README.md

In flowSpy: A Toolkit for Flow And Mass Cytometry Data

flowSpy

flowSpy 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.

Instructions and standard workflow can be found at:

https://github.com/JhuangLab/flowSpy/tree/master/vignettes

And PDF version of the instructions and standard workflow can be found at:

• Quick_start_of_flowSpy

Use cases could be found at:

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

And PDF version of the specific workflows for flow and mass cytometry data can be found at:

• Preprocessing
• usecase1_2
• usecase3_4

You can view and clone the repository of flowSpy on GitHub at:

https://github.com/JhuangLab/flowSpy

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 flowSpy, a trajectory inference and visualization toolkit for flow and mass cytometry data.

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

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

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

• Trajectory Inference. flowSpy 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 flowSpy and find the key intermediate cell states based on trajectory value.

2 Installation

2.1 From Bioconductor

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

BiocManager::install("flowSpy")

2.2 From Github

This requires the devtools package to be installed first.

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

library(flowSpy)

3 Quick start (Standard Workflow)

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

# Read fcs files
fcs.path <- system.file("extdata", package = "flowSpy")
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 FSPY object
fspy <- createFSPY(raw.data = fcs.data, markers = markers,
                   meta.data = meta.data,
                   normalization.method = "log",
                   verbose = TRUE)

# See information
fspy

# Standard workflow of flowSpy
fspy <- runCluster(fspy)
fspy <- processingCluster(fspy)
fspy <- runFastPCA(fspy)
fspy <- runTSNE(fspy)
fspy <- runDiffusionMap(fspy)
fspy <- runUMAP(fspy)
fspy <- buildTree(fspy, dim.type = "umap", dim.use = 1:2)
fspy <- defRootCells(fspy, root.cells = "Root cells")
fspy <- runPseudotime(fspy)
fspy <- defLeafCells(fspy, leaf.cells = "Leaf cells")
fspy <- runWalk(fspy)

4 Reported bugs and solutions

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

5 Version History

Apr 20, 2020 - Version 1.1.5 - Changes: - Fixed installation errors in Linux and Windows - Fixed legend name of figure - Fixed errors in building vignette

Mar 19, 2020 - Version 1.0.4 - Changes: - Fixed installation errors in Linux and Windows

Mar 17, 2020 - Version 1.0.3 - Changes: - Fixed bugs in runDiff

Feb 15, 2020 - Version 1.0.1 - Changes: - Add uniform downsampling in function processingCluster

Oct 17, 2019 - Version 0.99.3 - Changes: - Fixed some warnings in the checking process of Bioc 3.10

Oct 17, 2019 - Version 0.99.2 - Changes: - Added documentation for some parameters - Changed the default parameter to raw in dim.type, and add match.arg() to dim.type selection

Sep 19, 2019 - Version 0.99.1 - Changes: - Fixed some bugs

Sep 19, 2019 - Version 0.2.8 - Changes: - Removed flowSpy.Rproj, .DS_Store and .gitattributes

Sep 11, 2019 - Version 0.2.7 - Changes: - Fixed some bugs - Fixed errors and warnings in R CMD check and R CMD BiocCheck

Aug 29, 2019 - Version 0.2.6 - Changes: - Fixed some bugs - Add trajectory plot function - Update vignette tutorial

Aug 14, 2019 - Version 0.2.5 - Changes: - Fixed some bugs - Add branch analysis and differentially expressed markers analysis

Aug 08, 2019 - Version 0.2.4 - Changes: - Fixed some bugs and finished vignette tutorial

July 24, 2019 - Version 0.2.3 - Changes: - Add phenoGraph algorithm

July 19, 2019 - Version 0.2.2 - Changes: - Fixed some bugs on cluster based downsampling

7 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.

flowSpy documentation built on Nov. 8, 2020, 6:53 p.m.