ggplot {ggbio} | R Documentation

## 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

    ## S3 method for class 'Vector'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'Seqinfo'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'ExpressionSet'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'RsamtoolsFile'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'TxDbOREnsDb'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'BSgenome'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'matrix'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'character'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'SummarizedExperiment'
    ggplot(data, mapping = aes(),
                            assay.id = 1L, ..., environment = parent.frame())
    ## S3 method for class 'GAlignments'
    ggplot(data, mapping = aes(), ...,
                            environment = parent.frame())
    ## S3 method for class 'VCF'
    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.

### 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, <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.

### 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.

### Author(s)

Tengfei Yin

### See Also

`mold`, `ggbio`

### Examples

    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))

[Package ggbio version 1.34.0 Index]