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1.4 方向、曲率与避障
这些调整属于不同职责：geom控制逐序列几何，Position控制局部放置，coord控制整体视图，scale与guide负责颜色和图例。
本页准备
先解压教学输入。准备代码 · 在本页运行一次
代码
# 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"
)先检查方向证据
show_optimization 只优化显示方向，保留序列顺序；按真实比对长度加权。检查其seq_orientation与score_before/score_after，不能把显示翻转解释为新的生物学倒位结论。自动优化是启发式，不保证全局最优。
将功能组合成可读的图
代码
show_curvature <- c(
"MT108731.1" = 0, "MT118296.1" = 0.85,
"OQ646790.1" = 1.1, "OR222515.1" = -0.4
)
show_radius <- c(
"MT108731.1" = 1, "MT118296.1" = 1.05,
"OQ646790.1" = 0.95, "OR222515.1" = 1.08
)
p_show_chord <- ggchord(show_sequences, show_links, show_features) +
geom_link_ribbon(aes(ribbon_fill = pident), link_avoid = "smooth", alpha = 0.58, linewidth = 0.18) +
geom_seq(
seq_orientation = show_optimization$seq_orientation,
seq_curvature = show_curvature, seq_radius = show_radius,
seq_gap = 0.06, linewidth = 1.1
) +
geom_gene(position = position_strand(0.07), width = 0.035) +
geom_gene_label_repel(
data = show_labels, label_layout = "radial", label_wrap = 24,
size = 3.1, label_max_lines = 3, position = position_strand(0.07)
) +
geom_seq_label(size = 4, seq_label_radius = 0.84, seq_label_hjust = 0.5) +
scale_seq_colour_manual(
values = seq_palette, name = "Sequence",
guide = sequence_guide
) +
scale_gene_fill_manual(values = gene_palette, breaks = c("+", "-"), name = "Strand") +
scale_ribbon_fill_gradientn(
colours = identity_palette, values = identity_values,
limits = c(0, 100), breaks = identity_breaks, name = "Identity (%)", guide = identity_guide
) +
guides(gene_fill = strand_guide) +
scale_seq_position_continuous(breaks = c(0, 20000, 40000), minor_breaks = NULL) +
coord_chord(rotation = 50) +
doc_theme +
labs(
title = "Four genomes, one connected view",
subtitle = "Signed curvature and radius · selected strand-aware CDS · local ribbon clearance"
)
p_show_chord
命名向量逐序列匹配：曲率0是直线，正负值向相反方向弯曲，绝对值影响弓高；半径改变序列位置。上图同时演示自动方向、不同半径、零/正/负曲率和局部避障。参数不修改源坐标。
避障方式为何看起来不同
link_avoid = "uniform" 在整个端区间留统一空隙；"smooth" 只在特征附近平滑退让，形成局部缺口；"none" 不做特征避障。link_gap = NULL 才启用自动规则，给出固定数值会覆盖自动间距。避障检测箭头/特征几何，不避让文字，也不会自动绕过跨行连接途经的整条中间序列。
颜色与导出
identity采用独立左侧长色条，序列和链图例在底部。链图例显式传递方向覆盖，箭头符号与图中几何一致。样式专题解释图例职责；按12×10 in导出，最后到1.7 首页综合示例核对数据与结果。