# Cancer Multiomics > A Claude skill from zamushwani2/biomedical-ai-skills. - **Type:** Skill - **Install:** `agentstack add skill-zamushwani2-biomedical-ai-skills-cancer-multiomics` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [zamushwani2](https://agentstack.voostack.com/s/zamushwani2) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [zamushwani2](https://github.com/zamushwani2) - **Source:** https://github.com/zamushwani2/biomedical-ai-skills/tree/main/skills/cancer-multiomics ## Install ```sh agentstack add skill-zamushwani2-biomedical-ai-skills-cancer-multiomics ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Cancer Multi-Omics Analysis Integrated analysis of expression, mutation, copy number, and methylation data from TCGA and GEO for solid tumor characterization. ## When to Use This Skill Activate when the user requests: - TCGA data download and processing for any cancer type - Differential expression analysis between tumor conditions - Pathway and gene set enrichment analysis - Mutation landscape analysis (oncoplot, signatures, co-occurrence) - Copy number variation analysis - DNA methylation analysis (450K/EPIC arrays) - Integration of two or more omics layers ## Inputs | Data Type | Format | Source | |-----------|--------|--------| | Expression | Raw counts (STAR - Counts) | TCGA via TCGAbiolinks (v2.38+), GEO via GEOquery | | Mutations | MAF | TCGA GDC, cBioPortal | | Copy number | Segment files, GISTIC2.0 output | TCGA GDC, GDAC Firehose | | Methylation | IDAT files or beta-value matrices | TCGA GDC, GEO | | Clinical | Tabular | TCGA GDC, cBioPortal | --- ## Expression Analysis ### Data Retrieval ```r library(TCGAbiolinks) # v2.38.0+, Bioconductor 3.22 query DE analysis: feed directly to DESeq2 (handles normalization internally) -> Visualization (PCA, heatmap): apply vst() or rlog() from DESeq2 -> Cross-sample comparison: vst() preferred for n > 30 samples (faster than rlog) TPM/FPKM (already normalized) -> DE analysis: STOP. Go back, get raw counts. TPM/FPKM invalid for DESeq2/edgeR. -> Correlation/visualization: log2(TPM + 1), acceptable -> Gene set scoring (ssGSEA, GSVA): TPM acceptable as input RSEM expected counts (non-integer) -> round() before DESeq2: DESeqDataSetFromMatrix(countData = round(counts), ...) ``` ### Differential Expression (DESeq2 v1.50+) ```r library(DESeq2) # v1.50.2+, Bioconductor 3.22 set.seed(42) counts_mat = 10 counts in at least 5 samples # More stringent than rowSums >= 10; reduces false discoveries keep = 10) >= 5 dds 1) sig 1): %d up, %d down", sum(sig$log2FoldChange > 0), sum(sig$log2FoldChange Include batch in DESeq2 design formula. Do NOT pre-correct. For PCA/heatmap (log-scale data) -> limma::removeBatchEffect on vst/rlog values For downstream tools requiring counts -> ComBat-seq (sva package) on raw counts NEVER: ComBat (original) on raw counts. NEVER: removeBatchEffect before DE. ``` ```r # Visualization with batch removal vsd 1] ego 1, ifelse(df$log2FoldChange > 0, "Up", "Down"), "NS") top10 1 10000 DEGs: check for confounders, tumor purity bias PCA: tumor and normal should separate on PC1 or PC2 Known markers: TP53, EGFR, KRAS mutations reflected in expression? Direction: oncogenes should trend up in tumor, TSGs down Volcano symmetry: roughly symmetric unless strong biological bias expected ``` ## Mutation Analysis ### Data Retrieval ```r library(TCGAbiolinks) library(maftools) # v2.22+, Bioconductor 3.22 # Method 1: GDCquery_Maf (downloads open-access MAF, hg38-aligned) # Pipelines: mutect2, muse, varscan2, somaticsniper maf_df Most stringent, used in TCGA PanCancer Atlas Access via: maftools::tcgaLoad(study = "LUAD") Single caller: MuTect2 -> Best sensitivity for low-frequency variants MuSE -> Good for paired tumor-normal, models tumor heterogeneity VarScan2 -> Works with and without matched normal SomaticSniper -> Bayesian, good specificity For most analyses -> MuTect2 or MC3 consensus ``` ### Visualization ```r plotmafSummary(maf, rmOutlier = TRUE, addStat = "median") oncoplot(maf, top = 20, clinicalFeatures = c("ajcc_pathologic_stage", "gender"), sortByAnnotation = TRUE) # Lollipop: protein domain plot for a single gene lollipopPlot(maf, gene = "TP53", AACol = "HGVSp_Short", showMutationRate = TRUE) # Rainfall: inter-mutation distance, detects kataegis (localized hypermutation) rainfallPlot(maf, detectChangePoints = TRUE, pointSize = 0.4) ``` ### Tumor Mutation Burden ```r # TMB = total nonsynonymous mutations / capture region size (MB) laml.tmb = 30 samples for reliable decomposition tnm 0.85 is a confident assignment plotSignatures(sig, contributions = FALSE) # signature profiles plotSignatures(sig, contributions = TRUE) # per-sample contributions ``` ### Driver Gene Detection ```r # oncodrive: identifies genes with clustered mutations (positional clustering) # Not dN/dS — uses the concept that driver mutations cluster in specific protein regions oncodrive_res 0 = gain, 0.3 -> Gain > 0.7 -> Amplification (high-level gain) Loss (heterozygous deletion) Deep deletion (likely homozygous) These are conventions, not absolute cutoffs. Adjust based on tumor purity. ``` ### GISTIC2.0 Analysis ```r # GISTIC identifies recurrent focal CNV events across a cohort # Pre-computed GISTIC results available from Firehose/GDAC for most TCGA cancers: # https://gdac.broadinstitute.org/ # Read GISTIC output with maftools gistic 0.01 keep_samples 0.01 in > 5% of samples) keep (ncol(grSet) * 0.95) grSet ~420-440K retained # EPIC: ~866K -> ~780-810K retained ``` ### Alternative: ChAMP Pipeline ```r library(ChAMP) # All-in-one loading with built-in filtering # Handles detection p, SNPs, cross-reactive, sex chromosomes in one call myLoad Fine-grained control, Funnorm (best for cancer), EPIC v2 support ChAMP -> Rapid standardized pipeline, built-in filtering + batch correction Both use limma internally for DMP calling ``` ### Beta-Values vs M-Values ``` Beta-values: ratio = M / (M + U + 100), range [0, 1] Use for: biological interpretation, visualization, reporting delta-beta Problem: heteroscedastic (variance compressed at 0 and 1) M-values: log2(M / U), range (-Inf, Inf) Use for: ALL statistical testing (limma, t-tests, linear models) Reason: approximately normal, satisfies linear model assumptions Conversion: M = log2(beta / (1 - beta)) Always test on M-values, report delta-beta for biological effect size. ``` ### Differentially Methylated Positions (DMPs) ```r library(limma) # Extract M-values for statistical testing mVals 0.2, ] message(sprintf("Significant DMPs: %d hyper, %d hypo", sum(sig_dmps$deltaBeta > 0), sum(sig_dmps$deltaBeta BRAF V600E, MLH1 hypermethylation, MSI-H CIMP-L -> KRAS mutations Glioblastoma (GBM): G-CIMP defined by IDH1 mutation + global hypermethylation G-CIMP -> proneural subtype, younger patients, better survival Pan-cancer: 19/26 TCGA cancer types have confirmed CIMP subtypes ``` ### Integration: Methylation-Expression Correlation ```r # --- Match samples across platforms --- met_patients 0.2) | | `dmr_ranges.rds` | RDS | GRanges of differentially methylated regions | | `grSet.rds` | RDS | Filtered, normalized GenomicRatioSet | | `cimp_clusters.csv` | CSV | Sample-to-cluster assignments from consensus clustering | | `meth_expr_cor.csv` | CSV | Per-gene Spearman rho and adjusted p-values | | `dmp_heatmap.pdf` | PDF | Top DMPs across samples | ### Validation Checks ``` After running methylation analysis, verify: DMP count: Expect 10,000-100,000 DMPs for tumor vs normal at adj.P.Val 0.7 at many probes: check for sample swaps or contamination Known markers: MGMT promoter methylation in GBM (predicts temozolomide response) MLH1 methylation in MSI-H colorectal (silences mismatch repair) BRCA1 methylation in basal-like breast cancer Probe filtering: verify 40,000-65,000 probes removed (450K) or 55,000-85,000 (EPIC) Integration: promoter methylation-expression correlation should be predominantly negative Median rho around -0.05 to -0.15 across all genes Gene body methylation should show weak positive correlation ``` ## Common Pitfalls ### Expression 1. **DESeq2 input**: Using FPKM/TPM as input. DESeq2 requires raw integer counts. 2. **Pre-filtering GSEA input**: GSEA requires the full ranked gene list. Never pre-filter to significant genes only (that is ORA, not GSEA). 3. **LFC shrinkage for hypothesis testing**: apeglm shrinkage is for ranking and visualization. Use unshrunken results for strict significance calls unless using `lfcThreshold > 0`. 4. **Gene ID mismatch**: TCGA uses Ensembl IDs with version suffixes (ENSG00000141510.18). Strip versions before mapping: `sub("\\..*", "", ensembl_ids)`. ### Mutation 5. **Mixing callers**: Different variant callers produce different mutation sets. Pick one pipeline (or use MC3 consensus) and stick with it across the cohort. 6. **Signature sample size**: Extracting signatures from 0.3 gain, 60%) or include as covariate. 18. **TCGA barcode matching**: When integrating data types, match on patient barcode (first 12 characters), not full barcode. A patient may have multiple aliquots. ## Related Skills - [`immune-deconvolution`](../immune-deconvolution/SKILL.md): Estimate immune cell composition from the expression data produced here - [`survival-analysis`](../survival-analysis/SKILL.md): Build prognostic models using mutation, expression, and methylation features as Cox covariates ## Public Datasets for Testing | Dataset | Samples | Use Case | |---------|---------|----------| | TCGA-LUAD | 585 | Lung adenocarcinoma, well-characterized drivers (EGFR, KRAS, ALK) | | TCGA-BRCA | 1098 | Breast cancer, strong molecular subtypes (Luminal A/B, HER2, Basal) | | TCGA-GBM | 617 | Glioblastoma, classic for survival and methylation subtyping | | TCGA-COAD | 521 | Colorectal, MSI-H vs MSS comparison | ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [zamushwani2](https://github.com/zamushwani2) - **Source:** [zamushwani2/biomedical-ai-skills](https://github.com/zamushwani2/biomedical-ai-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-zamushwani2-biomedical-ai-skills-cancer-multiomics - Seller: https://agentstack.voostack.com/s/zamushwani2 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.