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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

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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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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. Some basic packages/libraries should be installed while working with flow data, this list isn't comprehensive but it contains some useful basics. 

   Code Block

   source("https://bioconductor.org/biocLite.R") biocLite() biocLite("FlowSOM",dependencies=TRUE,suppressUpdates=TRUE) biocLite("flowCore",dependencies=TRUE,suppressUpdates=TRUE) biocLite("flowViz",dependencies=TRUE,suppressUpdates=TRUE) biocLite("flowUtils",dependencies=TRUE,suppressUpdates=TRUE) biocLite("geneplotter",dependencies=TRUE,suppressUpdates=TRUE) biocLite("Seurat",dependencies=TRUE,suppressUpdates=TRUE) biocLite("stringi",dependencies=TRUE,suppressUpdates=TRUE) biocLite("yaml",dependencies=TRUE,suppressUpdates=TRUE) biocLite("dplyr",dependencies=TRUE,suppressUpdates=TRUE) biocLite("openCyto",dependencies=TRUE,suppressUpdates=TRUE) biocLite("tsne",dependencies=TRUE,suppressUpdates=TRUE) biocLite("Rtsne",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) 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.
   
   

4. 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")

   
   

5. Examples to try...
   flowClust workflow in vignette
   flowSOM workflow in vignette 
   openCyto workflow in vignette
   Rtsne - https://github.com/lmweber/FlowSOM-Rtsne-example
   
   
   
6. 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

Specific workflows when dealing with data generated at WRHFlow

Please note I am no expert in R/RStudio and apologise in advance if my nomenclature or the below has errors.This is still under construction so please email me and we can update this guide ASAP.

Workflow 1 - Analysing a high parameter single FCS file.
This workflow will demonstrate tSNE, SPADE, Scaffold, FlowClust, FlowSOM, VORTEX

Workflow 2 - Comparing group A to group B