24:53 Pseudotime exists in meta.data, it will be replaced.## 2022-04-26 16:24:53 The log data will be used to calculate pseudotime## 2022-04-26 16:24:53 Calculating Pseudotime completed.## 2022-04-26 16:24:53 37 cells will be added to leaf.cells .## 2022-04-26 16:24:53 Calculating walk between root.cells and leaf.cells .## 2022-04-26 16:24:53 Generating an adjacency matrix.## 2022-04-26 16:24:53 Walk forward.## 2022-04-26 16:24:53 Calculating walk completed.# Save object
if (FALSE) {
save(cyt, file = "Path to you output directory")
}
######################## Visualization
# Plot 2D tSNE. And cells are colored by cluster id
plot2D(cyt, item.use = c("tSNE_1", "tSNE_2"), color.by = "cluster.id",
alpha = 1, main = "tSNE", category = "categorical", show.cluser.id = TRUE)
# Plot 2D UMAP. And cells are colored by cluster id
plot2D(cyt, item.use = c("UMAP_1", "UMAP_2"), color.by = "cluster.id",
alpha = 1, main = "UMAP", category = "categorical", show.cluser.id = TRUE)
# Plot 2D tSNE. And cells are colored by cluster id
plot2D(cyt, item.use = c("tSNE_1", "tSNE_2"), color.by = "branch.id",
alpha = 1, main = "tSNE", category = "categorical", show.cluser.id = TRUE)
# Plot 2D UMAP. And cells are colored by cluster id
plot2D(cyt, item.use = c("UMAP_1", "UMAP_2"), color.by = "branch.id",
alpha = 1, main = "UMAP", category = "categorical", show.cluser.id = TRUE)
# Plot 2D tSNE. And cells are colored by stage
plot2D(cyt, item.use = c("tSNE_1", "tSNE_2"), color.by = "stage",
alpha = 1, main = "UMAP", category = "categorical") +
scale_color_manual(values = c("#00599F","#009900","#FF9933",
"#FF99FF","#7A06A0","#FF3222"))
# Plot 2D UMAP. And cells are colored by stage
plot2D(cyt, item.use = c("UMAP_1", "UMAP_2"), color.by = "stage",
alpha = 1, main = "UMAP", category = "categorical") +
scale_color_manual(values = c("#00599F","#009900","#FF9933",
"#FF99FF","#7A06A0","#FF3222"))
# Tree plot
plotTree(cyt, color.by = "D0.percent", show.node.name = TRUE, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
plotTree(cyt, color.by = "CD43", show.node.name = TRUE, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
# plot clusters
plotCluster(cyt, item.use = c("tSNE_1", "tSNE_2"), category = "numeric",
size = 100, color.by = "CD45RA") +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
# plot pie tree
plotPieTree(cyt, cex.size = 3, size.by.cell.number = TRUE) +
scale_fill_manual(values = c("#00599F","#FF3222","#009900",
"#FF9933","#FF99FF","#7A06A0"))
# plot pie cluster
plotPieCluster(cyt, item.use = c("tSNE_1", "tSNE_2"), cex.size = 40) +
scale_fill_manual(values = c("#00599F","#FF3222","#009900",
"#FF9933","#FF99FF","#7A06A0"))
## Warning: `fun.y` is deprecated. Use `fun` instead.## Warning: Groups with fewer than two data points have been dropped.
## Warning: `fun.y` is deprecated. Use `fun` instead.
# UMAP plot colored by pseudotime
plot2D(cyt, item.use = c("UMAP_1", "UMAP_2"), category = "numeric",
size = 1, color.by = "pseudotime") +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))
# tSNE plot colored by pseudotime
plot2D(cyt, item.use = c("tSNE_1", "tSNE_2"), category = "numeric",
size = 1, color.by = "pseudotime") +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))
# denisty plot by different stage
plotPseudotimeDensity(cyt, adjust = 1) +
scale_color_manual(values = c("#00599F","#009900","#FF9933",
"#FF99FF","#7A06A0","#FF3222"))
# Tree plot
plotTree(cyt, color.by = "pseudotime", cex.size = 1.5) +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))
plotViolin(cyt, color.by = "cluster.id", order.by = "pseudotime",
marker = "CD49f", text.angle = 90)## Warning: `fun.y` is deprecated. Use `fun` instead.
## Groups with fewer than two data points have been dropped.
# trajectory value
plotPseudotimeTraj(cyt, var.cols = TRUE) +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))## `geom_smooth()` using formula 'y ~ x'
plotHeatmap(cyt, downsize = 1000, cluster_rows = TRUE, clustering_method = "ward.D",
color = colorRampPalette(c("#00599F","#EEEEEE","#FF3222"))(100))
# plot cluster
plotCluster(cyt, item.use = c("tSNE_1", "tSNE_2"), color.by = "traj.value.log",
size = 10, show.cluser.id = TRUE, category = "numeric") +
scale_colour_gradientn(colors = c("#EEEEEE", "#FF3222", "#CC0000", "#CC0000"))
Announcement
The previous version of CytoTree is flowSpy link to GitHub and link to Bioconductor. To improve the identification and avoid awkward duplication of names in some situations, we changed the name of flowSpy to CytoTree. CytoTree more fits the functional orientation of this software.
We apologized for the inconvenience.
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