Automated data analysis of Flow Cytometry (FCM) data

Transcription

1 Automated data analysis of Flow Cytometry (FCM) data These exercises are divided into two parts: 1. Getting to know and be more comfortable with FCM data. 2. The automated analysis of some FCM data. Part 1: FCM data is stored in binary FCS files. We need to convert these files into ASCII text format that we can work with. We will use R (a free statistical software package) to do this, if you do not have R installed please visit this website to download it ( 1) Start up R On Windows/Mac you merely click the R icon. On UNIX/Linux, you start R by typing 'R' in a command terminal. 2) R: Install the flowcore package. If you have not already done this before, you need to install Bioconductor. source(" bioclite() provides a large range of tools (R packages) for the analysis and comprehension of high-throughput genomic data. Let s now install the flowcore package that we will be using: > source("

2 > bioclite("flowcore") 3) R: To use the flowcore package, use this command to load the package: > library("flowcore") Tip: if you are more curious about what the function library() actually does, then use?library to figure out 4) Download the FCS file (in a zipped folder, see course page). So now we re going to analyze some data: Peripheral blood mononuclear cells from a HIV infected individual and analyzed by Flow Cytometry. The cells were stained with 8 different fluorochrome-labelled antibodies. FCS name Cellular marker R780/60 CD3 V670/30 CD4 V585/42 CD8 B530/30 CD57 B695/40 CD28 V670/30 CD38 V450/50 PD- 1 V780/60 CD27 YG586/15 Tim- 3 YG620/14 CD45RO YG780/60 HLA- DR What s so interesting about these cellular markers? On CD4+ T-cells: Activation can be measured using CD38 and HLA-DR. Exhaustion can be measured using PD-1 and Tim-3. Senescence can be measured using CD28 and CD57. Memory can be measured using CD45RO and CD27.

3 5) R: Make new variable to store the file path to where the FCS file is stored. There are samples below. You can use your own path (e.g. ~/My/Lovely/Path/HIV.fcs) # For Mac/UNIX users: > fcs.filename <- "~/Desktop/HIV.fcs" # For Windows users: > fcs.filename <- "C:\\Data\\HIV.fcs" 6) R: Read in the FCS file into R and turns it into a format that we can read: > fcs <- read.fcs(file=fcs.filename) 7) R: Let s have a look at a snippet of what the FCM data looks like: > head(fcs) head is a function that returns a part of what is stored in the variable fcs. Question 1: Can you explain the column names? Question 2: What does each column represent? Question 3: What does each line represent? 8) R: Let s get a more general view of what the FCM data looks like: > summary(fcs) Summary is a function that returns a summary of what is stored in the variable fcs.

4 Question 4: Why are there negative values even though you are reading something? > dim(fcs) dim gives us the dimensions of our variable fcs. Question 5: How many events and parameters do you count? Question 6: What are those parameters (hint: look at the column names)? Question 7 (Bonus): Do you think every single event represent readings (parameters) from a single cell? Why/Why not? 9) R: Now that we ve looked at the numbers, let s have a look at a part of the data visually: > source(" > bioclite("flowviz") > library(flowviz) > plot(fcs[,grep("sc-",colnames(fcs),invert=t)]) Question 8: Take a screenshot of this. Question 9: What does the plot show? Did you notice that the plots looks like the events are squished up against towards the lower parts of the plot? This is because marker distributions are composed of mixtures of log-normal distributions. Which is why we need to transform the data. We will use logicle transformation in this case to give a more complete and readily interpretable representation of the data. To read more about this have a look at this article [ ]. 10) R: Transform the data: > fluorochromes <- grep("sc-",colnames(fcs),invert=t,value=t) > tr <- estimatelogicle(fcs, channels=fluorochromes)

5 > fcs <- transform(fcs,tr) Question 10: What are the ranges of values now for the measured fluorescent intensities of the fluorochromes? Hint: use summary. Question 11: What does the scatterplot of the transformed data look like? Hint: use the plot function. 11) R: Save the data to your computer: > write.table(exprs(fcs), file="~/desktop/hiv.csv", sep =",",col.names=t,row.names=f) If this does not work, you can use the same file in zipped folder.

6 Part 2: FLOCK Here we re going to automate this part by doing a different type of analysis: 1) Go to 2) First, sign up for free and log in using the username and password assigned to you (you should have gotten it in your ). 3) Go to the Flow Cytometry analysis overview page: 4) Get the file that we will work on: HIV.txt in your Data folder. 5) Upload the file HIV.txt:

7 6) Once it s uploaded you can change the fluorochrome names with User Assigned Name: 7) Then press Save Changes 8) Now you can start to analyze the uploaded FCM data with FLOCK: 9) Fill out the Name and Description:

8 10) Press Run FLOCK and wait until you ve gotten an that it works. 11) Have a look at the results. Question 12: How many populations does FLOCK find? 12) Click on results (under the status bar) and play around with the results by clicking and updating the colors. You should get something like this initially: