README.md
In JhuangLab/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
See the quick start tutorial of flowSpy, please visit Quick start of flowSpy.
See the basic tutorial of flowSpy, please visit Tutorial of flowSpy.
See time-course data analysis of flowSpy, please visit Time-course workflow of flowSpy.
Use cases could be found at:
https://github.com/JhuangLab/flowSpy-dataset
You can view and clone the repository of flowSpy on GitHub at:
https://github.com/JhuangLab/flowSpy
Note
Dear flowSpy users:
To improve the identification of this package and avoid awkward duplication of names in some situations, we decided to change the name of flowSpy to CytoTree. The package name of CytoTree more fits the functional orientation of this software. The usage and update of flowSpy and CytoTree will be consistent until the end of Bioc 3.11. And for the 3.12 devel, flowSpy will be deprecated.
The package CytoTree has been added to Bioconductor (https://bioconductor.org/packages/CytoTree/), we recommend that users can download this package and replace flowSpy as soon as possible.
We apologized for the inconvenience.
flowSpy team
2020-07-09
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 forlynna@sjtu.edu.cn
5 Version History
Jul 09, 2020
- Version 1.2.3
- Changes:
- Add note and change package name to CytoTree
May 07, 2020
- Version 1.2.2
- Changes:
- Add solutions for large sample size analysis
May 04, 2020
- Version 1.2.1
- Changes:
- Set linear transformation of FSC and SSC as optional in runExprsMerge and runExprsExtract
- Fixed bugs in vignette
Apr 26, 2020
- Version 1.1.5
- Changes:
- Add preprocessing tutorial
- Fixed bugs in runExprsExtract and runExprsMerge
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
6 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.
JhuangLab/flowSpy documentation built on July 15, 2020, 8:31 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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
See the quick start tutorial of flowSpy, please visit Quick start of flowSpy.
See the basic tutorial of flowSpy, please visit Tutorial of flowSpy.
See time-course data analysis of flowSpy, please visit Time-course workflow of flowSpy.
Use cases could be found at:
https://github.com/JhuangLab/flowSpy-dataset
You can view and clone the repository of flowSpy on GitHub at:
https://github.com/JhuangLab/flowSpy
Note
Dear flowSpy users:
To improve the identification of this package and avoid awkward duplication of names in some situations, we decided to change the name of flowSpy to CytoTree. The package name of CytoTree more fits the functional orientation of this software. The usage and update of flowSpy and CytoTree will be consistent until the end of Bioc 3.11. And for the 3.12 devel, flowSpy will be deprecated.
The package CytoTree has been added to Bioconductor (https://bioconductor.org/packages/CytoTree/), we recommend that users can download this package and replace flowSpy as soon as possible.
We apologized for the inconvenience.
flowSpy team
2020-07-09
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.
flowSpycan help you to discover and identify subtypes of cells. -
Dimensionality Reduction. Several dimensionality reduction methods are provided in
flowSpypackage 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.
flowSpycan 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 inflowSpyand 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 forlynna@sjtu.edu.cn
5 Version History
Jul 09, 2020
- Version 1.2.3
- Changes:
- Add note and change package name to CytoTree
May 07, 2020 - Version 1.2.2 - Changes: - Add solutions for large sample size analysis
May 04, 2020
- Version 1.2.1
- Changes:
- Set linear transformation of FSC and SSC as optional in runExprsMerge and runExprsExtract
- Fixed bugs in vignette
Apr 26, 2020
- Version 1.1.5
- Changes:
- Add preprocessing tutorial
- Fixed bugs in runExprsExtract and runExprsMerge
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
6 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.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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