In saeyslab/FlowSOM_workshop: How to get started with FlowSOM

In this script, we show how tSNE can be applied to an FCS file, to visualize the cells in 2 dimensions.

Setup

We load some previously installed libraries, which offer specific functionalities for flow cytometry data analysis. You can have a look at http://bioconductor.org/packages/release/BiocViews.html#___FlowCytometry to see more packages which might be of interest.

library(flowCore)
library(Rtsne)
library(FlowSOMworkshop)
library(FlowSOM)

We point to where the FlowJo workspace and the fcs file are saved on our computer, and we parse the fcs file:

wsp_file <- "../inst/extdata/manualGating.wsp"
fcs_file <- "../inst/extdata/21-10-15_Tube_013.fcs"
tube_13 <- parse_flowjo(fcs_file, wsp_file)

We will then select only the live cells to run tSNE on:

live <- gating_subset(tube_13, "Live")
live_ff <- live$flowFrame

While we read information of all gates in the gating hierarchy, some of them are only used as intermediate steps to finally recover the correct subsets of cells. We identify all gates which are really of interest to label the cells, and can then use this information to assign 1 final label to each cell.

cell_types <- c("Macrophages", "B cells", "NK cells", "NK T cells",
                "DCs", "Neutrophils", "Basophils", "T cells")

manual_labels <- manual_vector(live$gates,
                               cell_types)

head(manual_labels, n = 10)

tSNE

As tSNE can take a long time to run on large amounts of cells, we will work only on the 1000 first cells from the flowframe:

subsample <- live_ff[1:1000, ]

We will also select the channels which we are interested in to perform the tSNE analysis:

channels_of_interest <-  colnames(live_ff)[c(8:15, 17:19)]
print(get_markers(live_ff, channels_of_interest))

We can then apply tSNE to the matrix "subsample@exprs" that contains the expression values of 1000 live cells:

tsne <- Rtsne(subsample@exprs[, channels_of_interest])

And plot the result of the tSNE:

plot(tsne$Y)

We can also color this plot according to the expression values of a certain marker of interest:

plotSNE_marker(flowframe = subsample,
               tsne_result = tsne,
               marker = "CD19")

Or we can color this plot according to the results of the manual gating. To do so, we will first save the identities of our 1000 cells in an object:

cell_labels <- manual_labels[1:1000]

We can then plot the tSNE result, colored according to the manual gating:

plotSNE_manual_gating(tsne_result = tsne, cell_labels = cell_labels)

Exercises :

1) Run tSNE on the same subsample of cells, but this time using only the "CD3" and "CD19" channels.

channels_of_interest <- colnames(live_ff)[c(17:18)]
print(get_markers(live_ff, channels_of_interest))
tsne_2 <- Rtsne(subsample@exprs[,channels_of_interest])

2) Color the new tSNE results based on the expression of "CD3"

plotSNE_marker(flowframe = subsample,
               tsne_result = tsne_2,
               marker = "CD3")

3) Color the new tSNE results based on the expression of "CD19"

plotSNE_marker(flowframe = subsample,
               tsne_result = tsne_2,
               marker = "CD19")

saeyslab/FlowSOM_workshop documentation built on Sept. 3, 2021, 9:21 a.m.