cancer-multiomics

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

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Expression Analysis

Data Retrieval

library(TCGAbiolinks)  # v2.38.0+, Bioconductor 3.22

query <- GDCquery(
  project = "TCGA-LUAD",
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts"
)
GDCdownload(query, directory = "GDCdata")
se <- GDCprepare(query, directory = "GDCdata")
# Returns SummarizedExperiment; raw counts in assay(se, "unstranded")
# TPM in assay(se, "tpm_unstrand") — use for visualization only, never for DE

# Extract clinical data
clinical <- as.data.frame(colData(se))

GEO Data Retrieval (Alternative)

library(GEOquery)
gse <- getGEO("GSE72094", GSEMatrix = TRUE)[[1]]
# For count data from GEO Supplementary files:
# Download counts matrix manually, read with read.csv/read.delim

Normalization Decision Tree

Input type?
  Raw counts (HTSeq, STAR)
    -> 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), ...)