ggcyto is a cytometry data visualization tool built around ggplot and the grammar of graphics paradigm. The software extends the popular ggplot2 framework, already familiar to many data scientists, enabling it to recog-nize the core Bioconductor flow cytometry data structures for gated and annotated cytometry data. It simplifies visualization and plotting of flow data for publication quality graphics.
There are three ways to construct the
ggcyto plots. Each represents a different level of complexity and flexibility. They meet the needs of various plot applications and thus are suitable for users at different levels of coding skills.
This inherits the spirit from ggplot’s
Quick plot, which simplies the plotting job by hiding more details from users and taking more assumptions for the plot.
ggcyto constructor along with overloaded
+ operator gives user more flexibility to fine-tune the plot yet still encapsulates lots of details that might be tedious and intimidating for many users.
See more examples of
ggcyto constructor here:
The package overloads ggplot’s
fortify S3 method so that
Cytometry data structures (e.g.
flowSet/flowFrame) are fully compatible with
ggplot. More examples of using
ggplot directly on
library(ggcyto) dataDir <- system.file("extdata",package="flowWorkspaceData") gs_orig <- load_gs(list.files(dataDir, pattern = "gs_bcell_auto",full = TRUE)) gs <- gs_clone(gs_orig)
#plot a gate by specifying the population node name (here it is 'CD3') autoplot(gs, "CD3")
#change the resolution p <- autoplot(gs, "CD3", bins = 64) p
#display the transformed value at breaks label by turning off the inverse transform autoplot(gs, "CD3", axis_inverse_trans = FALSE)
#you can switch the limits from default `instrument` to the actual `data` range p + ggcyto_par_set(limits = "data")
# Choose between `marker` and `channel` names for axis label text p + labs_cyto("channel") #default is "both"
# overlay another population 'IgD-CD27-' as dots on top of the existing plot p + geom_overlay("IgD-CD27-", alpha = 0.5, size = 0.1, color = "purple")
# plot a population without gate fs <- gs_pop_get_data(gs, "CD20") #extract the gated data as a flowSet autoplot(fs, "CD20", "CD19") #plot 2D
gs1 <- gs gs2 <- gs #construct the ggcyto object for gs1 p <- ggcyto(gs1, aes(cd24, cd38)) + geom_hex(bins = 128) p <- p + geom_gate("Transitional") #add gate #customize the stats layer p <- p + geom_stats(type = "count", size = 6, color = "red", fill = "green", adjust = 0.7) #customize the layer p <- p + labs_cyto("channel") #customize the axis limits p <- p + ggcyto_par_set(limits = "instrument") #add another population as the overlay dots p <- p + geom_overlay("IgD-CD27-", col = "black", size = 1.2, alpha = 0.4) #hide the legend p <- p + guides(fill=FALSE) p
#replace the data with gs2 and see the same visual effect p %+% gs2
autoplot(fs, "CD20") #1d density
#extract one sample as a flowFrame fr <- fs[] #plot 1d density on all available channels autoplot(fr)