geom_variant: Draw place of mutation

In thackl/gggenomes: A Grammar of Graphics for Comparative Genomics

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(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

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.

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()  
 

thackl/gggenomes documentation built on March 10, 2024, 7:26 a.m.