bio-data-visualization-oncoprint-mutation-matrices
Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
npx skills add BioTender-max/awesome-bio-agent-skills --skill oncoprint-mutation-matrices --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: ComplexHeatmap 2.18+, maftools 2.18+, comut 0.0.3+, MAFtools requires R 4.0+; comut.py requires pandas 2.0+, matplotlib 3.8+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # OncoPrint and Mutation Matrix Plots **"Plot mutations across a cohort"** -> Render a gene-by-sample matrix where each cell stacks colored rectangles encoding alteration class (missense, truncating, splice, copy-gain, copy-loss, fusion). Sort samples by burden, optionally split by clinical group, and overlay co-mutation / mutual-exclusivity annotations. OncoPrint (Cerami 2012 *Cancer Discov* 2:401; canonical at cBioPortal) is the genre-defining visualization. - R: `ComplexHeatmap::oncoPrint`, `maftools::oncoplot` - Python: `comut.CoMut`, `cbioportal`-style implementations ## The Single Most Important Modern Insight -- Cell Stacking Enc
- Version Compatibility
- Decision Tree by Cohort and Question
- ComplexHeatmap::oncoPrint -- Canonical Implementation
- maftools::oncoplot -- Faster Onboarding
- Mutual Exclusivity and Co-Occurrence
- comut.py -- Python Equivalent
- Per-Method Failure Modes
- Alterations flattened to a single class
- Sample sort by gene 1 frequency only
- Showing only mutated samples (removeemptycolumns = TRUE)
- Hypermutators dominate visual
- Mutex/co-occurrence p-values overinterpreted on small cohorts
- Small-Cohort Regime (N = 20-50)
- Reconciliation: When Implementations Differ
What does the bio-data-visualization-oncoprint-mutation-matrices skill do?
Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill oncoprint-mutation-matrices --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.