Bio Plots

manhattan

Manhattan plot for genome-wide association studies. Alternating chromosome colors with significance threshold line.

manhattan(tbl, opts?) -> svg

let gwas = read_csv("data/expression.csv")
manhattan(gwas, {
  chrom: "CHR", pos: "BP", pvalue: "P",
  label_top: 5,
  title: "GWAS Manhattan Plot"
}) |> save_svg("manhattan.svg")

qq_plot

Quantile-quantile plot for p-value distribution assessment. Includes genomic inflation factor (lambda).

qq_plot(pvalues, opts?) -> svg

let pvals = gwas |> select("P") |> to_records |> map(|r| r.P)
qq_plot(pvals, {title: "QQ Plot", show_lambda: true})

ideogram

Chromosome ideogram with banding patterns and highlighted regions.

ideogram(chromosome_bands, opts?) -> svg

ideogram("hg38", [
  {chrom: "chr17", start: 43044295, end: 43170245, label: "BRCA1", color: "red"},
  {chrom: "chr13", start: 32315474, end: 32400266, label: "BRCA2", color: "blue"}
])

circos

Circular genome plot with multiple data tracks (links, scatter, histogram, heatmap).

circos(tracks, opts?) -> svg

circos([
  {type: "ideogram", genome: "hg38"},
  {type: "scatter", data: gwas_hits, r_inner: 0.7, r_outer: 0.9},
  {type: "links", data: translocations, color: "red"}
], {title: "Structural Variants"})

hic_map

Hi-C contact map visualization. Renders chromatin interaction frequencies as a triangular or square heatmap.

hic_map(data, opts?) -> svg

hic_map(contact_matrix, {resolution: "10kb", title: "Hi-C chr1"})

oncoprint

Mutation landscape visualization (genes x samples).

oncoprint(mutations, opts?) -> svg

let muts = table({
  gene: ["TP53", "TP53", "BRCA1", "EGFR"],
  sample: ["S01", "S02", "S01", "S03"],
  type: ["missense", "nonsense", "frameshift", "missense"]
})
oncoprint(muts, {
  gene: "gene",
  sample: "sample"
})

rainfall

Mutation rainfall plot showing inter-mutation distances along the genome. Useful for identifying localized hypermutation (kataegis).

rainfall(data, opts?) -> svg

rainfall(variants, {chrom: "chr", pos: "position", title: "Mutation Rainfall"})

cnv_plot

Copy number variation plot across the genome. Displays log2 ratios with gain/loss coloring and segmentation lines.

cnv_plot(data, opts?) -> svg

cnv_plot(cnv_data, {chrom: "chr", start: "start", end: "end", ratio: "log2ratio", title: "CNV Profile"})

lollipop

Protein mutation lollipop plot. Shows mutations along a protein domain diagram with frequency stems.

lollipop(data, opts?) -> svg

lollipop(mutations, {position: "aa_pos", label: "mutation", height: "count", length: 393, title: "TP53 Mutations"})

violin

Violin plot showing distribution shape. Data is a record of {group_name: [values]} or a table.

violin(data, opts?) -> svg

violin({"WT": [30,32,28,35], "KO": [22,25,20,18]}, {title: "Expression"})

density

Kernel density estimation plot. Smooths a distribution of values into a continuous curve.

density(data, opts?) -> svg

density(quality_scores, {title: "Quality Distribution", bandwidth: 0.5})

kaplan_meier

Survival analysis plot with step function curves and optional confidence intervals. Supports group comparison.

kaplan_meier(data, opts?) -> svg

kaplan_meier(survival_data, {time: "months", event: "status", group: "treatment", title: "Overall Survival"})

forest_plot

Meta-analysis forest plot with effect sizes and confidence intervals. Shows individual study estimates and pooled summary.

forest_plot(data, opts?) -> svg

forest_plot(studies, {effect: "OR", ci_low: "lower", ci_high: "upper", title: "Meta-Analysis"})

roc_curve

ROC curve with AUC annotation. Visualizes classifier performance with true/false positive rate tradeoff.

roc_curve(data, opts?) -> svg

roc_curve(predictions, {fpr: "fpr", tpr: "tpr", auc: 0.85, title: "Classifier Performance"})

pca_plot

PCA scatter plot with group coloring and variance explained annotations on each axis.

pca_plot(data, opts?) -> svg

pca_plot(pca_result, {group: "cell_type", title: "PCA of Samples"})

sashimi

Render splice junction counts, optionally with a coverage table.

sashimi(junctions, opts?) -> svg
sashimi({coverage: coverage_table, junctions: junction_table}, opts?) -> svg

let junctions = table([
  {start: 43045600, end: 43049100, count: 42},
  {start: 43045600, end: 43051000, count: 17}
])

sashimi(junctions, {
  title: "BRCA1 Splicing"
})

clustered_heatmap

Heatmap with hierarchical clustering on rows, columns, or both. Adds dendrograms and reorders axes by similarity. Works with any numeric matrix — especially useful for expression data.

clustered_heatmap(matrix, opts?) -> svg

clustered_heatmap(expr_matrix, {cluster_rows: true, cluster_cols: true, title: "Clustered Expression"})

Sequence Visualization

# Sequence logo from binding sites
let motif_seqs = ["ATCGATCG", "ATCGTTCG", "ATCGATGG", "ATCGATCG"]
sequence_logo(motif_seqs, {title: "Binding Motif"}) |> save_svg("motif.svg")

# Phylogenetic tree
let tree = read_text("species.nwk")
phylo_tree(tree, {layout: "circular", title: "Species Tree"})

venn

Venn diagram for 2-4 sets. Shows overlapping and unique counts with labeled regions.

venn(data, opts?) -> svg

venn({A: genes_a, B: genes_b, C: genes_c}, {title: "Gene Set Overlap"})

upset

UpSet plot for complex set intersections. Scales better than Venn diagrams for 4+ sets, showing intersection sizes as a bar chart with a dot matrix indicating set membership.

upset(data, opts?) -> svg

upset({ChIP: chip_genes, RNA: rna_genes, ATAC: atac_genes}, {title: "Multi-Omics Intersection"})