Cytometry
data with ggplotlibrary(ggcyto)
dataDir <- system.file("extdata",package="flowWorkspaceData")
3 types of plot constructorggplotThe overloaded fority methods empower
ggplot to work with all the major Cytometry data structures
right away, which allows users to do all kinds of highly customized and
versatile plots.
GatingSetgs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))
attr(gs, "subset") <- "CD3+"
ggplot(gs, aes(x = `<B710-A>`, y = `<R780-A>`)) + geom_hex(bins = 128) + scale_fill_gradientn(colours = gray.colors(9))
flowSet/ncdfFlowSet/flowFramefs <- gs_pop_get_data(gs, "CD3+")
ggplot(fs, aes(x = `<B710-A>`)) + geom_density(fill = "blue", alpha= 0.5)
gatesgates <- filterList(gs_pop_get_gate(gs, "CD8"))
ggplot(gs, aes(x = `<B710-A>`, y = `<R780-A>`)) + geom_hex(bins = 128) + geom_polygon(data = gates, fill = NA, col = "purple")
ggcytoggcyto constructor along with overloaded +
operator encapsulate lots of details that might be tedious and
intimidating for many users.
ggcyto(gs, aes(x = CD4, y = CD8)) + geom_hex(bins = 128) + geom_gate("CD8")
It simplies the plotting by: * add a default scale_fill_gradientn for
you * fuzzy-matching in aes by either detector or
fluorochromes names * determine the parent popoulation
automatically * exact and plot the gate object by simply referring to
the child population name
autoplotInheriting the spirit from ggplot’s Quick plot, it
further simply the plotting job by hiding more details from users and
taking more assumptions for the plot.
flowSet, it determines geom
type automatically by the number of dim suppliedGatingSet, it further skip the need of
dim by guessing it from the children gate#1d
autoplot(fs, "CD4")
#2d
autoplot(fs, "CD4", "CD8", bins = 64)
autoplot(gs, c("CD4", "CD8"), bins = 64)
It is done by different scales layers speically designed
for cytometry
data(GvHD)
fr <- GvHD[[1]]
p <- autoplot(fr, "FL1-H")
p #raw scale
p + scale_x_logicle() #flowCore logicle scale
p + scale_x_flowJo_fasinh() # flowJo fasinh
p + scale_x_flowJo_biexp() # flowJo biexponential
geom_gate layerIt hides the complex details pf plotting different geometric shapes
fr <- fs[[1]]
p <- autoplot(fr,"CD4", "CD8") + ggcyto_par_set(limits = "instrument")
#1d gate vertical
gate_1d_v <- openCyto::gate_mindensity(fr, "<B710-A>")
p + geom_gate(gate_1d_v)
#1d gate horizontal
gate_1d_h <- openCyto::gate_mindensity(fr, "<R780-A>")
p + geom_gate(gate_1d_h)
#2d rectangle gate
gate_rect <- rectangleGate("<B710-A>" = c(gate_1d_v@min, 4e3), "<R780-A>" = c(gate_1d_h@min, 4e3))
p + geom_gate(gate_rect)
#ellipsoid Gate
gate_ellip <- gh_pop_get_gate(gs[[1]], "CD4")
class(gate_ellip)
## [1] "ellipsoidGate"
## attr(,"package")
## [1] "flowCore"
p + geom_gate(gate_ellip)
geom_statsp <- ggcyto(gs, aes(x = "CD4", y = "CD8"), subset = "CD3+") + geom_hex()
p + geom_gate("CD4") + geom_stats()
p + geom_gate("CD4") + geom_stats(type = "count") #display cell counts
axis_inverse_transIt can display the log scaled data in the original
value
p # axis display the transformed values
p + axis_x_inverse_trans() # restore the x axis to the raw values
It currently only works with GatingSet.
Optionally you can set limits by instrument or
data range
p <- p + ggcyto_par_set(limits = "instrument")
p
You can choose between marker and channel
names (or both by default)
p + labs_cyto("markers")
ggcyto_par_setIt aggregates the different settings in one layer
#put all the customized settings in one layer
mySettings <- ggcyto_par_set(limits = "instrument"
, facet = facet_wrap("name")
, hex_fill = scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(11, "Spectral")))
, lab = labs_cyto("marker")
)
# and use it repeatly in the plots later (similar to the `theme` concept)
p + mySettings
Currently we only support 4 settings, but will add more
in future.
as.ggplotIt allows user to convert ggcyto objects to pure
ggplot objects for further the manipulating jobs that can
not be done within ggcyto framework.
class(p) # may not fully compatile with all the `ggplot` functions
## [1] "ggcyto_GatingSet"
## attr(,"package")
## [1] "ggcyto"
p1 <- as.ggplot(p)
class(p1) # a pure ggplot object, thus can work with all the `ggplot` features
## [1] "gg" "ggplot"
Layout many gate plots on the same page
When plooting a GatingHierarchy, multiple cell
populations with their asssociated gates can be plotted in different
panels of the same plot.
gh <- gs[[1]]
nodes <- gs_get_pop_paths(gh, path = "auto")[c(3:9, 14)]
nodes
## [1] "singlets" "CD3+" "CD4" "CD4/38- DR+" "CD4/38+ DR+"
## [6] "CD4/38+ DR-" "CD4/38- DR-" "CD8"
p <- autoplot(gh, nodes, bins = 64)
class(p)
## [1] "ggcyto_GatingLayout"
## attr(,"package")
## [1] "ggcyto"
p
As you see, this generates a special ggcyto_GatingLayout
object which is a container storing multiple ggcyto
objects. You can do more about the plot layout with the helper function
ggcyto_arrange. For example, to arrange it as one-row
gtable object
gt <- ggcyto_arrange(p, nrow = 1)
class(gt)
## [1] "gtable" "gTree" "grob" "gDesc"
plot(gt)
or even combine it with other ggcyto_GatingLayout
objects(or any gtable objects) and print it on the sampe
page
p2 <- autoplot(gh_pop_get_data(gh, "CD3+")[,5:8]) # some density plot
p2@arrange.main <- ""#clear the default title
gt2 <- ggcyto_arrange(p2, nrow = 1)
gt3 <- gridExtra::gtable_rbind(gt, gt2)
plot(gt3)