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- Download and install R (latest stable version, or a specific version if needed) from here - https://www.r-project.org/
- Download and install RStudio Desktop from here - https://www.rstudio.com/products/rstudio/#Desktop
- Download and install Rtools (for package building from GitHub repositories) - https://cran.r-project.org/bin/windows/Rtools/
- Download and install devtools (for package building from GitHub repositories -
install.packages("devtools") - Download and install FlowJo from here (and organise a licence if you haven't already got one)
- Download and install SeqGeq from here (and organise a licence if you haven't already got one)
- Download and install Matlab
| Software | Advantages | Disadvantages | Cost |
|---|---|---|---|
| R | Fast Opensource HPC Great community Scalable to large datasets Most automated population clustering options | Learning curve Support is from the community | FREE |
| RStudio | Same as R but with freiendlier user interface Code can be deployed on HPC | Same as R Learning curve (less than R due to GUI) | FREE |
| RTools | Used to deploy newly published methods | Complicated | FREE |
| FlowJo | Commercial software Powerful Easy to use Large number of advanced users Works well | Cumbersome to implement new advanced analysis methods | $$ |
| SeqGeq | Useful for single cell workflows (RNA/DNA) Commercial software Powerful Easy to use Large number of advanced users Many automated population clustering options | $$ | |
| Matlab | Powerful Some published workflows use Matlab Code is easier to understand compared to R (personal advantage, although this will likely change soon) | Flow cytometry analysis community is heavily developing in R compared to Matlab |
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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- Open RStudio (ensure you run as administrator, otherwise you may run into permission errors).
- Make a new project (a project can be considered a workspace where everything can be saved to)
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(pkgs =c"FlowSOM",dependencies=TRUE,suppressUpdates=TRUE) biocLite("flowCore","FlowSOM",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"), ask = FALSE,dependencies=TRUE,suppressUpdates=TRUE) install.packages("dplyr",dependencies=TRUE,suppressUpdates=TRUE) install.packages("yaml",dependencies=TRUE,suppressUpdates=TRUE) install.packages("devtools",dependencies=TRUE,asksuppressUpdates=FALSETRUE) library(flowCore,flowViz,flowUtils,geneplotter,Seurat,dplyr,yaml,stringi,devtools) library(FlowSOM) library(flowViz) library(flowUtils) library(geneplotter) library(Seurat) library(dplyr) library(yaml) library(stringi)
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.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")- Lets analyse some data..
- Put your data in a folder on your computer
- In RStudio go to file and then new R script (we will be writing the script so that we can rerun it if needed)
- Lets assign the folder to a variable
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