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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 FlowJo from here (and organise a licence if you haven't already got one)
5. Download and install SeqGeq from here (and organise a licence if you haven't already got one)
6. Download and install Matlab

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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 yo umay run into permission errors).

2. Install Bioconductor and additional packages that are used for flow cytometry data analysis

   Code Block
   ## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite() biocLite("flowCore",dependencies=TRUE) biocLite("flowViz") biocLite("flowUtils") biocLite("geneplotter")

   
   

3. Determine your working directory by entering 

   Code Block
   getwd()

   
   

4. Set your working directory by entering - for example

   Code Block
   setwd('C:/Users/utopi/Desktop/testdata')

   
   

5. Set the FCS folder as a variable

   Code Block
   ##file.name <- system.file("extdata","0877408774.B08", package="flowCore") read.FCS('A1.fcs')

   
   

6. Still in progress...

Example:

source("https://bioconductor.org/biocLite.R")

biocLite()

biocLite("flowCore",dependencies=TRUE)

install.packages("devtools").