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

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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

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• 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

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- outdated code. 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. 

   Code Block

   #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

   Code Block

   #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

   Code Block
   folderName <- "C:/Users/utopiUSERNAME/Desktop/testdata/"

   
   

10. Lets load the flowSOM package

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

    
    

11. Lets make the flowSOM object

    Code Block
    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

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

    
    

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Workflow 1 - Analysing a high parameter single FCS file

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Workflow 2 - Comparing group A to group B
This workflow will demonstrate comparison of samples. 

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/