geom_variant {gggenomes}

Draw place of mutation

Description

geom_variant allows the user to draw points at locations where a mutation has occured. Data on SNPs, Insertions, Deletions and more (often stored in a variant call format (VCF)) can easily be visualized this way.

Usage

geom_variant(
  mapping = NULL,
  data = feats(),
  stat = "identity",
  position = "identity",
  geom = "variant",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  offset = 0,
  ...
)

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

Data from the first feats track is used for this function by default. When several feats tracks are present within the gggenomes track system, make sure that the wanted data is used by calling ⁠data = feats(*df*)⁠ within the geom_variant function.

stat

Describes what statistical transformation is used for this layer. By default it uses "identity", indicating no statistical transformation.

position

Describes how the position of different plotted features are adjusted. By default it uses "identity", but different position adjustments, such as position_variant(), ggplot2' "jitter" or "pile" can be used as well.

geom

Describes what geom is called upon by the function for plotting. By default the function uses "variant", a modified geom_point object. For larger sequences with abundant mutations/variations, it is recommended to use "ticks" (a modified geom_point object with different default shape and alpha, which plots the points as small "ticks"), but in theory any other ggplot2 geom can be called here as well.

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().

offset

Numeric value describing how far the points will be drawn from the base/sequence. By default it is set on offset = 0.

...

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.

Details

geom_variant uses ggplot2::geom_point under the hood. As a result, different aesthetics such as alpha, size, color, etc. can be called upon to modify the data visualization.

#' the function gggenomes::read_feats is able to read VCF files and converts them into a format that is applicable within the gggenomes' track system. Keep in mind: The function uses data from the feats' track.

Value

A ggplot2 layer with variant information.

Examples

# Creation of example data.
# (Note: These are mere examples and do not fully resemble data from VCF-files)
## Small example data set
f1 <- tibble::tibble(
  seq_id = c(rep(c("A", "B"), 4)), start = c(1, 10, 15, 15, 30, 40, 40, 50),
  end = c(2, 11, 20, 16, 31, 41, 50, 51), length = end - start,
  type = c("SNP", "SNP", "Insertion", "Deletion", "Deletion", "SNP", "Insertion", "SNP"),
  ALT = c("A", "T", "CAT", ".", ".", "G", "GG", "G"),
  REF = c("C", "G", "C", "A", "A", "C", "G", "T")
)
s1 <- tibble::tibble(seq_id = c("A", "B"), start = c(0, 0), end = c(55, 55), length = end - start)

## larger example data set
f2 <- tibble::tibble(
  seq_id = c(rep("A", 667)),
  start = c(
    seq(from = 1, to = 500, by = 2),
    seq(from = 500, to = 2500, by = 50),
    seq(from = 2500, to = 4000, by = 4)
  ),
  end = start + 1, length = end - start,
  type = c(
    rep("SNP", 100),
    rep("Deletion", 20),
    rep("SNP", 180),
    rep("Deletion", 67),
    rep("SNP", 100),
    rep("Insertion", 50),
    rep("SNP", 150)
  ),
  ALT = c(
    sample(x = c("A", "C", "G", "T"), size = 100, replace = TRUE),
    rep(".", 20), sample(x = c("A", "C", "G", "T"), size = 180, replace = TRUE),
    rep(".", 67), sample(x = c("A", "C", "G", "T"), size = 100, replace = TRUE),
    sample(x = c(
      "AA", "AC", "AG", "AT", "CA", "CC", "CG", "CT", "GA", "GC",
      "GG", "GT", "TA", "TC", "TG", "TT"
    ), size = 50, replace = TRUE),
    sample(x = c("A", "C", "G", "T"), size = 150, replace = TRUE)
  )
)

# Basic example plot with geom_variant
gggenomes(seqs = s1, feats = f1) +
  geom_seq() +
  geom_variant()

# Improving plot elements, by changing shape and adding bin_label
gggenomes(seqs = s1, feats = f1) +
  geom_seq() +
  geom_variant(aes(shape = type), offset = -0.1) +
  scale_shape_variant() +
  geom_bin_label()

# Positional adjustment based on type of mutation: position_variant
gggenomes(seqs = s1, feats = f1) +
  geom_seq() +
  geom_variant(
    aes(shape = type),
    position = position_variant(offset = c(Insertion = -0.2, Deletion = -0.2, SNP = 0))
  ) +
  scale_shape_variant() +
  geom_bin_label()

# Plotting larger example data set with Changing default geom to
# `geom = "ticks"` using positional adjustment based on type (`position_variant`)
gggenomes(feats = f2) +
  geom_variant(aes(color = type), geom = "ticks", alpha = 0.4, position = position_variant()) +
  geom_bin_label()

# Changing geom to `"text"`, to plot ALT nucleotides
gggenomes(seqs = s1, feats = f1) +
  geom_seq() +
  geom_variant(aes(shape = type), offset = -0.1) +
  scale_shape_variant() +
  geom_variant(aes(label = ALT), geom = "text", offset = -0.25) +
  geom_bin_label()