添加数据轨道

本页目录

本页准备
从实际 DNA 计算数据轨道
绘制 GC 比例
同一 ID 共享同一条轨道
检查裁剪和数值范围
叠加柱、滑动均值和注释带
完成后检查
保存本页结果

从真实 DNA 计算 GC 比例，再逐步叠加柱、滑动均值和注释带。读者应能说清每个轨道的单位、窗口与基线。

本页准备

在新的 R 会话中先运行下方代码，再按页面顺序继续。配色和排版是可选的显示设置，输入只选择需要的列。

准备代码 · 在本页运行一次

代码library(ggchord)
feature_palette <- c(
  CDS = "#993366", promoter = "#31849B", rep_origin = "#F1E51D",
  primer_bind = "#A020F0", misc_feature = "#A6ACB3", protein_bind = "#31849B"
)

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(dplyr)
data(plasmid_example_pUC19)
plasmid_seq <- plasmid_example_pUC19 |>
  select(accver, length)
feature_source <- find_common_features(plasmid_example_pUC19)
features <- feature_source |>
  select(accver, start, end, strand, type, anno)

从实际 DNA 计算数据轨道

本例读取随包 pUC19，每 100 bp 计算一次 GC 比例；末尾不足 100 bp 的窗口按实际碱基数计算。

代码library(dplyr)
data(plasmid_example_pUC19)
track_seq <- plasmid_example_pUC19 |>
  select(accver, length)
starts <- seq.int(1L, track_seq$length, by = 100L)
gc_fraction <- function(sequence) {
  bases <- strsplit(sequence, "")[[1]]
  mean(bases %in% c("G", "C"))
}
bins <- tibble(
  accver = track_seq$accver,
  start = starts,
  end = pmin(starts + 99L, track_seq$length)
) |>
  rowwise() |>
  mutate(gc = gc_fraction(substr(plasmid_example_pUC19$sequence, start, end))) |>
  ungroup()
head(bins)

bins 的每一行是一段实际序列窗口；先检查起止位置及 gc，再把数值映射到轨道高度与颜色。

绘制 GC 比例

代码p_tracks <- ggchord(seq_data = track_seq) +
  geom_seq() +
  geom_seq_center_label() +
  geom_track_bar(aes(track_value = gc, track_fill = gc),
    data = bins,
    position = position_track("gc", width = unit(10, "mm"))
  ) +
  scale_track_value_continuous("gc", limits = c(0, 1), unit = "GC fraction") +
  scale_track_fill_gradientn("gc", colours = c("#E4F3EA", "#65B79B", "#27775C")) +
  coord_circular() +
  doc_theme
p_tracks

值域为 0–1，单位是比例。换成其他测量值时，要声明对应单位和有效范围。 position_track() 决定放置与物理宽度，scale_track_value_continuous() 决定数值映射，二者不能互相替代。

同名轨道上的各层共享数值域，bar 的基线也参与范围计算；它们必须声明一致的宽度和放置方式。颜色标度按轨道 ID 独立设置。tile 表示区间类别，line 连接观测位置， bar 表示相对基线的区间值。不要把相同数值域误认为必须位于同一条轨道。

检查裁剪和数值范围

export_ggchord_layout() 的 track_registry 与 track_diagnostics 记录值域、物理尺寸、原生位移及裁剪情况。被裁剪或省略的行不等于测量值为零。局部视图使用 focus_ggchord_track() 并共享相同窗口。

叠加柱、滑动均值和注释带

本页准备代码已创建实际注释 features，完成上面的 GC 计算后继续：

代码line_bins <- bins |>
  transmute(
    accver,
    pos = floor((start + end) / 2),
    gc_mean = as.numeric(stats::filter(gc, rep(1 / 3, 3), sides = 2))
  ) |>
  filter(is.finite(gc_mean))
categories <- features |>
  select(accver, start, end, type)
p_multitrack <- ggchord(seq_data = track_seq) +
  geom_seq() +
  geom_seq_center_label() +
  geom_track_bar(aes(track_value = gc, track_fill = gc),
    data = bins,
    position = position_track("gc", width = unit(10, "mm"))
  ) +
  geom_track_line(aes(track_value = gc_mean),
    data = line_bins,
    position = position_track("gc", width = unit(10, "mm")), colour = "#233E4A"
  ) +
  scale_track_value_continuous("gc", limits = c(0, 1), unit = "GC fraction") +
  scale_track_fill_gradientn("gc", colours = c("#E4F3EA", "#65B79B", "#27775C")) +
  geom_track_tile(aes(track_fill = type),
    data = categories,
    position = position_track("features", order = 2, width = unit(2, "mm"))
  ) +
  scale_track_fill_manual("features", values = feature_palette) +
  coord_circular() +
  doc_theme +
  theme(legend.box = "vertical", legend.box.just = "left")
focused_bins <- focus_ggchord_track(bins, track_seq,
  loci = tibble(accver = track_seq$accver, start = 500, end = 1500), type = "interval"
)
p_multitrack

柱和线都来自同一组实际 GC 值，线使用三个窗口的滑动均值；两端没有完整窗口时省略，不填造观测值。折线位置是整数窗口中点。注释类型带有独立 ID 和配色。轨道放在圆外并按顺序排列；小半径下过宽的内侧带可能折叠，此时会明确报错。

完成后检查

bins 的 gc 在 0–1 之间，每行对应一个实际窗口；最后窗口可短于 100 bp。连续色表达数值，类别带只表达注释类型，不能用它比较 GC 大小。

继续：预览和保存图形 →

保存本页结果

在工作目录中保存刚刚创建的图；预览时使用相同宽高。

代码ggsave("pUC19-tracks.png", p_multitrack, width = 9, height = 8, units = "in", dpi = 144)