library(ggcyto) data(GvHD) fs <- GvHD[subset(pData(GvHD), Patient %in%5:7 & Visit %in% c(5:6))[["name"]]] fr <- fs[[1]]
ggcyto wrapper will construct the ggcyto object that inherits from ggplot class.
## [1] "ggcyto_flowSet"
## attr(,"package")
## [1] "ggcyto"
is(p, "ggplot")
## [1] TRUE
Since only one dimension is specified, we can add any 1d geom layer
p1 <- p + geom_histogram() p1

As shown, data is facetted by samples name automatically (i.e facet_wrap(~name)).
We can overwrite the default faceting by any variables that are defined in pData
pData(fs)
## Patient Visit Days Grade name
## s5a05 5 5 19 3 s5a05
## s5a06 5 6 26 3 s5a06
## s6a05 6 5 19 3 s6a05
## s6a06 6 6 27 3 s6a06
## s7a05 7 5 21 3 s7a05
## s7a06 7 6 28 3 s7a06
p1 + facet_grid(Patient~Visit)

To display 1d density
p + geom_density()

Fill the same color
p + geom_density(fill = "black")

Fill different colors
ggcyto(fs, aes(x = `FSC-H`, fill = name)) + geom_density(alpha = 0.2)

Or plot in the same panel by using ggplot directly (thus removing the default facetting effect)
ggplot(fs, aes(x = `FSC-H`, fill = name)) + geom_density(alpha = 0.2)

#you can use ggridges package to display stacked density plot require(ggridges) #stack by fcs file ('name') p + geom_density_ridges(aes(y = name)) + facet_null() #facet_null is used to remove the default facet_wrap (by 'name' column)

#or to stack by Visit and facet by patient p + geom_density_ridges(aes(y = Visit)) + facet_grid(~Patient)

# 2d hex p <- ggcyto(fs, aes(x = `FSC-H`, y = `SSC-H`)) p <- p + geom_hex(bins = 128) p

A default scale_fill_gradientn is applied to 2d hexbin plot.
To add limits

To overwrite the default fill gradien
p + scale_fill_gradientn(colours = rainbow(7), trans = "sqrt")

p + scale_fill_gradient(trans = "sqrt", low = "gray", high = "black")

geom_gate and geom_stats layersFirstly we create an ellipsoidGate with a data-driven method provided by flowStats package.
# estimate a lymphGate (which is an ellipsoidGate) for each sample lg <- flowStats::lymphGate(fs, channels=c("FSC-H", "SSC-H"),scale=0.6) # apply the ellipsoidGates to their corresponding samples fres <- filter(fs, lg)
Then pass the gates to the gate layer
p + geom_gate(lg)

We can also plot the rectangleGate, this time we simply replicate a static gate across samples:
rect.g <- rectangleGate(list("FSC-H" = c(300,500), "SSC-H" = c(50,200))) rect.gates <- sapply(sampleNames(fs), function(sn)rect.g)
Similarly, supply the list of gates to the geom_gate layer
p + geom_gate(rect.gates)

Stats layer can be added on top of gate
p + geom_gate(rect.gates) + geom_stats(size = 3)

The percentage of the gated population over its parent is displayed as geom_label. Alternatively cell count can be displayed by setting type argument in geom_stats function.
Here is another example of displaying the 1d gate generated by the automated gating method gate_mindensity from openCyto package.
den.gates.x <- fsApply(fs, openCyto::gate_mindensity, channel = "FSC-H", gate_range = c(100, 300), adjust = 1) p + geom_gate(den.gates.x) + geom_stats()

geom_gate layer supports the 1d gate on either dimension, which means it automatically determines between the vertical or horizontal lines based on the gate dimension and given aes.
den.gates.y <- fsApply(fs, openCyto::gate_mindensity, channel = "SSC-H", gate_range = c(100, 500), adjust = 1, positive = FALSE) p + geom_gate(den.gates.y) + geom_stats(value = lapply(rect.gates, function(g)0.1))

Here we also demenstrated the option of passing the precalculated arbitary stats value to geom_stats lay instead of letting it compute on the fly,
We can also put the 1d gate on density plot
ggcyto(fs, aes(x = `FSC-H`)) + geom_density(fill = "black", aes(y = ..scaled..)) + geom_gate(den.gates.x) + geom_stats(type = "count")

Without supplying data for geom_stats, we add stats layer for all the gate layers implicitly
p + geom_gate(lg) + geom_gate(rect.gates) + geom_stats(size = 3)

Or we can add stats layer specificly just for one of the gate layer
p + geom_gate(lg) + geom_gate(rect.gates) + geom_stats(gate = lg, size = 3)

Although ggcyto object is fully ggplot-compatible in terms of adding layers and parameters, its data slot MAY NOT be fully fortified to data.frame before it is printed/plotted.
class(p)
## [1] "ggcyto_flowSet"
## attr(,"package")
## [1] "ggcyto"
class(p$data)
## [1] "flowSet"
## attr(,"package")
## [1] "flowCore"
To convert it to a pure ggplot object, use as.ggplot function:
## [1] "gg" "ggplot"
class(p$data)
## [1] "data.table" "data.frame"