    library(ggcyto)
    dataDir <- system.file("extdata",package="flowWorkspaceData")

## 1: suppoort `3` types of plot constructor

- represent different levels of complexity and flexibility
- meet the needs of various plot applications
- suitable for users at different levels of coding skills.

### low level: `ggplot`

The 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 versitled plots.

#### `GatingSet`

    gs <- 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/flowFrame`

    fs <- gs_pop_get_data(gs, "CD3+")
    ggplot(fs, aes(x = `<B710-A>`)) + geom_density(fill = "blue", alpha= 0.5)

#### `gates`

    gates <- 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")

### medium level: `ggcyto`

`ggcyto` 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

### top level: `autoplot`

Inheriting 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.

- when plotting `flowSet`, it determines `geom` type automatically by the number of `dim` supplied
- for `GatingSet`, 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)

## 2: in-line transformation

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

## 3: generic `geom_gate` layer

It 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)

## 4: `geom_stats`

    p <- 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

## 5: `axis_inverse_trans`

It 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`.

## 6: auto limits

Optionally you can set limits by `instrument` or `data` range

    p <- p + ggcyto_par_set(limits = "instrument")
    p

## 7: labs_cyto

You can choose between `marker` and `channel` names (or `both` by default)

    p + labs_cyto("markers")

## 8: `ggcyto_par_set`

It 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.

## 9: `as.ggplot`

It 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"

## 10: ggcyto_layout

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)