/omics-plotting

name: omics-plotting
description: >
  omics-plotting: publication-style figure authoring for omics / bioinformatics
  results with matplotlib / seaborn. Read this before writing any plotting or
  figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant,
  or database results — not only when a plot is explicitly requested: whenever an
  analysis will produce a figure, load this first and follow its recipes. Covers
  volcano, MA, expression / correlation heatmap, GSEA bar / dot plot,
  box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier,
  Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste
  recipes so every figure looks like one consistent system. To combine several
  plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.
license: Proprietary (HITS Inc.)

omics-plotting

Overview

When the user wants a figure, **generate it with matplotlib / seaborn**, applying the shared style block below. The user can hand-tune colors, fonts, or spines per plot, but unless they ask for something specific, paste the style block and reuse the palette so a whole analysis reads as one figure system at a glance.

This skill is self-contained: everything you need (style, palette, recipes) is in this document.

When to use

• The user asks for a plot / figure / chart / visualization from a results table

or an in-memory DataFrame (DEG table, enrichment result, expression matrix, long-form measurements, survival table…).

• You are preparing figures for a report, a paper submission or presentation and

want a consistent publication style.

> Combining several plots into one multi-panel composite, or assembling > user-supplied PNG/PDF panels, is handled by the sibling `multipanel` > skill — use this skill to draw each individual panel.

Do NOT use for

• Interactive dashboards or web charts (this is static matplotlib output).
• 3D molecular structure rendering (that is the structure viewer, not a plot).

Key Concepts

One consistent figure system

The core idea is that every figure from a single analysis should look like it came from the same publication. That is enforced by two shared objects: the `PUB_STYLE` rcParams block (fonts, spines, DPI, editable vector text) and a fixed `PALETTE` / directional color set (`UP`, `DOWN`, `NS`). Paste both at the top of every plot script and map the *same* group or direction to the *same* color across panels, so a reader can carry meaning from one figure to the next.

Diverging vs sequential colormaps

Color encoding is not free choice. Use the **diverging** colormap (`DIVERGING_CMAP = "RdBu_r"`, always `center=0`, `vmin=-vmax`) for signed quantities where zero is meaningful — z-scores, log2 fold changes, correlations. Use the **sequential** colormap (`SEQUENTIAL_CMAP = "viridis"`) for unsigned magnitudes — densities, `-log10 p`, counts. Mixing these (a sequential map on signed data) hides the sign and misleads the reader.

Data shape drives figure type

Each recipe expects a specific table shape: a per-gene DEG table (volcano, MA), a genes × samples matrix (heatmap), a samples × features matrix (PCA/UMAP), or long-form tidy rows (box/violin/bar, ridgeline, Kaplan–Meier). Identifying the shape first — then reading the header to confirm the real column names — is what selects the recipe. The column names in each recipe are defaults to override, not fixed requirements.

Decision Framework

Pick the figure type from what the data represents and what question it answers:

What does the table hold?
├─ Per-gene stats (log2FC, padj)
│   ├─ emphasize significance ......... Volcano
│   └─ emphasize expression level ..... MA plot
├─ genes × samples matrix
│   ├─ show patterns/clusters ......... Clustered expression heatmap (z-score)
│   └─ show sample-sample QC .......... Correlation heatmap
├─ Enrichment / gene-set result
│   ├─ signed effect (NES) ............ GSEA bar
│   └─ ratio + size + significance .... GSEA dot plot
├─ Long-form measurements (x, y)
│   ├─ compare distributions .......... Box / Violin
│   ├─ compare means .................. Bar (with error bars)
│   └─ many groups, shape matters ..... Ridgeline
├─ samples × features (high-dim) ...... PCA / UMAP / t-SNE
└─ time-to-event + group ............. Kaplan–Meier

| Data you have | Question | Figure | Colormap / palette | |---|---|---|---| | DEG table | Which genes change, how significantly? | Volcano | `UP`/`DOWN`/`NS` | | DEG table | Effect vs abundance | MA plot | `UP`/`DOWN`/`NS` | | Expression matrix | Cluster structure | Clustered heatmap | diverging, center 0 | | Expression matrix | Sample QC | Correlation heatmap | diverging, [-1, 1] | | Enrichment result | Top pathways, direction | GSEA bar | `UP`/`DOWN` | | Enrichment result | Ratio + significance + size | GSEA dot plot | sequential | | Long-form | Group distributions | Box / Violin | categorical `PALETTE` | | High-dim matrix | Global sample layout | PCA / UMAP / t-SNE | categorical `PALETTE` | | Survival table | Group survival over time | Kaplan–Meier | categorical `PALETTE` |

Workflow

1. **Identify the data source** — a workspace-relative CSV/TSV path or a DataFrame already in memory — and the **figure type** (pick from the table below). If the required columns are unclear, inspect the table's header first. 2. **Write one python script**: paste the style block, load the data, draw the plot with the matching recipe, and save to a **workspace-relative** path under `figures/`. 3. **Report the saved path** back to the user (and reference it in any report / deck by that relative path, e.g. ``).

Shared style — paste at the top of every plot script

import matplotlib.pyplot as plt

# Publication style (colorblind-friendly, editable vector text, no top/right spines)
PUB_STYLE = {
    "figure.dpi": 110, "savefig.dpi": 300, "savefig.bbox": "tight",
    "font.famil