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2.2 Switch coordinates and add layers

On this page

Verify the shared grammar before tuning linear composition. These objects replace only the coordinates on the chord teaching objects: no table edits or new alignments.

Prepare this page

Extract the input bundle first.
Preparation code — run once on this page
Code
# Optional appearance choices; none of these columns or scales is required input.
library(ggchord)
seq_palette <- setNames(
  c("#E64B35", "#233E4A", "#269B82", "#D59B17"),
  c("MT108731.1", "MT118296.1", "OQ646790.1", "OR222515.1")
)
gene_palette <- c("+" = "#CE1536", "-" = "#346B9D", "." = "#8291A6")

library(ggchord)
doc_background <- "#FFFCF5"
identity_palette <- c("#303B71", "#36537D", "#269B82", "#A5D52C", "#ECEB31")
identity_values <- c(0, 0.5, 0.8, 0.9, 1)
identity_breaks <- c(0, 50, 80, 90, 100)
doc_legend_theme <- theme(
  legend.title = element_text(size = 13, face = "bold"),
  legend.text = element_text(size = 11),
  legend.key.width = unit(9, "mm"), legend.key.height = unit(5, "mm"),
  legend.spacing.x = unit(9, "mm"), legend.spacing.y = unit(7, "mm"),
  legend.margin = margin(l = 4, r = 4, unit = "mm")
)
sequence_guide <- guide_ggchord_legend(nrow = 1, theme = doc_legend_theme)
strand_guide <- guide_ggchord_legend(
  nrow = 1, override.aes = list(strand = c("+", "-")), theme = doc_legend_theme
)
identity_guide <- guide_ggchord_colourbar(
  position = "left", direction = "vertical",
  theme = theme(
    legend.key.height = unit(75, "mm"), legend.key.width = unit(5, "mm"),
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11), legend.margin = margin(r = 7, unit = "mm")
  )
)
doc_theme <- theme_ggchord_publication(
  axis.text = element_text(size = 11),
  axis.text.check.overlap = TRUE
) +
  theme(
    legend.position = "bottom",
    legend.title = element_text(size = 13, face = "bold"),
    legend.text = element_text(size = 11),
    legend.key.width = unit(9, "mm"), legend.key.height = unit(5, "mm"),
    legend.spacing.x = unit(9, "mm"), legend.spacing.y = unit(7, "mm"),
    legend.box.spacing = unit(6, "mm"),
    plot.background = element_rect(fill = doc_background, colour = NA),
    panel.background = element_blank(),
    plot.title = element_text(face = "bold", size = 18),
    plot.subtitle = element_text(size = 10, colour = "#5C625A"),
    plot.margin = margin(14, 18, 14, 18)
  )
doc_axis <- function(coordinates = "relative") {
  scale_seq_position_continuous(
    coordinates = coordinates, n.breaks = 3, minor_breaks = NULL,
    labels = function(x) format(x, scientific = FALSE, big.mark = ",", trim = TRUE)
  )
}

library(ggchord)
library(dplyr)
library(purrr)
library(tidyr)
# Original repository records; extract the tutorial input bundle first.
show_sequences <- read_fasta_lengths(files = "examples/fasta/*.fna") |>
  select(accver, length)
show_annotations <- read_gff3(files = "examples/gff3/*.gff3", unstranded = "keep")
show_genes <- show_annotations |>
  filter(type == "CDS") |>
  select(accver, start, end, strand, anno)
show_alignments <- read_blast(files = "examples/blastn-standard/*.tsv")
# At most three substantial observed HSPs per pair; no coordinates are invented.
show_links <- show_alignments |>
  filter(length >= 900, pident >= 75) |>
  group_by(qaccver, saccver) |>
  slice_max(length, n = 3, with_ties = FALSE) |>
  ungroup()
# Select up to four source CDS per sequence, spread across annotation order.
# No feature is moved or invented; keep complete source tables separately.
show_features <- show_genes |>
  filter(end - start + 1 >= 500) |>
  group_by(accver) |>
  arrange(start, .by_group = TRUE) |>
  mutate(display_bin = ntile(row_number(), 4), informative = !is.na(anno) & anno != "hypothetical protein") |>
  group_by(accver, display_bin) |>
  arrange(desc(informative), desc(end - start), .by_group = TRUE) |>
  slice_head(n = 1) |>
  ungroup() |>
  select(accver, start, end, strand, anno)
# Also retain well-separated negative-strand CDS, if not already represented.
show_reverse <- show_genes |>
  filter(strand == "-", end - start + 1 >= 500) |>
  group_by(accver) |>
  arrange(start, .by_group = TRUE) |>
  slice(c(1, n())) |>
  ungroup()
show_features <- bind_rows(show_features, show_reverse) |>
  distinct(accver, start, end, strand, .keep_all = TRUE) |>
  arrange(accver, start)
show_labels <- show_features
show_optimization <- optimize_ggchord_layout(
  show_sequences, show_links,
  optimize = "orientation", weight = "length"
)
Preparation code — run once on this page
Code
p_learn_seq <- ggchord(seq_data = show_sequences) +
  geom_seq() +
  geom_seq_label(size = 4) +
  scale_seq_colour_manual(
    values = seq_palette, name = "Sequence",
    guide = sequence_guide
  ) +
  scale_seq_position_continuous(breaks = c(0, 20000, 40000), minor_breaks = NULL) +
  coord_chord() +
  doc_theme
p_learn_seq

p_learn_links <- p_learn_seq +
  geom_link_ribbon(
    aes(ribbon_fill = pident),
    data = show_links,
    adjacent_only = FALSE, link_avoid = "smooth"
  ) +
  scale_ribbon_fill_gradientn(
    colours = identity_palette, values = identity_values, limits = c(0, 100),
    breaks = identity_breaks, name = "Identity (%)", guide = identity_guide
  )
p_learn_links

p_learn_genes <- p_learn_links +
  geom_gene(data = show_features, position = position_strand(0.08)) +
  scale_gene_fill_manual(values = gene_palette, breaks = c("+", "-"), name = "Strand") +
  guides(gene_fill = strand_guide)
p_learn_genes

p_learn_labels <- p_learn_genes +
  geom_gene_label_repel(
    data = show_features, label_layout = "arc", label_wrap = 24,
    label_max_lines = 3, position = position_strand(0.08), size = 3
  )
p_learn_labels

Replace coord

Code
# Reuse exactly the same data and layers; replace only the coordinate system.
p_linear_seq <- p_learn_seq + coord_collinear()
p_linear_links <- p_learn_links + coord_collinear()
p_linear_labels <- p_learn_labels + coord_collinear(ratio = 0.65)
p_linear_labels

Read backbone lengths

The same backbones with linear coordinates
The same backbones with linear coordinates · Click to enlarge

Rows share one bp scale. Different sequence lengths remain different rather than being stretched to equal width.

The same observed links with linear coordinates
The same observed links with linear coordinates · Click to enlarge

The same observed HSPs and cross-row connections remain. This also exposes the limitations of default long routes, addressed on the next page.

Add strand-separated genes and labels

The same genes and labels with linear coordinates
The same genes and labels with linear coordinates · Click to enlarge

Arrows and labels use the same selected table. Display flips do not rewrite source strands. Continue to 2.3 Route cross-row links.