| ggplot {ggbio} | R Documentation |
Usage is similar to ggplot function in ggplot2 package, but support
more objects defined in Bioconductor, or even for object like matrix,
which intitialize a ggbio object. It can be used to declare
the input data frame for a graphic and to specify the set of plot
aesthetics intended to be common throughout all subsequent layers
unless specifically overridden. And more than that, it store the
original data object before coercion into the returned object.
Please check the behavior for mold method to see how
each object coerced into data.frame.
## S4 method for signature 'GRanges' ggplot(data, ...) ## S4 method for signature 'GRangesList' ggplot(data, ...) ## S4 method for signature 'IRanges' ggplot(data, ...) ## S4 method for signature 'Seqinfo' ggplot(data, ...) ## S4 method for signature 'matrix' ggplot(data, ...) ## S4 method for signature 'Views' ggplot(data, ...) ## S4 method for signature 'ExpressionSet' ggplot(data, ...) ## S4 method for signature 'SummarizedExperiment' ggplot(data, assay.id = 1, ...) ## S4 method for signature 'VCF' ggplot(data, ...) ## S4 method for signature 'GappedAlignments' ggplot(data, ...) ## S4 method for signature 'BamFile' ggplot(data, ...) ## S4 method for signature 'character' ggplot(data, ...) ## S4 method for signature 'TranscriptDb' ggplot(data, ...) ## S4 method for signature 'BSgenome' ggplot(data, ...) ## S4 method for signature 'Rle' ggplot(data, ...) ## S4 method for signature 'RleList' ggplot(data, ...)
data |
original data object. |
... |
other arguments passed to specific methods. |
assay.id |
index of assay you are using when multiple assays exist. |
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, <other aesthetics>))
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.
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.
Tengfei Yin
mold, ggbio
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)$pair)
ggplot(grl) + geom_alignment()
ggplot(grl) + geom_rect()
ggplot(grl) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))
## IRanges
ir <- ranges(gr)
ggplot(ir) + geom_rect()
ggplot(ir) + layout_circle(geom = "rect")
## Seqinfo
seqlengths(gr) <- c(400, 500, 420)
ggplot(seqinfo(gr)) + geom_point(aes(x = midpoint, y = seqlengths))
## matrix
mx <- matrix(1:12, nrow = 3)
ggplot(mx, aes(x = x, y = y)) + geom_raster(aes(fill = value))
## row is the factor
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = value))
colnames(mx)
## NULL
colnames(mx) <- letters[1:ncol(mx)]
mx
## a b c d
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12
## has extra 'colnames'
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = colnames))
rownames(mx)
## NULL
rownames(mx) <- LETTERS[1:nrow(mx)]
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = rownames))
## please check autoplot, matrix for more control
## Views
## ExpressionSet
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 500 features, 26 samples
## element names: exprs, se.exprs
## protocolData: none
## phenoData
## sampleNames: A B ... Z (26 total)
## varLabels: sex type score
## varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: hgu95av2
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = value))
## please check autoplot,matrix method which gives you more control
head(ggplot(eset)$data)
## x y value row col colnames rownames sex type score
## A 1 1 177.1 1 1 A 31372_at Female Control 0.75
## B 2 1 171.2 1 2 B 31372_at Male Case 0.40
## C 3 1 277.2 1 3 C 31372_at Male Control 0.73
## D 4 1 190.2 1 4 D 31372_at Male Case 0.42
## E 5 1 138.4 1 5 E 31372_at Female Case 0.93
## F 6 1 174.0 1 6 F 31372_at Male Control 0.22
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
## SimpleRleList of length 2
## [[1]]
## numeric-Rle of length 10000 with 829 runs
## Lengths: 729 1 208 1 1599 1 ... 1 1 1 5 1 4512
## Values : 0 1 0 1 0 1 ... 1 0 1 0 1 0
##
## [[2]]
## numeric-Rle of length 10000 with 829 runs
## Lengths: 729 1 208 1 1599 1 ... 1 1 1 5 1 4512
## Values : 0 2 0 2 0 2 ... 2 0 2 0 2 0
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~.)
## SummarizedExperiments
library(GenomicRanges)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowData <- 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),
rowData=rowData, colData=colData)
ggplot(sset) + geom_raster(aes(x = x, y = y , fill = value))
## Assay index: 1 used
