By specifying the dimensions through aes
and selecting the cell population through subset
, ggcyto
can easily visualize the gated data stored in GatingSet
.
p <- ggcyto(gs, aes(x = CD4, y = CD8), subset = "CD3+")
# 2d plot
p <- p + geom_hex(bins = 64)
p
We can use the instrument range to automatically filter out these outlier cell events
p + ggcyto_par_set(limits = "instrument")
Or by setting limits manually
myPars <- ggcyto_par_set(limits = list(x = c(0,3.5e3), y = c(-10, 4.1e3)))
p <- p + myPars# or xlim(0,3.5e3) + ylim(-10, 4e3)
p
To check what kind of visualization parameters can be changed through ggcyto_par_set
, simply print the default settings
ggcyto_par_default()
## $limits
## [1] "data"
##
## $facet
## <ggproto object: Class FacetWrap, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetWrap, Facet, gg>
##
## $hex_fill
## <ScaleContinuous>
## Range:
## Limits: 0 -- 1
##
## $lab
## $labels
## [1] "both"
##
## attr(,"class")
## [1] "labs_cyto"
##
## attr(,"class")
## [1] "ggcyto_par"
To plot a gate, simply pass the gate name to the geom_gate
layer
p + geom_gate("CD4")
More than one gate can be added as long as they share the same parent and dimensions
p <- p + geom_gate(c("CD4","CD8")) # short for geom_gate("CD8") + geom_gate("CD4")
p
## geom_stats By default, stats for all gate layers are added through empty geom_stats
layer.
p + geom_stats() + labs_cyto("marker")
Note that we choose to only display marker on axis through labs_cyto
layer here.
To add stats just for one specific gate, we can pass the gate name to geom_gate
p + geom_stats("CD4")
stats type, background color and position are all adjustable.
p + geom_stats("CD4", type = "count", size = 6, color = "white", fill = "black", adjust = 0.3)
When ‘subset’ is not specified, it is at abstract status thus can not be visualized
p <- ggcyto(gs, aes(x = CD4, y = CD8)) + geom_hex() + myPars
p
## Error in fortify_fs.GatingSet(model, ...): 'subset' must be instantiated by the actual node name!
## Make sure either 'subset' is specified or the 'geom_gate' layer is added.
unless it is instantiated by the gate layer, i.e. lookup the gating tree for the parent node based on the given gates in geom_gate
p <- p + geom_gate(c("CD4", "CD8"))
p
With geom_overlay
, you can easily overlay the additional cell populations (whose gates are not defined in the current projection) on top of the existing plot.
p + geom_overlay("CD8/CCR7- 45RA+", col = "black", size = 0.1, alpha = 0.4)
geom_overlay
automatically determines the overlay type (goem_point
or geom_density
) based on the number of dimensions specified in ggcyto
constructor. Note that we change the default y
axis from density
to count
in order to make the scales comparable for the stacked density layers. They are wrapped with ..
because they belong to the computed variables
.
p <- ggcyto(gs, aes(x = CD4), subset = "CD3+") + geom_density(aes(y = ..count..))
p + geom_overlay("CD8/CCR7- 45RA+", aes(y = ..count..), fill = "red")
Alternatively, we can choose to plot all children of one specified parent and projections
p <- ggcyto(gs, aes(x = 38, y = DR), subset = "CD4") + geom_hex(bins = 64) + geom_gate() + geom_stats()
p
Or we can add gate layer to any arbitary node instead of its parent node
ggcyto(gs, subset = "root", aes(x = CD4, y = CD8)) + geom_hex(bins = 64) + geom_gate("CD4") + myPars
Sometime it is helpful to display the axis label in raw scale by inverse transforming the axis without affecting the data
p + axis_x_inverse_trans() + axis_y_inverse_trans()
#add filter (consistent with `margin` behavior in flowViz)
# ggcyto(gs, aes(x = CD4, y = CD8), subset = "CD3+", filter = marginalFilter) + geom_hex(bins = 32, na.rm = T)