README.md

In DillonHammill/cytoSuite: Compensation, Gating & Visualisation Toolkit for Analysis of Flow Cytometry Data

CytoRSuite

CytoRSuite is designed to provide an interactive interface for the analysis of flow cytometry data in R. If you are new to CytoRSuite visit https://dillonhammill.github.io/CytoRSuite/ to get started.

Installation

CytoRSuite is built on the existing flow cytometry infrastructure for R developed by the RGLab. In order to properly install CytoRSuite and its dependencies the following platform-specific tools are required:

Windows and Linux

flowWorkspace requires additional C++ libraries to build from source using Rtools for windows users. Windows and Linux users should follow these instructions before proceeding.

Mac

Mac users will need to ensure that XQuartz is in their list of installed Applications. XQuartz is commonly found in the utilities folder. If XQuartz is missing from your list of applications it can be installed from this link. Restart your computer following installation.

flowCore, flowWorkspace & openCyto

Once these tools are installed, users can proceed to installing the release versions of flowCore, flowWorkspace and openCyto from Bioconductor.

# Bioconductor
install.packages("BiocManager")

# Install flowCore, flowWorkspace and openCyto
library(BiocManager)
install(c("flowCore", "flowWorkspace", "openCyto"))

CytoRSuite

Once these packages are successfully installed, users will need to install CytoRSuiteData which contains example datasets which will be used to demonstrate the features of CytoRSuite. CytoRSuite can then be installed from GitHub.

# CytoRSuiteData development version on GitHub
devtools::install_github("DillonHammill/CytoRSuiteData")

# CytoRSuite development version on GitHub
devtools::install_github("DillonHammill/CytoRSuite", build_vignettes = TRUE)

Overview

CytoRSuite provides an interactive interface for analysis of flow cytometry data. Some key features include:

• user guided automatic compensation using spillover_compute
• interactively modify spillover matrices using spillover_edit
• visualise compensation in all channels using cyto_plot_compensation
• easily associate experimental details with each file using cyto_annotate
• manual gate drawing using gate_draw
• ability to edit drawn gates using gate_edit
• remove gates using gate_remove
• gate saving directly to an openCyto gatingTemplate for future use
• support for using both manual and automated gating approaches through linking to openCyto
• visualisation of flowFrames, flowSets, GatingHierarchies and GatingSets using cyto_plot
• visualisation of complete gating strategies with back-gating and/or gate tracking using cyto_plot_gating_scheme
• visualisation of marker expression profiles in all channels using cyto_plot_profile
• export population level statistics using cyto_stats_compute

Usage

The full details of how CytoRSuite works will be tackled individually in the package vignettes, but a succinct usage outline is described below:

1. Compensation of fluorescent spillover

   1.1 Load compensation controls into a ncdfFlowSet

   ``` r library(CytoRSuite) library(CytoRSuiteData)

   Save .fcs files to folder "Compensation Controls" in working directory

   files <- list.files(path = "Compensation Controls", full.names = TRUE) fs <- read.ncdfFlowSet(files = files) ```

   1.2 Load compensation controls into GatingSet for gating

   ``` r

   Add flowSet to GatingSet

   gs <- GatingSet(fs) ```

   1.3 Gate Single Cells using gate_draw

   ``` r

   Gate Cells

   gate_draw(gs, parent = "root", alias = "Cells", channels = c("FSC-A","SSC-A"), type = "polygon", gatingTemplate = "Compensation-gatingTemplate.csv")

   Gate Single Cells

   gate_draw(gs, 
         parent = "Cells",
         alias = "Single Cells",
         channels = c("FSC-A","FSC-H"),
         type = "polygon",
         gatingTemplate = "Compensation-gatingTemplate.csv")
   

   ```

   

   1.4 Compute fluorescent spillover matrix using spillover_compute

   r spillover_compute(gs, parent = "Single Cells")

   1.5 Interactively edit computed spillover matrix using spillover_edit

   r spillover_edit(gs, parent = "Single Cells", channel_match = "Compensation-Channels.csv", spillover = "Spillover-Matrix.csv")

   

2. Analyse samples

   2.1 Load samples into a ncdfFlowSet

   ``` r

   Save samples to folder "Samples" in working directory

   files <- list.files(path = "Samples", full.names = TRUE) fs <- read.ncdfFlowSet(files = files) ```

   2.2 Annotate samples with markers using cyto_markers

   r cyto_markers(fs)

   

   2.3 Annotate samples with experimental details using cyto_annotate

   r cyto_annotate(fs)

   

   2.4 Add samples to GatingSet

   r gs <- GatingSet(fs)

   2.4 Apply fluorescent compensation

   ``` r

   Load in spillover matrix

   spill <- read.csv("Spillover-Matrix.csv", header = TRUE, row.names = 1) colnames(spill) <- rownames(spill)

   Apply compensation to samples

   gs <- compensate(gs, spill) ```

   2.5 Transform fluorescent channels for gating

   ``` r

   Fluorescent channels

   chans <- cyto_fluor_channels(gs)

   Logicle transformation

   trans <- estimateLogicle(gs[[4]], chans) gs <- transform(gs, trans) ```

   2.6 Build gating scheme using gate_draw

   ``` r

   Cells

   gate_draw(gs, parent = "Cells", alias = "Cells", channels = c("FSC-A","SSC-A"), gatingTemplate = "Example-gatingTemplate.csv")

   Copy above & edit to add new population(s)

   Repeat until gating scheme is complete

   ```

1. Visualise gating schemes using cyto_plot_gating_scheme

cyto_plot_gating_scheme(gs[[4]], back_gate = TRUE)

1. Export population-level statistics using cyto_stats_compute

cyto_stats_compute(gs,
                   alias = c("CD4 T Cells","CD8 T Cells"),
                   channels = c("CD44","CD69"),
                   stat = "median")

#> $`CD4 T Cells`
#>                 OVAConc Alexa Fluor 647-A   7-AAD-A
#> Activation1.fcs   0.000          675.1999  606.7077
#> Activation2.fcs   0.005          720.3611  656.8873
#> Activation3.fcs   0.050          971.4868  744.3725
#> Activation4.fcs   0.500         1503.4010 1233.6546
#> 
#> $`CD8 T Cells`
#>                 OVAConc Alexa Fluor 647-A  7-AAD-A
#> Activation1.fcs   0.000          414.2267 260.0531
#> Activation2.fcs   0.005          410.1949 248.5102
#> Activation3.fcs   0.050          454.0508 312.6521
#> Activation4.fcs   0.500          552.0260 382.5721

News

There is a Changelog for the GitHub master branch which will reflect any updates made to improve the stability, usability or plenitude of the package. Users should refer to the Changelog before installing new versions of the package.

Credits

CytoRSuite would not be possible without the existing flow cytometry infrastructure developed by the RGLab. CytoRSuite started out as simple plugin for openCyto to facilitate gate drawing but has evolved into a fully-fledged flow cytometry analysis package thanks to the support and guidance of members of the RGLab. Please take the time to check out their work on GitHub.

Development

CytoRSuite is a maturing package which will continue to be sculpted by the feedback and feature requests of users. The GitHub master branch will always contain the most stable build of the package. New features and updates will be made to a separate branch and merged to the master branch when stable and tested. The Changelog will reflect any changes made to the master branch.

Getting help

The Get Started and Reference sections on the CytoRSuite website are your first port of call if you require any help. For more detailed workflows refer the Articles tab. If you encounter any issues with the functioning of the package refer to these issues to see if the problem has been identified and resolved. Feel free to post new issues on the GitHub page if they have not already been addressed.

Code of conduct

Please note that the CytoRSuite project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

DillonHammill/cytoSuite documentation built on March 7, 2019, 10:09 a.m.