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Software that we may utilise in different workflows (or access ARGUS for a desktop pre-installed with software)

1. Download and install R (latest stable version, or a specific version if needed) from here - https://www.r-project.org/
2. Download and install RStudio Desktop from here - https://www.rstudio.com/products/rstudio/#Desktop
3. Download and install Rtools (for package building from GitHub repositories) - https://cran.r-project.org/bin/windows/Rtools/
4. Download and install devtools (for package building from GitHub repositories - install.packages("devtools")
5. Download and install FlowJo from here (and organise a licence if you haven't already got one)
6. Download and install SeqGeq from here (and organise a licence if you haven't already got one)
7. Download and install Matlab

Excellent links for information that was used for R/Rstudio/flowCore

1. Basic intro to R - https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf
2. Introduction to flowCore - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-106
3. R package vigenettes (which are the mini guides for each package developed in R)
4. Flow Cytometry Bioinformatics 2013 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3867282/

Data pre-processing in FlowJo using R plugins

FlowJo check sample quality

In FlowJo, after importing your cytometry data, click the 'Check sample quality' button in the tools workspace. This will flag samples that should be checked. FlowJo plots the median values for each parameter over time and flags any that are outside of 2 standard deviations. Green is good. Any thing else should be reviewed. 

Active sample quality check - FlowAI in FlowJo

FlowJo also provides access to 2 active cytometry data quality plugins. FlowClean & FlowAI

To enable FlowAI

• Have R installed (version >3.4)
• Install BioConductor & flowCore
  • source("https://bioconductor.org/biocLite.R")
  • biocLite()
  • biocLite("flowCore")
• Install PNG package
  • install.packages("png")
• Install flowAI
  • biocLite("flowAI")
• Install stringi (I required this package additionally)
  • biocLite("stringi")
• Load libraries in R
  • library(flowCore)
    library(stringi)
    library(flowAI)
• Have FlowJo installed with the correct R path in the preferences
  
• If not already present, download and move FlowAI.jar file into the FlowJo plugins folder (available from the FlowJo exchange)
• Restart FlowJo
• Select sample and navigate to Plugins, FlowAI. Select parameters and compute. Example output is shown below including layout depiction.
  
  

Active sample quality check - FlowClean in FlowJo

To enable FlowClean

• Have R installed (version >3.4)
• Install BioConductor
  • source("https://bioconductor.org/biocLite.R")
  • biocLite("flowCore")
  • biocLite("flowClean")
• Load libraries in R
  • library(flowCore)
    library(flowClean)
• Have FlowJo installed with the correct R path in the preferences
  
• If not already present, download and move FlowClean.jar file into the FlowJo plugins folder (available from the FlowJo exchange)
• Restart FlowJo
• Select sample and navigate to Plugins, FlowClean. Select parameters and compute. Example output is shown below.
  

Reading and analysing FCS files in R/RStudio - outdated code. 

1. Open RStudio (ensure you run as administrator, otherwise you may run into permission errors).
2. Make a new project (a project can be considered a workspace where everything can be saved to)
3. You want to download and load packages for the analysis that you will be doing (not efficient to load all the libraries, as RStudio is limited to 100, I learnt by experience, to see loaded libraries, type library())

4. Some basic packages/libraries should be installed while working with flow data, this list isn't comprehensive but it contains some for the workflows listed, you can load the libraries that you will use. 

   #this will download the core Bioconductor packages
   source("https://bioconductor.org/biocLite.R")
   biocLite()
   
   #this will download flowCore (useful core for working with flow data)
   source("https://bioconductor.org/biocLite.R")
   biocLite("flowCore",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download flowUtils (another set of useful tools)
   source("https://bioconductor.org/biocLite.R")
   biocLite("flowUtils",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download flowViz
   source("https://bioconductor.org/biocLite.R")
   biocLite("flowViz",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download flowSOM 
   source("https://bioconductor.org/biocLite.R")
   biocLite("FlowSOM",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download geneplotter 
   source("https://bioconductor.org/biocLite.R")
   biocLite("geneplotter",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download Seurat 
   source("https://bioconductor.org/biocLite.R")
   biocLite("Seurat",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download stringi
   source("https://bioconductor.org/biocLite.R")
   biocLite("stringi",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download yaml 
   source("https://bioconductor.org/biocLite.R")
   biocLite("yaml",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download dplyr
   source("https://bioconductor.org/biocLite.R")
   biocLite("dplyr",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download openCyto 
   source("https://bioconductor.org/biocLite.R")
   biocLite("openCyto",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download tsne 
   source("https://bioconductor.org/biocLite.R")
   biocLite("tsne",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download Rtsne 
   source("https://bioconductor.org/biocLite.R")
   biocLite("Rtsne",dependencies=TRUE,suppressUpdates=TRUE)
   
   #this will download edgeR 
   source("https://bioconductor.org/biocLite.R")
   biocLite("edgeR",dependencies=TRUE,suppressUpdates=TRUE)
   
   install.packages("dplyr",dependencies=TRUE,suppressUpdates=TRUE)
   install.packages("yaml",dependencies=TRUE,suppressUpdates=TRUE) 
   install.packages("devtools",dependencies=TRUE,suppressUpdates=TRUE)
   
   #use this to load downloaded packages
   library(flowCore)
   library(FlowSOM)
   library(flowViz)
   library(flowUtils)
   library(geneplotter)
   library(Seurat)
   library(dplyr)
   library(yaml)
   library(stringi)
   library(openCyto)
   library(tsne)
   library(Rtsne)

   Note: There are many Vignettes in the packages which are ever so helpful. Vignettes are help guides that can help to show you how to use different tools/functions.
   
   

5. Some commonly used codes - good to get familiar with

   #gets to working directory
   getwd()
   #sets the working directory
   setwd('C:/Users/utopi/Desktop/testdata')
   #assign a file to a variable
   fileName <- "C:/Users/utopi/Desktop/testdata/sample.fcs"
   #assign a folder to a variable
   folderName <- "C:/Users/utopi/Desktop/testdata/"
   #read a FCS file to a variable using read.FCS from flowCore
   data1 = read.FCS('A1.fcs')
   #assign a variable an example data file i.e. from a vignette example
   fileName <- system.file("extdata","lymphocytes.fcs",package="FlowSOM")

6. Examples to try...
   flowClust workflow in vignette
   flowSOM workflow in vignette 
   openCyto workflow in vignette
   Rtsne - https://github.com/lmweber/FlowSOM-Rtsne-example
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4980779/
   https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.23030  - good comparison paper
   
   Lets analyse some data..
7. Put your data in a folder on your computer
8. In RStudio go to file and then new R script (we will be writing the script so that we can rerun it if needed)

9. Lets assign the folder to a variable

   folderName <- "C:/Users/USERNAME/Desktop/testdata/"

10. Lets load the flowSOM package

    #this will download flowSOM 
    source("https://bioconductor.org/biocLite.R")
    biocLite("FlowSOM",dependencies=TRUE,suppressUpdates=TRUE)library(flowSOM)

11. Lets make the flowSOM object

    fSOM <- FlowSOM(file,
                    # Input options:
                    compensate = TRUE,transform = TRUE,
                    scale = TRUE,
                    # SOM options:(will use parameters 1,4 and 5 thru 7)
                    colsToUse = c(1,4,5:7), xdim = 7, ydim = 7,
                    # Metaclustering options:
                    nClus = 10,
                    # Seed for reproducible results:
                    seed = 42)

12. Lets view the flowSOM

    PlotStars(fSOM$FlowSOM,backgroundValues = as.factor(fSOM$metaclustering))
    

Specific workflows when dealing with data generated at WRHFlow

Workflow 2 - Comparing group A to group B

Thomas Ashhurst over at Sydney Cytometry has compiled a number of other scripts together to put together CAPX for R that automatically generates some pretty tSNE & flowSOM visualisations. 

https://github.com/sydneycytometry/