ggplot: ggplot methods

Description

These methods extend ggplot to support several types of Bioconductor objects, as well as some base types like matrix. They return a ggbio object, which stores the original data object. Please check the corresponding method for mold to see how an object is coerced into a data.frame.

Usage

"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), assay.id = 1L, ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())
"ggplot"(data, mapping = aes(), ..., environment = parent.frame())

Arguments

data

original data object.

mapping

the aesthetic mapping.

...

other arguments passed to specific methods.

environment

fall-back environment for evaluation of aesthetic symbols

assay.id

index of assay you are using when multiple assays exist.

Value

a return ggbio object, which is a subclass of ggplot defined in ggplot2 package, but that's more, a '.data' list entry is stored with the returned object.

Details

The biggest difference for objects returned by ggplot in ggbio from ggplot2, is we always keep the original data copy, this is useful because in ggbio, our starting point is not always data.frame, many special statistical transformation is computed upon original data objects instead of coerced data.frame. This is a hack to follow ggplot2's API while allow our own defined components to trace back to original data copy and do the transformation. For objects supported by mold we transform them to data.frame stored along the original data set, for objects which not supported by mold method, we only store the original copy for ggbio specific graphics.

ggplot() is typically used to construct a plot incrementally, using the + operator to add layers to the existing ggplot object. This is advantageous in that the code is explicit about which layers are added and the order in which they are added. For complex graphics with multiple layers, initialization with ggplot is recommended. You can always call qplot in package ggplot2 or autoplot in ggbio for convenient usage.

There are three common ways to invoke ggplot:

ggplot(df, aes(x, y, ))
ggplot(df)
ggplot()

The first method is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used to add a layer using data from another data frame. The second method specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly as layers are added, but the aesthetics may vary from one layer to another. The third method initializes a skeleton ggplot object which is fleshed out as layers are added. This method is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

The examples below illustrate how these methods of invoking ggplot can be used in constructing a graphic.

Examples

Run this code

set.seed(1)
N <- 100
library(GenomicRanges)
## GRanges
gr <- GRanges(seqnames =
              sample(c("chr1", "chr2", "chr3"),
                     size = N, replace = TRUE),
              IRanges(
                      start = sample(1:300, size = N, replace = TRUE),
                      width = sample(70:75, size = N,replace = TRUE)),
              strand = sample(c("+", "-", "*"), size = N,
                replace = TRUE),
              value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
              sample = sample(c("Normal", "Tumor"),
                size = N, replace = TRUE),
              pair = sample(letters, size = N,
                replace = TRUE))

## automatically facetting and assign y
## this must mean geom_rect support GRanges object
ggplot(gr) + geom_rect()
ggplot(gr) + geom_alignment()
ggplot() + geom_alignment(gr)


## use pure ggplot2's geom_rect, no auto facet
ggplot(gr) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
                               xmax = end, ymax = score + 1))

## GRangesList
grl <- split(gr, values(gr)$pair)
ggplot(grl) + geom_alignment()
ggplot(grl) + geom_rect()
ggplot(grl) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
                               xmax = end, ymax = score + 1))


## IRanges
ir <- ranges(gr)
ggplot(ir) + geom_rect()
ggplot(ir) + layout_circle(geom = "rect")

## Seqinfo
seqlengths(gr) <- c(400, 500, 420)
ggplot(seqinfo(gr)) + geom_point(aes(x = midpoint, y = seqlengths))

## matrix
mx <- matrix(1:12, nrow = 3)
ggplot(mx, aes(x = x, y = y)) + geom_raster(aes(fill = value))
## row is the factor
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = value))
colnames(mx)
colnames(mx) <- letters[1:ncol(mx)]
mx
## has extra 'colnames'
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = colnames))
rownames(mx)
rownames(mx) <- LETTERS[1:nrow(mx)]
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = rownames))
## please check autoplot, matrix for more control

##  Views










## ExpressionSet
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]

ggplot(eset) + geom_tile(aes(x = x, y = y, fill = value))
## please check autoplot,matrix method which gives you more control
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = sex))
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = type))

## Rle
library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
            seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
ggplot(xRle) + geom_tile(aes(x = x, y = y, fill = value))

## RleList
xRleList <- RleList(xRle, 2L * xRle)
xRleList
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
names(xRleList) <- c("a" ,"b")
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)


## RangedSummarizedExperiment
library(SummarizedExperiment)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                   IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                   strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                     row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts,
                                               counts2 = counts2),
                             rowRanges=rowRanges, colData=colData)
ggplot(sset) + geom_raster(aes(x = x, y = y , fill = value))