SVG Plots

14 functions for publication-quality SVG and PNG plots. All return an svg value that can be saved, displayed inline, or embedded in HTML reports.

Common Options

Option	Type	Default	Description
width	int	800	Plot width in pixels
height	int	600	Plot height in pixels
title	string	""	Plot title
x_label	string	""	X-axis label
y_label	string	""	Y-axis label
color	string	"#8b5cf6"	Primary color (hex or named)
palette	string	"viridis"	Color palette name
theme	string	"dark"	"dark" or "light"

plot

Generic plot dispatcher. Accepts data and a type option to select the chart kind.

plot(data, opts?) -> svg

plot([1, 4, 9, 16, 25], {type: "line", title: "Squares"})
plot({"A": 30, "T": 25, "C": 28, "G": 27}, {type: "bar"})

scatter

Scatter plot with optional color and size mapping.

scatter(data, opts?) -> svg

let expr = csv("expression.csv")
scatter(expr, {
  x: "control_mean",
  y: "treated_mean",
  color: "significant",
  title: "Control vs Treated Expression",
  x_label: "Control (TPM)",
  y_label: "Treated (TPM)"
}) |> save_svg("scatter.svg")

Output

heatmap

Heatmap from matrix or table data. Supports clustering and dendrograms.

heatmap(mat, opts?) -> svg

let expr_matrix = matrix([
  [10.2, 8.5, 12.1, 9.8],
  [5.3, 4.8, 5.9, 5.1],
  [20.1, 18.7, 22.3, 19.5]
])
heatmap(expr_matrix, {
  row_labels: ["BRCA1", "TP53", "EGFR"],
  col_labels: ["S1", "S2", "S3", "S4"],
  palette: "viridis",
  cluster_rows: true,
  cluster_cols: true,
  title: "Gene Expression Heatmap"
}) |> save_svg("heatmap.svg")

Output

histogram

histogram(data, opts?) -> svg     # continuous data, binned

histogram(quality_scores, {bins: 30, title: "Quality Score Distribution"})

Output

violin

violin(groups, opts?) -> svg

let groups = {
  "WT": [30, 32, 28, 35, 31],
  "KO": [22, 25, 20, 18, 24],
  "OE": [40, 38, 42, 45, 41]
}
violin(groups, {title: "Expression Distribution"})

volcano / ma_plot

Standard differential expression visualizations.

volcano(tbl, opts?) -> svg
ma_plot(tbl, opts?) -> svg

let de = csv("deseq2_results.csv")
volcano(de, {
  x: "log2FoldChange",
  y: "padj",
  fc_threshold: 1.0,
  p_threshold: 0.05,
  label_top: 10,
  title: "Differential Expression"
}) |> save_svg("volcano.svg")

Output

ma_plot(de, {
  x: "baseMean",
  y: "log2FoldChange",
  color: "padj",
  title: "MA Plot"
})

save_svg

save_svg(svg, path) -> string

let p = scatter(data, {title: "My Plot"})
save_svg(p, "plot.svg")

Additional Plot Types

Function	Description
density(data, opts?)	Kernel density estimation plot
genome_track(data, opts?)	Genome annotation track (genes, exons, introns)