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Plot genomic ranges

Source: R/geom_range.R, R/geom_half_range.R
geom_range.Rd

geom_range() and geom_half_range() draw tiles that are designed to represent range-based genomic features, such as exons. In combination with geom_intron(), these geoms form the core components for visualizing transcript structures.

Usage

geom_range(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  vjust = NULL,
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_half_range(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  range.orientation = "bottom",
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer. When using a geom_*() function to construct a layer, the stat argument can be used the override the default coupling between geoms and stats. The stat argument accepts the following:

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.

  • When constructing a layer using a stat_*() function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.

  • Inversely, when constructing a layer using a geom_*() function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.

  • The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

vjust

A numeric vector specifying vertical justification. If specified, overrides the just setting.

linejoin

Line join style (round, mitre, bevel).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

range.orientation

character() one of "top" or "bottom", specifying where the half ranges will be plotted with respect to each transcript (y).

Value

the return value of a geom_* function is not intended to be directly handled by users. Therefore, geom_* functions should never be executed in isolation, rather used in combination with a ggplot2::ggplot() call.

Details

geom_range() and geom_half_range() require the following aes(); xstart, xend and y (e.g. transcript name). geom_half_range() takes advantage of the vertical symmetry of transcript annotation by plotting only half of a range on the top or bottom of a transcript structure. This can be useful for comparing between two transcripts or free up plotting space for other transcript annotations (e.g. geom_junction()).

Examples


library(magrittr)
library(ggplot2)

# to illustrate the package's functionality
# ggtranscript includes example transcript annotation
sod1_annotation %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type        gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(type == "exon")
sod1_exons %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       31659666 31659784 +      exon  SOD1      SOD1-202       
#> 2 21       31663790 31663886 +      exon  SOD1      SOD1-202       
#> 3 21       31666449 31666518 +      exon  SOD1      SOD1-202       
#> 4 21       31667258 31667375 +      exon  SOD1      SOD1-202       
#> 5 21       31668471 31668931 +      exon  SOD1      SOD1-202       
#> 6 21       31659693 31659841 +      exon  SOD1      SOD1-204       
#> # ℹ 1 more variable: transcript_biotype <chr>

base <- sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    ))

# geom_range() is designed to visualise range-based annotation such as exons
base + geom_range()


# geom_half_range() allows users to plot half ranges
# on the top or bottom of the transcript
base + geom_half_range()


# where the half ranges are plotted can be adjusted using range.orientation
base + geom_half_range(range.orientation = "top")


# as a ggplot2 extension, ggtranscript geoms inherit the
# the functionality from the parameters and aesthetics in ggplot2
base + geom_range(
    aes(fill = transcript_name),
    linewidth = 1
)


# together, geom_range() and geom_intron() are designed to visualize
# the core components of transcript annotation
base + geom_range(
    aes(fill = transcript_biotype)
) +
    geom_intron(
        data = to_intron(sod1_exons, "transcript_name")
    )


# for protein coding transcripts
# geom_range() be useful for visualizing UTRs that lie outside of the CDS
sod1_exons_prot_coding <- sod1_exons %>%
    dplyr::filter(transcript_biotype == "protein_coding")

# extract cds
sod1_cds <- sod1_annotation %>%
    dplyr::filter(type == "CDS")

sod1_exons_prot_coding %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        fill = "white",
        height = 0.25
    ) +
    geom_range(
        data = sod1_cds
    ) +
    geom_intron(
        data = to_intron(sod1_exons_prot_coding, "transcript_name")
    )


# geom_half_range() can be useful for comparing between two transcripts
# enabling visualization of one transcript on the top, other on the bottom
sod1_201_exons <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_201_cds <- sod1_cds %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_202_exons <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-202")
sod1_202_cds <- sod1_cds %>% dplyr::filter(transcript_name == "SOD1-202")

sod1_201_plot <- sod1_201_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = "SOD1-201/202"
    )) +
    geom_half_range(
        fill = "white",
        height = 0.125
    ) +
    geom_half_range(
        data = sod1_201_cds
    ) +
    geom_intron(
        data = to_intron(sod1_201_exons, "transcript_name")
    )

sod1_201_plot


sod1_201_202_plot <- sod1_201_plot +
    geom_half_range(
        data = sod1_202_exons,
        range.orientation = "top",
        fill = "white",
        height = 0.125
    ) +
    geom_half_range(
        data = sod1_202_cds,
        range.orientation = "top",
        fill = "purple"
    ) +
    geom_intron(
        data = to_intron(sod1_202_exons, "transcript_name")
    )

sod1_201_202_plot


# leveraging existing ggplot2 functionality via e.g. coord_cartesian()
# can be useful to zoom in on areas of interest
sod1_201_202_plot + coord_cartesian(xlim = c(31659500, 31660000))