SLTCGA is a comprehensive R package designed for systematic analysis of multi-omics data from The Cancer Genome Atlas (TCGA). The package implements 17 analytical scenarios covering correlation analysis, pathway enrichment, and survival analysis across 8 omics layers including transcriptomics, genomics, epigenomics, microRNA, clinical data, molecular signatures, and immune cell infiltration. Supports 33 main cancer types plus 32 molecular subtypes.
- 17 Analytical Scenarios: Comprehensive coverage of all variable type combinations
- 8 Omics Layers: RNAseq, Mutation, CNV, Methylation, miRNA, Clinical, ImmuneCell, Signature
- 33 Cancer Types: BRCA, LUAD, COAD, KIRC, HNSC, LIHC, GBM, SKCM, PRAD, THCA, and more
- 32 Molecular Subtypes: BRCA_IDC, BRCA_ILC, BRCA_TNBC, COAD_LCC, LUAD_LUSC, etc.
- Automated Workflows: From data loading to publication-ready visualizations
# Install Bioconductor packages
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("fgsea", "ComplexHeatmap"))# Install from GitHub
devtools::install_github("SolvingLab/SLTCGA")
# Install dependencies from SolvingLab
devtools::install_github("Zaoqu-Liu/ggforge")
devtools::install_github("SolvingLab/BioEnricher")
devtools::install_github("Zaoqu-Liu/genekitr2")
devtools::install_github("SolvingLab/astat")library(SLTCGA)
# Set path to TCGA bulk data
Sys.setenv(SL_BULK_DATA = "/path/to/bulk_data")| Scenario | Variable Types | Analysis | Visualization |
|---|---|---|---|
| 1 | 1 continuous vs 1 continuous | Pearson/Spearman correlation | CorPlot, ScatterPlot |
| 2 | 1 vs multiple continuous | Correlation | LollipopPlot, DotPlot |
| 3 | Multiple vs multiple continuous | Correlation matrix | DotPlot, Heatmap |
| 4 | 1 categorical vs 1 continuous | Wilcoxon/Kruskal-Wallis | BoxPlot |
| 5-6 | Multiple BoxPlots | Group comparison | Multiple BoxPlots |
| 7 | Categorical vs categorical | Chi-square/Fisher's exact | BarPlot, Heatmap |
| 8-9 | 1 categorical vs genome/pathways | DEA → GSEA | NetworkPlot, DotPlot |
| 10-11 | Multiple categorical vs genome/pathways | Multi-DEA → GSEA | DotPlot Paired, Matrix |
| 12-13 | 1 continuous vs genome/pathways | Correlation → GSEA | NetworkPlot, DotPlot |
| 14-15 | Multiple continuous vs genome/pathways | Multi-correlation → GSEA | DotPlot Paired, Matrix |
| 16 | 1 variable vs survival | Kaplan-Meier + Cox | KM + Cox curves |
| 17 | Multiple variables vs survival | Cox regression | Forest plot |
Load and merge multi-omics data with automatic preprocessing.
# Load single gene across multiple cancers
data <- tcga_load_modality(
var1 = "TP53",
var1_modal = "RNAseq",
var1_cancers = c("BRCA", "LUAD", "COAD")
)
# Returns: 3 features (TP53 in each cancer)Comprehensive correlation and association analysis (Scenarios 1-7).
Genome-wide scans and pathway enrichment (Scenarios 8-15).
Kaplan-Meier curves and Cox regression (Scenarios 16-17).
Example 1: Single gene mRNA correlation across cancer
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = "MDM2", var2_modal = "RNAseq", var2_cancers = "BRCA",
method = "pearson"
)
Figure 1. TP53-MDM2 mRNA expression correlation in breast cancer (r=0.42, p<0.001)
Example 2: Multi-cancer gene expression correlation
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq",
var1_cancers = c("BRCA", "LUAD", "COAD"),
var2 = "MYC", var2_modal = "RNAseq",
var2_cancers = c("BRCA", "LUAD", "COAD")
)
Figure 2. TP53-MYC correlation across three major cancer types
Example 3: Gene expression and methylation
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "Methylation", var2_cancers = "BRCA"
)
Figure 3. TP53 promoter methylation inversely correlates with mRNA expression
Example 4: ESR1 methylation-expression coupling
result <- tcga_correlation(
var1 = "ESR1", var1_modal = "Methylation", var1_cancers = "BRCA",
var2 = "ESR1", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 4. ESR1 methylation silences estrogen receptor expression
Example 5: MLH1 methylation in colorectal cancer
result <- tcga_correlation(
var1 = "MLH1", var1_modal = "Methylation", var1_cancers = "COAD",
var2 = "MLH1", var2_modal = "RNAseq", var2_cancers = "COAD"
)
Figure 5. MLH1 promoter hypermethylation leads to microsatellite instability
Example 6: CDKN2A methylation in lung cancer
result <- tcga_correlation(
var1 = "CDKN2A", var1_modal = "Methylation", var1_cancers = "LUAD",
var2 = "CDKN2A", var2_modal = "RNAseq", var2_cancers = "LUAD"
)
Figure 6. CDKN2A methylation is a key tumor suppressor inactivation mechanism
Example 7: Gene expression and CNV
result <- tcga_correlation(
var1 = "ERBB2", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = "ERBB2", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 7. ERBB2 (HER2) amplification drives overexpression in breast cancer
Example 8: EGFR CNV-expression in lung cancer
result <- tcga_correlation(
var1 = "EGFR", var1_modal = "CNV", var1_cancers = "LUAD",
var2 = "EGFR", var2_modal = "RNAseq", var2_cancers = "LUAD"
)
Figure 8. EGFR copy number variation correlates with mRNA levels
Example 9: MYC amplification
result <- tcga_correlation(
var1 = "MYC", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = "MYC", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 9. MYC amplification leads to oncogenic overexpression
Example 10: CCND1 CNV in head and neck cancer
result <- tcga_correlation(
var1 = "CCND1", var1_modal = "CNV", var1_cancers = "HNSC",
var2 = "CCND1", var2_modal = "RNAseq", var2_cancers = "HNSC"
)
Figure 10. CCND1 amplification at 11q13 locus in HNSC
Example 11: let-7a targets MYC
result <- tcga_correlation(
var1 = "hsa-let-7a-1", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = "MYC", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 11. let-7a inversely regulates MYC oncogene expression
Example 12: miR-200c and CDH1 in EMT
result <- tcga_correlation(
var1 = "hsa-mir-200c", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = "CDH1", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 12. miR-200c regulates E-cadherin in epithelial-mesenchymal transition
Example 13: miR-21 and inflammation
result <- tcga_correlation(
var1 = "hsa-mir-21", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = c("Macrophages_M1", "Macrophages_M2"), var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 13. miR-21 correlates with tumor-associated macrophage infiltration
Example 14: let-7a correlation with multiple miRNAs
result <- tcga_correlation(
var1 = "hsa-let-7a-1", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = "hsa-mir-21", var2_modal = "miRNA", var2_cancers = "BRCA"
)
Figure 14. Co-regulation patterns between oncogenic miRNAs
Example 15: miRNA and gene expression networks
result <- tcga_correlation(
var1 = c("hsa-let-7a-1", "hsa-mir-21", "hsa-mir-200c"), var1_modal = "miRNA",
var1_cancers = "BRCA",
var2 = c("MYC", "KRAS", "CDH1"), var2_modal = "RNAseq",
var2_cancers = "BRCA"
)
Figure 15. Systematic miRNA-mRNA regulatory network analysis
Example 16: TMB correlation with gene expression
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 16. TP53 expression correlates with tumor mutational burden
Example 17: ESR1 and tumor purity
result <- tcga_correlation(
var1 = "ESR1", var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = "Purity", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 17. ESR1-positive tumors show higher tumor purity
Example 18: VHL and hypoxia signature
result <- tcga_correlation(
var1 = "VHL", var1_modal = "RNAseq", var1_cancers = "KIRC",
var2 = "Hypoxia", var2_modal = "Signature", var2_cancers = "KIRC"
)
Figure 18. VHL loss activates hypoxia signaling in kidney cancer
Example 19: IFNG and IFN-gamma signature
result <- tcga_correlation(
var1 = "IFNG", var1_modal = "RNAseq", var1_cancers = "HNSC",
var2 = "IFN_Gamma", var2_modal = "Signature", var2_cancers = "HNSC"
)
Figure 19. IFNG expression drives interferon-gamma response signature
Example 20: TGFB1 and TGF-beta pathway
result <- tcga_correlation(
var1 = "TGFB1", var1_modal = "RNAseq", var1_cancers = "LIHC",
var2 = "TGF_Beta", var2_modal = "Signature", var2_cancers = "LIHC"
)
Figure 20. TGFB1 expression activates TGF-beta signaling in liver cancer
Example 21: Multiple genes vs multiple signatures
result <- tcga_correlation(
var1 = c("hsa-let-7a-1", "hsa-mir-21"), var1_modal = "miRNA",
var1_cancers = "BRCA",
var2 = c("TMB", "Purity", "TIL_Score"), var2_modal = "Signature",
var2_cancers = "BRCA"
)
Figure 21. miRNA correlations with tumor microenvironment signatures
Example 22: Signature-signature correlation
result <- tcga_correlation(
var1 = "TMB", var1_modal = "Signature", var1_cancers = "BRCA",
var2 = "Purity", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 22. High TMB tumors show lower purity (more immune infiltration)
Example 23: Multiple signature correlation matrix
result <- tcga_correlation(
var1 = c("TMB", "Purity"), var1_modal = "Signature", var1_cancers = "BRCA",
var2 = c("TIL_Score", "Leukocyte"), var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 23. Tumor microenvironment signature correlation matrix
Example 24: Multiple signatures vs gene expression
result <- tcga_correlation(
var1 = c("Leukocyte", "Stromal", "TIL_Score"), var1_modal = "Signature",
var1_cancers = "BRCA",
var2 = "CD274", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 24. Immune signatures correlate with PD-L1 expression
Example 25: TP53 mutation and expression
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Mutation", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 25. TP53 mutation status and mRNA expression levels (p=0.03)
Example 26: PIK3CA mutation and AKT1 expression
result <- tcga_correlation(
var1 = "PIK3CA", var1_modal = "Mutation", var1_cancers = "BRCA",
var2 = "AKT1", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 26. PIK3CA mutation activates AKT pathway
Example 27: KRAS mutation and EGFR expression
result <- tcga_correlation(
var1 = "KRAS", var1_modal = "Mutation", var1_cancers = "LUAD",
var2 = "EGFR", var2_modal = "RNAseq", var2_cancers = "LUAD"
)
Figure 27. KRAS mutation correlates with reduced EGFR expression
Example 28: PIK3CA mutation and ESR1 expression
result <- tcga_correlation(
var1 = "ESR1", var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = "PIK3CA", var2_modal = "Mutation", var2_cancers = "BRCA"
)
Figure 28. PIK3CA mutations are enriched in ER-positive breast cancers
Example 29: Mutation and methylation
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Methylation", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "Mutation", var2_cancers = "BRCA"
)
Figure 29. TP53 methylation and mutation are mutually exclusive
Example 30: MLH1 methylation and mutation
result <- tcga_correlation(
var1 = "MLH1", var1_modal = "Methylation", var1_cancers = "COAD",
var2 = "MLH1", var2_modal = "Mutation", var2_cancers = "COAD"
)
Figure 30. MLH1 inactivation through methylation or mutation
Example 31: miRNA and mutation
result <- tcga_correlation(
var1 = "hsa-mir-200c", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = "CDH1", var2_modal = "Mutation", var2_cancers = "BRCA"
)
Figure 31. miR-200c regulation is independent of CDH1 mutation status
Example 32: miR-21 and TP53 mutation
result <- tcga_correlation(
var1 = "hsa-mir-21", var1_modal = "miRNA", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "Mutation", var2_cancers = "BRCA"
)
Figure 32. miR-21 expression is elevated in TP53-mutant tumors
Example 33: TP53 mutation and TMB
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Mutation", var1_cancers = "BRCA",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 33. TP53-mutant tumors show elevated tumor mutational burden
Example 34: KRAS mutation and MSI in colorectal cancer
result <- tcga_correlation(
var1 = "KRAS", var1_modal = "Mutation", var1_cancers = "COAD",
var2 = "MSI", var2_modal = "Signature", var2_cancers = "COAD"
)
Figure 34. KRAS mutations are exclusive with microsatellite instability
Example 35: MLH1 mutation and MSI
result <- tcga_correlation(
var1 = "MLH1", var1_modal = "Mutation", var1_cancers = "COAD",
var2 = "MSI", var2_modal = "Signature", var2_cancers = "COAD"
)
Figure 35. MLH1 mutations lead to microsatellite instability
Example 36: MLH1 methylation and MSI
result <- tcga_correlation(
var1 = "MLH1", var1_modal = "Methylation", var1_cancers = "COAD",
var2 = "MSI", var2_modal = "Signature", var2_cancers = "COAD"
)
Figure 36. MLH1 promoter methylation causes MSI-high phenotype
Example 37: IDH1 mutation and stemness
result <- tcga_correlation(
var1 = "IDH1", var1_modal = "Mutation", var1_cancers = "GLIOMA",
var2 = "Stemness", var2_modal = "Signature", var2_cancers = "GLIOMA"
)
Figure 37. IDH1-mutant gliomas show higher stemness signature
Example 38: VHL mutation and HRD signature
result <- tcga_correlation(
var1 = "VHL", var1_modal = "Mutation", var1_cancers = "KRCC",
var2 = "HRD", var2_modal = "Signature", var2_cancers = "KRCC"
)
Figure 38. VHL mutation correlates with homologous recombination deficiency
Example 39: ERBB2 CNV and TMB
result <- tcga_correlation(
var1 = "ERBB2", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 39. ERBB2 amplification correlates with chromosomal instability
Example 40: MYC CNV and purity
result <- tcga_correlation(
var1 = "MYC", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = "Purity", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 40. MYC amplification in high-purity aggressive tumors
Example 41: PD-L1 expression and CD8+ T cells
result <- tcga_correlation(
var1 = "CD274", var1_modal = "RNAseq", var1_cancers = "LUAD",
var2 = "T_cells_CD8", var2_modal = "ImmuneCell", var2_cancers = "LUAD",
immune_algorithm = "cibersort"
)
Figure 41. PD-L1 expression correlates with CD8+ T cell infiltration
Example 42: Immune checkpoints and T cells
result <- tcga_correlation(
var1 = c("CD274", "PDCD1", "CTLA4"), var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "T_cells_CD4_memory_resting", "Macrophages_M1"),
var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 42. Systematic immune checkpoint-immune cell infiltration analysis
Example 43: IL6 and M1 macrophages
result <- tcga_correlation(
var1 = "Macrophages_M1", var1_modal = "ImmuneCell", var1_cancers = "BRCA",
var2 = "IL6", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 43. M1 macrophage infiltration correlates with IL6 expression
Example 44: CTLA4 and stromal signature
result <- tcga_correlation(
var1 = "CTLA4", var1_modal = "RNAseq", var1_cancers = "KIRC",
var2 = "Stromal", var2_modal = "Signature", var2_cancers = "KIRC"
)
Figure 44. CTLA4 expression in stromal-rich kidney tumors
Example 45: PDCD1 and multiple immune cells
result <- tcga_correlation(
var1 = c("T_cells_CD8", "T_cells_CD4_memory_resting", "Macrophages_M1"),
var1_modal = "ImmuneCell", var1_cancers = "KIRC",
var2 = "PDCD1", var2_modal = "RNAseq", var2_cancers = "KIRC"
)
Figure 45. PD-1 expression across immune cell types in kidney cancer
Example 46: CD274 and TIL score
result <- tcga_correlation(
var1 = "CD274", var1_modal = "RNAseq", var1_cancers = "LUAD",
var2 = "TIL_Score", var2_modal = "Signature", var2_cancers = "LUAD"
)
Figure 46. PD-L1 expression reflects tumor-infiltrating lymphocyte density
Example 47: All immune cells pan-cancer analysis
result <- tcga_correlation(
var1 = "CD274", var1_modal = "RNAseq", var1_cancers = "LUAD",
var2 = "ALL_IMMUNE_CELLS", var2_modal = "ImmuneCell", var2_cancers = "LUAD"
)
Figure 47. Comprehensive immune cell infiltration landscape correlates with PD-L1
Example 48: M1 vs M2 macrophages
result <- tcga_correlation(
var1 = c("T_cells_CD8", "T_cells_CD4_memory_resting"), var1_modal = "ImmuneCell",
var1_cancers = "BRCA",
var2 = c("Macrophages_M1", "Macrophages_M2"), var2_modal = "ImmuneCell",
var2_cancers = "BRCA"
)
Figure 48. T cell and macrophage infiltration patterns in breast cancer
Example 49: PIK3CA mutation and immune cells
result <- tcga_correlation(
var1 = "PIK3CA", var1_modal = "Mutation", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "T_cells_CD4_memory_resting"), var2_modal = "ImmuneCell",
var2_cancers = "BRCA"
)
Figure 49. PIK3CA mutations associated with reduced T cell infiltration
Example 50: PIK3CA mutation and multiple immune cells
result <- tcga_correlation(
var1 = "PIK3CA", var1_modal = "Mutation", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "Macrophages_M1", "NK_cells_activated"),
var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 50. PIK3CA mutation creates immunosuppressive microenvironment
Example 51: ERBB2 CNV and macrophages
result <- tcga_correlation(
var1 = "ERBB2", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = c("Macrophages_M1", "Macrophages_M2"), var2_modal = "ImmuneCell",
var2_cancers = "BRCA"
)
Figure 51. ERBB2 amplification alters macrophage polarization
Example 52: MYC CNV and T cells
result <- tcga_correlation(
var1 = "MYC", var1_modal = "CNV", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "T_cells_CD4_memory_resting"),
var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 52. MYC amplification correlates with immune exclusion
Example 53: Immune cells and TIL score
result <- tcga_correlation(
var1 = c("T_cells_CD8", "Macrophages_M1", "NK_cells_activated"),
var1_modal = "ImmuneCell", var1_cancers = "BRCA",
var2 = "TIL_Score", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 53. Cytotoxic immune cells correlate with TIL score
Example 54: Tumor purity and immune cells
result <- tcga_correlation(
var1 = "Purity", var1_modal = "Signature", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "B_cells_naive", "NK_cells_activated"),
var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 54. Tumor purity inversely correlates with immune infiltration
Example 55: CDKN2A methylation and macrophages
result <- tcga_correlation(
var1 = "CDKN2A", var1_modal = "Methylation", var1_cancers = "LUAD",
var2 = c("Macrophages_M1", "Macrophages_M2"), var2_modal = "ImmuneCell",
var2_cancers = "LUAD"
)
Figure 55. CDKN2A silencing correlates with M2 macrophage polarization
Example 56: TP53 methylation and T cells
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Methylation", var1_cancers = "BRCA",
var2 = c("T_cells_CD8", "T_cells_CD4_memory_resting"),
var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 56. TP53 methylation associated with altered T cell infiltration
Example 57: Age and TP53 expression
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 57. TP53 expression correlates with patient age
Example 58: Tumor stage and gene expression
result <- tcga_correlation(
var1 = "Stage", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "MYC", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 58. MYC expression increases with tumor stage (Kruskal-Wallis p<0.001)
Example 59: Stage and proliferation signature
result <- tcga_correlation(
var1 = "Stage", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "Proliferation", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 59. Proliferation signature correlates with advanced stage
Example 60: Age and TMB
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 60. Tumor mutational burden increases with patient age
Example 61: Age and TP53 mutation
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "Mutation", var2_cancers = "BRCA"
)
Figure 61. TP53 mutation frequency across age groups
Example 62: Age and T cell infiltration
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "T_cells_CD8", var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 62. CD8+ T cell infiltration decreases with age
Example 63: Age and methylation
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "BRCA1", var2_modal = "Methylation", var2_cancers = "BRCA"
)
Figure 63. BRCA1 methylation accumulates with age
Example 64: Age and miRNA expression
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "hsa-let-7a-1", var2_modal = "miRNA", var2_cancers = "BRCA"
)
Figure 64. let-7a expression changes with patient age
Example 65: Gender and gene expression
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "ESR1", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 65. ESR1 expression differences between male and female breast cancer
Example 66: Gender and TIL score
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "TIL_Score", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 66. Gender differences in tumor immune infiltration
Example 67: Gender and miRNA
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = c("hsa-let-7a-1", "hsa-mir-21"), var2_modal = "miRNA",
var2_cancers = "BRCA"
)
Figure 67. miRNA expression patterns differ by gender
Example 68: Gender and methylation
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical", var1_cancers = "COAD",
var2 = "MLH1", var2_modal = "Methylation", var2_cancers = "COAD"
)
Figure 68. MLH1 methylation shows gender-specific patterns in colon cancer
Example 69: ER status and ESR1 expression
result <- tcga_correlation(
var1 = "ER", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "ESR1", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 69. Clinical ER status validated by ESR1 mRNA expression
Example 70: PR status and PGR expression
result <- tcga_correlation(
var1 = "PR", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "PGR", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 70. PR status correlation with PGR mRNA levels
Example 71: HER2 status and ERBB2 expression
result <- tcga_correlation(
var1 = "HER2", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "ERBB2", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 71. HER2 IHC status validated by ERBB2 mRNA
Example 72: ER status and immune cells
result <- tcga_correlation(
var1 = "ER", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "ALL_IMMUNE_CELLS", var2_modal = "ImmuneCell", var2_cancers = "BRCA"
)
Figure 72. ER-negative tumors show higher immune infiltration
Example 73: ER status and IFN-gamma signature
result <- tcga_correlation(
var1 = "ER", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "IFN_Gamma", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 73. ER-negative tumors have elevated IFN-gamma response
Example 74: Clinical-clinical associations (chi-square)
result <- tcga_correlation(
var1 = "ER", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "PR", var2_modal = "Clinical", var2_cancers = "BRCA"
)
Figure 74. ER and PR status are highly concordant (Chi-square p<0.001)
Example 75: ER-HER2 clinical associations
result <- tcga_correlation(
var1 = "ER", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "HER2", var2_modal = "Clinical", var2_cancers = "BRCA"
)
Figure 75. ER and HER2 status define breast cancer subtypes
Example 76: Age and gender distribution
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "Gender", var2_modal = "Clinical", var2_cancers = "BRCA"
)
Figure 76. Age distribution by gender in breast cancer
Example 77: Gender distribution across cancer types
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical", var1_cancers = "HNSC",
var2 = "Race", var2_modal = "Clinical", var2_cancers = "HNSC"
)
Figure 77. Gender and racial distribution in head and neck cancer
Example 78: Vital status and tumor purity
result <- tcga_correlation(
var1 = "VitalStatus", var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = "Purity", var2_modal = "Signature", var2_cancers = "BRCA"
)
Figure 78. Tumor purity correlates with patient outcome
Example 79: Multiple genes correlation matrix
result <- tcga_correlation(
var1 = c("TP53", "ESR1", "ERBB2"), var1_modal = "RNAseq",
var1_cancers = "BRCA",
var2 = c("PGR", "AR", "GATA3"), var2_modal = "RNAseq",
var2_cancers = "BRCA"
)
Figure 79. Breast cancer gene expression correlation matrix
Example 80: Multiple CNV correlation
result <- tcga_correlation(
var1 = c("MYC", "ERBB2", "CCND1"), var1_modal = "CNV",
var1_cancers = "BRCA",
var2 = c("MYC", "ERBB2", "CCND1"), var2_modal = "RNAseq",
var2_cancers = "BRCA"
)
Figure 80. Systematic CNV-expression correlation for oncogenes
Example 81: Multiple methylation-expression pairs
result <- tcga_correlation(
var1 = c("TP53", "ESR1", "PGR"), var1_modal = "Methylation",
var1_cancers = "BRCA",
var2 = c("TP53", "ESR1", "PGR"), var2_modal = "RNAseq",
var2_cancers = "BRCA"
)
Figure 81. Epigenetic regulation of key breast cancer genes
Example 82: Multiple clinical variables vs gene expression
result <- tcga_correlation(
var1 = c("Age", "Gender", "Race"), var1_modal = "Clinical",
var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 82. TP53 expression across demographic variables
Example 83: Multiple clinical vs multiple genes
result <- tcga_correlation(
var1 = c("TP53", "ESR1"), var1_modal = "RNAseq", var1_cancers = "BRCA",
var2 = c("Age", "Gender", "Race"), var2_modal = "Clinical", var2_cancers = "BRCA"
)
Figure 83. Gene expression patterns across clinical variables
Example 84: Four clinical variables vs gene
result <- tcga_correlation(
var1 = c("Age", "Gender", "Race", "BMI"), var1_modal = "Clinical",
var1_cancers = "BRCA",
var2 = "TP53", var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 84. Comprehensive clinical association with TP53 expression
Example 85: ER-PR-HER2 trinity vs gene expression
result <- tcga_correlation(
var1 = c("ER", "PR", "HER2"), var1_modal = "Clinical", var1_cancers = "BRCA",
var2 = c("ESR1", "PGR", "ERBB2"), var2_modal = "RNAseq", var2_cancers = "BRCA"
)
Figure 85. Clinical IHC status vs mRNA expression for breast cancer biomarkers
Example 86: Pan-cancer age-TP53 correlation
result <- tcga_correlation(
var1 = "Age", var1_modal = "Clinical",
var1_cancers = c("BRCA", "LUAD", "COAD"),
var2 = "TP53", var2_modal = "RNAseq",
var2_cancers = c("BRCA", "LUAD", "COAD")
)
Figure 86. Age-TP53 relationship across three major cancer types
Example 87: Pan-cancer gender-TIL correlation
result <- tcga_correlation(
var1 = "Gender", var1_modal = "Clinical",
var1_cancers = c("BRCA", "LUAD", "HNSC"),
var2 = "TIL_Score", var2_modal = "Signature",
var2_cancers = c("BRCA", "LUAD", "HNSC")
)
Figure 87. Gender differences in immune infiltration across cancers
Example 88: Pan-cancer stage-TMB correlation
result <- tcga_correlation(
var1 = "Stage", var1_modal = "Clinical",
var1_cancers = c("LUAD", "COAD", "KIRC"),
var2 = "TMB", var2_modal = "Signature",
var2_cancers = c("LUAD", "COAD", "KIRC")
)
Figure 88. TMB increases with stage in multiple cancer types
Example 89: BRCA subtypes - ESR1 vs PGR
result <- tcga_correlation(
var1 = "ESR1", var1_modal = "RNAseq",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "PGR", var2_modal = "RNAseq",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 89. ESR1-PGR correlation across breast cancer molecular subtypes
Example 90: BRCA subtypes - ESR1 vs purity
result <- tcga_correlation(
var1 = "ESR1", var1_modal = "RNAseq",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "Purity", var2_modal = "Signature",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 90. Hormone receptor status and tumor purity across subtypes
Example 91: BRCA subtypes - PIK3CA mutation vs purity
result <- tcga_correlation(
var1 = "PIK3CA", var1_modal = "Mutation",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "Purity", var2_modal = "Signature",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 91. PIK3CA mutation frequency and tumor characteristics by subtype
Example 92: BRCA subtypes - stage vs immune cells
result <- tcga_correlation(
var1 = "Stage", var1_modal = "Clinical",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = c("T_cells_CD8", "Macrophages_M1"), var2_modal = "ImmuneCell",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 92. Immune infiltration across stages in breast cancer subtypes
Example 93: BRCA subtypes - TP53 mutation vs TMB
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Mutation",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "TMB", var2_modal = "Signature",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 93. TP53 mutation drives TMB across breast cancer subtypes
Example 94: BRCA subtypes - TP53 mutation vs immune cells
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Mutation",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "ALL_IMMUNE_CELLS", var2_modal = "ImmuneCell",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 94. Comprehensive immune landscape in TP53-mutant tumors by subtype
Example 95: BRCA subtypes - TMB vs purity
result <- tcga_correlation(
var1 = "TMB", var1_modal = "Signature",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "Purity", var2_modal = "Signature",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 95. TMB-purity relationship varies across breast cancer subtypes
Example 96: BRCA subtypes - ERBB2 CNV vs TIL score
result <- tcga_correlation(
var1 = "ERBB2", var1_modal = "CNV",
var1_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC"),
var2 = "TIL_Score", var2_modal = "Signature",
var2_cancers = c("BRCA_IDC", "BRCA_ILC", "BRCA_TNBC")
)
Figure 96. ERBB2 amplification correlates with immune infiltration
Example 97: IDC subtype-specific analysis
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA_IDC",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA_IDC"
)
Figure 97. TP53 expression-TMB correlation in invasive ductal carcinoma
Example 98: TNBC subtype-specific analysis
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA_TNBC",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA_TNBC"
)
Figure 98. TP53-TMB relationship in triple-negative breast cancer
Example 99: ILC subtype-specific analysis
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq", var1_cancers = "BRCA_ILC",
var2 = "TMB", var2_modal = "Signature", var2_cancers = "BRCA_ILC"
)
Figure 99. TP53-TMB dynamics in invasive lobular carcinoma
Example 100: COAD subtypes - stage vs TIL score
result <- tcga_correlation(
var1 = "Stage", var1_modal = "Clinical",
var1_cancers = c("COAD_LCC", "COAD_MAC"),
var2 = "TIL_Score", var2_modal = "Signature",
var2_cancers = c("COAD_LCC", "COAD_MAC")
)
Figure 100. Stage-immune infiltration across colon cancer anatomical subtypes
Example 101: COAD subtypes - KRAS mutation vs MSI
result <- tcga_correlation(
var1 = "KRAS", var1_modal = "Mutation",
var1_cancers = c("COAD_LCC", "COAD_RCC", "COAD_MAC"),
var2 = "MSI", var2_modal = "Signature",
var2_cancers = c("COAD_LCC", "COAD_RCC", "COAD_MAC")
)
Figure 101. KRAS mutation and MSI status across colon cancer locations
Example 102: COAD subtypes - APC mutation vs stemness
result <- tcga_correlation(
var1 = "APC", var1_modal = "Mutation",
var1_cancers = c("COAD_LCC", "COAD_RCC"),
var2 = "Stemness", var2_modal = "Signature",
var2_cancers = c("COAD_LCC", "COAD_RCC")
)
Figure 102. APC mutation correlates with cancer stemness by tumor location
Example 103: COAD subtypes - TIL score vs stemness
result <- tcga_correlation(
var1 = "TIL_Score", var1_modal = "Signature",
var1_cancers = c("COAD_LCC", "COAD_RCC"),
var2 = "Stemness", var2_modal = "Signature",
var2_cancers = c("COAD_LCC", "COAD_RCC")
)
Figure 103. Inverse correlation between immune infiltration and stemness
Example 104: ESCA subtypes - TP53 mutation vs TMB
result <- tcga_correlation(
var1 = "TP53", var1_modal = "Mutation",
var1_cancers = c("ESCA_ESCC", "ESCA_EAC"),
var2 = "TMB", var2_modal = "Signature",
var2_cancers = c("ESCA_ESCC", "ESCA_EAC")
)
Figure 104. TP53 mutation and TMB in esophageal cancer histological subtypes
Example 105: ESCA subtypes - CDKN2A vs purity
result <- tcga_correlation(
var1 = "CDKN2A", var1_modal = "RNAseq",
var1_cancers = c("ESCA_ESCC", "ESCA_EAC"),
var2 = "Purity", var2_modal = "Signature",
var2_cancers = c("ESCA_ESCC", "ESCA_EAC")
)
Figure 105. CDKN2A expression patterns across esophageal cancer subtypes
Example 106: LGG subtypes - IDH1 mutation vs stemness
result <- tcga_correlation(
var1 = "IDH1", var1_modal = "Mutation",
var1_cancers = c("LGG_ASTROCYTOMA", "LGG_OLIGODENDROGLIOMA"),
var2 = "Stemness", var2_modal = "Signature",
var2_cancers = c("LGG_ASTROCYTOMA", "LGG_OLIGODENDROGLIOMA")
)
Figure 106. IDH1 mutation and stemness across glioma histological types
Example 107: LGG subtypes - TP53 vs TIL score
result <- tcga_correlation(
var1 = "TP53", var1_modal = "RNAseq",
var1_cancers = c("LGG_ASTROCYTOMA", "LGG_OLIGOASTROCYTOMA"),
var2 = "TIL_Score", var2_modal = "Signature",
var2_cancers = c("LGG_ASTROCYTOMA", "LGG_OLIGOASTROCYTOMA")
)
Figure 107. TP53 expression and immune infiltration in glioma subtypes
Example 108: TP53 mutation genome-wide scan
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "BRCA",
analysis_type = "genome",
genome_modal = "RNAseq",
top_n = 50
)
Figure 108. Network visualization of top 50 up/down-regulated genes in TP53-mutant tumors
Example 109: TP53 mutation pathway enrichment
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "BRCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 109. Hallmark pathway enrichment in TP53-mutant breast cancer
Example 110: KRAS mutation genome scan in lung cancer
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "genome",
genome_modal = "RNAseq",
top_n = 50
)
Figure 110. KRAS mutation-driven transcriptomic changes in lung adenocarcinoma
Example 111: KRAS mutation pathway enrichment
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 111. MAPK and metabolic pathway activation in KRAS-mutant lung cancer
Example 112: EGFR mutation pathway analysis
result <- tcga_enrichment(
var1 = "EGFR",
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 112. EGFR mutation activates distinct pathways from KRAS
Example 113: PIK3CA mutation pathway enrichment
result <- tcga_enrichment(
var1 = "PIK3CA",
var1_modal = "Mutation",
var1_cancers = "UCEC",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 113. PI3K-AKT pathway activation in PIK3CA-mutant endometrial cancer
Example 114: BRAF mutation enrichment in melanoma
result <- tcga_enrichment(
var1 = "BRAF",
var1_modal = "Mutation",
var1_cancers = "SKCM",
analysis_type = "genome",
genome_modal = "RNAseq",
top_n = 50
)
Figure 114. BRAF V600E mutation transcriptomic signature in melanoma
Example 115: BRAF mutation pathway analysis
result <- tcga_enrichment(
var1 = "BRAF",
var1_modal = "Mutation",
var1_cancers = "SKCM",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 115. MAPK pathway hyperactivation in BRAF-mutant melanoma
Example 116: VHL mutation pathway analysis
result <- tcga_enrichment(
var1 = "VHL",
var1_modal = "Mutation",
var1_cancers = "KIRC",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 116. Hypoxia and angiogenesis pathways in VHL-mutant kidney cancer
Example 117: IDH1 mutation pathway analysis
result <- tcga_enrichment(
var1 = "IDH1",
var1_modal = "Mutation",
var1_cancers = "GBM",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 117. Metabolic reprogramming in IDH1-mutant glioblastoma
Example 118: CTNNB1 mutation pathway analysis
result <- tcga_enrichment(
var1 = "CTNNB1",
var1_modal = "Mutation",
var1_cancers = "LIHC",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 118. WNT pathway activation in CTNNB1-mutant liver cancer
Example 119: APC mutation pathway analysis
result <- tcga_enrichment(
var1 = "APC",
var1_modal = "Mutation",
var1_cancers = "COAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 20
)
Figure 119. WNT signaling dysregulation in APC-mutant colorectal cancer
Example 120: KRAS-EGFR comparative genome scan
result <- tcga_enrichment(
var1 = c("KRAS", "EGFR"),
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "genome",
genome_modal = "RNAseq",
top_n = 50
)
Figure 120. Comparative transcriptomic impact of KRAS, EGFR, and TP53 mutations
Example 121: KRAS-EGFR pathway comparison
result <- tcga_enrichment(
var1 = c("KRAS", "EGFR"),
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 121. Differential pathway activation in KRAS vs EGFR-mutant tumors
Example 122: KRAS-TP53 pathway comparison
result <- tcga_enrichment(
var1 = c("KRAS", "TP53"),
var1_modal = "Mutation",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 122. Synergistic pathway dysregulation in KRAS-TP53 co-mutant tumors
Example 123: TP53-ESR1 pathway comparison in breast cancer
result <- tcga_enrichment(
var1 = c("TP53", "ESR1"),
var1_modal = "RNAseq",
var1_cancers = "BRCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 15
)
Figure 123. TP53 vs ESR1-associated pathway activities
Example 124: Three-gene pathway comparison
result <- tcga_enrichment(
var1 = c("TP53", "ESR1", "ERBB2"),
var1_modal = "RNAseq",
var1_cancers = "BRCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 15
)
Figure 124. Comprehensive pathway matrix for breast cancer biomarkers
Example 125: Pan-cancer TP53 mutation comparison
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = c("BRCA", "LUAD", "COAD"),
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 125. TP53 mutation drives similar pathways across different cancers
Example 126: Pan-cancer KRAS mutation comparison
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = c("LUAD", "COAD", "PAAD"),
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 126. KRAS mutation pathway effects across GI and lung cancers
Example 127: COAD subtype KRAS comparison
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = c("COAD_LCC", "COAD_RCC"),
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 127. KRAS mutation pathway effects differ by colon tumor location
Example 128: BRCA subtype TP53 comparison
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = c("BRCA_IDC", "BRCA_TNBC"),
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
top_n = 15
)
Figure 128. TP53 mutation pathway impact varies between breast cancer subtypes
Example 129: TP53 expression genome scan
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "RNAseq",
var1_cancers = "BRCA",
analysis_type = "genome",
genome_modal = "RNAseq",
method = "pearson",
top_n = 50
)
Figure 129. Genes co-expressed with TP53 in breast cancer
Example 130: TP53 expression pathway enrichment
result <- tcga_enrichment(
var1 = "TP53",
var1_modal = "RNAseq",
var1_cancers = "BRCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 130. Pathways correlated with TP53 expression levels
Example 131: KRAS expression genome scan
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "RNAseq",
var1_cancers = "LUAD",
analysis_type = "genome",
genome_modal = "RNAseq",
method = "spearman",
top_n = 50
)
Figure 131. KRAS co-expression network in lung adenocarcinoma
Example 132: KRAS expression pathway enrichment
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "RNAseq",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 132. KRAS expression-correlated pathway activities
Example 133: EGFR expression genome scan
result <- tcga_enrichment(
var1 = "EGFR",
var1_modal = "RNAseq",
var1_cancers = "LUAD",
analysis_type = "genome",
genome_modal = "RNAseq",
method = "pearson",
top_n = 50
)
Figure 133. EGFR co-expression network reveals pathway dependencies
Example 134: EGFR expression pathway enrichment
result <- tcga_enrichment(
var1 = "EGFR",
var1_modal = "RNAseq",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 134. EGFR-associated pathway activation patterns
Example 135: VHL expression pathway analysis
result <- tcga_enrichment(
var1 = "VHL",
var1_modal = "RNAseq",
var1_cancers = "KIRC",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 135. VHL expression inversely correlates with hypoxia pathways
Example 136: MYC expression pathway analysis
result <- tcga_enrichment(
var1 = "MYC",
var1_modal = "RNAseq",
var1_cancers = "HNSC",
analysis_type = "genome",
genome_modal = "RNAseq",
method = "pearson",
top_n = 50
)
Figure 136. MYC co-expression network in head and neck cancer
Example 137: MYC pathway enrichment
result <- tcga_enrichment(
var1 = "MYC",
var1_modal = "RNAseq",
var1_cancers = "HNSC",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 137. MYC drives proliferation and metabolic pathways
Example 138: CD274 (PD-L1) expression pathway analysis
result <- tcga_enrichment(
var1 = "CD274",
var1_modal = "RNAseq",
var1_cancers = "SKCM",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 138. PD-L1 expression correlates with immune activation pathways
Example 139: AR expression pathway analysis
result <- tcga_enrichment(
var1 = "AR",
var1_modal = "RNAseq",
var1_cancers = "PRAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 139. Androgen receptor drives hormone signaling in prostate cancer
Example 140: BRAF expression pathway analysis
result <- tcga_enrichment(
var1 = "BRAF",
var1_modal = "RNAseq",
var1_cancers = "THCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 140. BRAF expression-associated pathways in thyroid cancer
Example 141: EGFR-KRAS comparative genome scan
result <- tcga_enrichment(
var1 = c("EGFR", "KRAS"),
var1_modal = "RNAseq",
var1_cancers = "LUAD",
analysis_type = "genome",
genome_modal = "RNAseq",
method = "pearson",
top_n = 50
)
Figure 141. Differential co-expression networks for EGFR vs KRAS
Example 142: Pan-cancer KRAS expression comparison
result <- tcga_enrichment(
var1 = "KRAS",
var1_modal = "RNAseq",
var1_cancers = c("LUAD", "COAD"),
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 15
)
Figure 142. KRAS-correlated pathways across lung and colon cancers
Example 143: TMB signature pathway analysis
result <- tcga_enrichment(
var1 = "TMB",
var1_modal = "Signature",
var1_cancers = "BRCA",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 143. High TMB tumors show immune activation pathways
Example 144: TIL score pathway analysis
result <- tcga_enrichment(
var1 = "TIL_Score",
var1_modal = "Signature",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 144. TIL score correlates with adaptive immune response
Example 145: Stemness signature pathway analysis
result <- tcga_enrichment(
var1 = "Stemness",
var1_modal = "Signature",
var1_cancers = "GBM",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 20
)
Figure 145. Stemness signature associates with developmental pathways
Example 146: Purity signature pathway analysis
result <- tcga_enrichment(
var1 = "Purity",
var1_modal = "Signature",
var1_cancers = "COAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "pearson",
top_n = 20
)
Figure 146. Tumor purity inversely correlates with immune pathways
Example 147: CD8+ T cell pathway analysis
result <- tcga_enrichment(
var1 = "T_cells_CD8",
var1_modal = "ImmuneCell",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
immune_algorithm = "cibersort",
method = "spearman",
top_n = 20
)
Figure 147. CD8+ T cell infiltration correlates with immune checkpoint pathways
Example 148: All T cell algorithms pathway analysis
result <- tcga_enrichment(
var1 = c("T_cells_CD8_cibersort", "T_cells_CD8_epic", "T_cells_CD8_mcpcounter",
"T_cells_CD8_quantiseq", "T_cells_CD8_timer", "T_cells_CD8_naive_xcell",
"T_cells_CD8_xcell", "T_cells_CD8_Tcm_xcell", "T_cells_CD8_Tem_xcell"),
var1_modal = "ImmuneCell",
var1_cancers = "LUAD",
analysis_type = "enrichment",
enrich_database = "MsigDB",
msigdb_category = "H",
method = "spearman",
top_n = 15
)
Figure 148. Consensus CD8+ T cell pathway analysis across deconvolution algorithms
Example 149: TP53 expression and overall survival
result <- tcga_survival(
var1 = "TP53",
var1_modal = "RNAseq",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 149. TP53 expression predicts survival in breast cancer. Left: KM curve with log-rank test. Right: Cox regression with HR=1.85, p=0.012
Example 150: TP53 mutation and survival
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "BRCA",
surv_type = "OS"
)
Figure 150. TP53 mutations associated with worse survival outcomes
Example 151: PIK3CA mutation and survival
result <- tcga_survival(
var1 = "PIK3CA",
var1_modal = "Mutation",
var1_cancers = "BRCA",
surv_type = "OS"
)
Figure 151. PIK3CA mutation shows protective effect in breast cancer
Example 152: TMB signature and survival
result <- tcga_survival(
var1 = "TMB",
var1_modal = "Signature",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 152. High TMB predicts better survival (immunotherapy benefit)
Example 153: Purity signature and survival
result <- tcga_survival(
var1 = "Purity",
var1_modal = "Signature",
var1_cancers = "OV",
surv_type = "OS",
cutoff_type = "median"
)
Figure 153. Tumor purity predicts survival in ovarian cancer
Example 154: Stemness signature and survival
result <- tcga_survival(
var1 = "Stemness",
var1_modal = "Signature",
var1_cancers = "COAD",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 154. High stemness signature predicts poor prognosis
Example 155: Age and survival
result <- tcga_survival(
var1 = "Age",
var1_modal = "Clinical",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 155. Advanced age is a poor prognostic factor
Example 156: Stage and survival
result <- tcga_survival(
var1 = "Stage",
var1_modal = "Clinical",
var1_cancers = "LUAD",
surv_type = "OS"
)
Figure 156. Tumor stage strongly predicts survival outcome
Example 157: Race and survival
result <- tcga_survival(
var1 = "Race",
var1_modal = "Clinical",
var1_cancers = "BRCA",
surv_type = "OS"
)
Figure 157. Racial disparities in breast cancer survival
Example 158: Gender and survival in kidney cancer
result <- tcga_survival(
var1 = "Gender",
var1_modal = "Clinical",
var1_cancers = "KIRC",
surv_type = "OS"
)
Figure 158. Female patients show survival advantage in kidney cancer
Example 159: CNV and survival
result <- tcga_survival(
var1 = "MYC",
var1_modal = "CNV",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 159. MYC amplification predicts poor prognosis
Example 160: Methylation and survival
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Methylation",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 160. TP53 promoter methylation impacts survival
Example 161: MLH1 methylation and survival
result <- tcga_survival(
var1 = "MLH1",
var1_modal = "Methylation",
var1_cancers = "COAD",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 161. MLH1 methylation predicts better survival due to MSI
Example 162: miRNA and survival
result <- tcga_survival(
var1 = "hsa-let-7a-1",
var1_modal = "miRNA",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 162. let-7a expression is a favorable prognostic marker
Example 163: miRNA survival in lung cancer
result <- tcga_survival(
var1 = "hsa-let-7a-1",
var1_modal = "miRNA",
var1_cancers = "LUAD",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 163. let-7a predicts survival in lung adenocarcinoma
Example 164: Immune cell and survival
result <- tcga_survival(
var1 = "T_cells_CD8",
var1_modal = "ImmuneCell",
var1_cancers = "LUAD",
surv_type = "OS",
immune_algorithm = "cibersort",
cutoff_type = "optimal"
)
Figure 164. High CD8+ T cell infiltration predicts favorable outcome
Example 165: Progression-free survival analysis
result <- tcga_survival(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = "LUAD",
surv_type = "PFS"
)
Figure 165. KRAS mutation and progression-free survival
Example 166: KRAS expression and PFS
result <- tcga_survival(
var1 = "KRAS",
var1_modal = "RNAseq",
var1_cancers = "LUAD",
surv_type = "PFS",
cutoff_type = "optimal"
)
Figure 166. KRAS expression level predicts disease progression
Example 167: TIL score and PFS
result <- tcga_survival(
var1 = "TIL_Score",
var1_modal = "Signature",
var1_cancers = "LUAD",
surv_type = "PFS",
cutoff_type = "optimal"
)
Figure 167. TIL score predicts progression-free survival
Example 168: CNV and PFS
result <- tcga_survival(
var1 = "ERBB2",
var1_modal = "CNV",
var1_cancers = "BRCA",
surv_type = "PFS",
cutoff_type = "optimal"
)
Figure 168. ERBB2 amplification and disease progression
Example 169: Macrophages and PFS
result <- tcga_survival(
var1 = "Macrophages_M1",
var1_modal = "ImmuneCell",
var1_cancers = "LIHC",
surv_type = "PFS",
immune_algorithm = "cibersort",
cutoff_type = "optimal"
)
Figure 169. M1 macrophage infiltration predicts better PFS in liver cancer
Example 170: Subtype-specific survival - TNBC
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "BRCA_TNBC",
surv_type = "OS"
)
Figure 170. TP53 mutation in triple-negative breast cancer
Example 171: Subtype-specific survival - IDC
result <- tcga_survival(
var1 = "ESR1",
var1_modal = "RNAseq",
var1_cancers = "BRCA_IDC",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 171. ESR1 expression predicts survival in invasive ductal carcinoma
Example 172: Subtype-specific survival - IDC TP53 mutation
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "BRCA_IDC",
surv_type = "OS"
)
Figure 172. TP53 mutation impact in IDC subtype
Example 173: Colorectal cancer subtype survival
result <- tcga_survival(
var1 = "KRAS",
var1_modal = "Mutation",
var1_cancers = "COAD_LCC",
surv_type = "OS"
)
Figure 173. KRAS mutation in left-sided colon cancer
Example 174: Esophageal cancer subtype survival
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = "ESCA_ESCC",
surv_type = "OS"
)
Figure 174. TP53 mutation in esophageal squamous cell carcinoma
Example 175: Multi-gene forest plot
result <- tcga_survival(
var1 = c("TP53", "ESR1", "ERBB2"),
var1_modal = "RNAseq",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 175. Multivariate Cox analysis for breast cancer biomarkers
Example 176: Multi-mutation forest plot
result <- tcga_survival(
var1 = c("KRAS", "EGFR", "TP53"),
var1_modal = "Mutation",
var1_cancers = "LUAD",
surv_type = "OS"
)
Figure 176. Independent prognostic value of key mutations in lung cancer
Example 177: Multi-signature forest plot
result <- tcga_survival(
var1 = c("TMB", "TIL_Score", "IFN_Gamma"),
var1_modal = "Signature",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 177. Immune signatures as independent prognostic factors
Example 178: Multi-CNV forest plot
result <- tcga_survival(
var1 = c("MYC", "ERBB2", "CCND1"),
var1_modal = "CNV",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 178. Amplification events as prognostic markers
Example 179: Multi-methylation forest plot
result <- tcga_survival(
var1 = c("TP53", "ESR1", "BRCA1"),
var1_modal = "Methylation",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 179. Epigenetic markers in survival prediction
Example 180: Multi-miRNA forest plot
result <- tcga_survival(
var1 = c("hsa-let-7a-1", "hsa-mir-21", "hsa-mir-200c"),
var1_modal = "miRNA",
var1_cancers = "BRCA",
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 180. miRNA panel for survival stratification
Example 181: Multi-immune cell forest plot
result <- tcga_survival(
var1 = c("T_cells_CD8", "Macrophages_M1", "NK_cells_activated"),
var1_modal = "ImmuneCell",
var1_cancers = "BRCA",
surv_type = "OS",
immune_algorithm = "cibersort",
cutoff_type = "optimal"
)
Figure 181. Immune cell panel predicts overall survival
Example 182: Multi-clinical variable forest plot
result <- tcga_survival(
var1 = c("Age", "Gender", "Race"),
var1_modal = "Clinical",
var1_cancers = "BRCA",
surv_type = "OS"
)
Figure 182. Demographic variables in multivariate survival model
Example 183: Stage and age forest plot
result <- tcga_survival(
var1 = c("Stage", "Age"),
var1_modal = "Clinical",
var1_cancers = "BRCA",
surv_type = "OS"
)
Figure 183. Stage remains the strongest prognostic factor
Example 184: ER-PR-HER2 trinity forest plot
result <- tcga_survival(
var1 = c("ER", "PR", "HER2"),
var1_modal = "Clinical",
var1_cancers = "BRCA",
surv_type = "PFS"
)
Figure 184. Hormone receptor status predicts disease progression
Example 185: Pan-cancer TP53 mutation forest plot
result <- tcga_survival(
var1 = "TP53",
var1_modal = "Mutation",
var1_cancers = c("BRCA", "LUAD"),
surv_type = "OS"
)
Figure 185. TP53 mutation shows cancer-specific survival effects
Example 186: Pan-cancer TP53 expression forest plot
result <- tcga_survival(
var1 = "TP53",
var1_modal = "RNAseq",
var1_cancers = c("BRCA", "LUAD"),
surv_type = "OS",
cutoff_type = "optimal"
)
Figure 186. TP53 expression has opposite effects in different cancers
| Modality | Description | Example Variables |
|---|---|---|
| RNAseq | mRNA expression (log2 TPM) | TP53, ESR1, MYC, EGFR |
| Mutation | Binary mutation status | TP53, KRAS, PIK3CA, BRAF |
| CNV | Copy number variation | MYC, ERBB2, CCND1, EGFR |
| Methylation | Promoter methylation beta values | TP53, ESR1, MLH1, BRCA1 |
| miRNA | microRNA expression | hsa-let-7a-1, hsa-mir-21 |
| Clinical | Patient demographics and clinical features | Age, Gender, Stage, ER, PR, HER2 |
| ImmuneCell | Immune cell infiltration scores | T_cells_CD8, Macrophages_M1, NK_cells |
| Signature | Molecular signatures | TMB, Purity, TIL_Score, Stemness, MSI |
SLTCGA integrates 7 immune deconvolution algorithms:
- CIBERSORT: 22 immune cell types
- EPIC: 8 cell types
- MCPcounter: 10 cell types
- quanTIseq: 11 cell types
- TIMER: 6 cell types
- xCell: 64 cell types
- Consensus: Average across algorithms
- MsigDB Hallmark: 50 curated gene sets (default)
- MsigDB C2 KEGG: KEGG pathways
- MsigDB C2 Reactome: Reactome pathways
- MsigDB C5 GO: Gene Ontology (BP, MF, CC)
- MsigDB C6: Oncogenic signatures
- MsigDB C7: Immunologic signatures
Correlation Analysis:
- Continuous vs Continuous: Pearson/Spearman correlation
- Categorical vs Continuous: Wilcoxon/Kruskal-Wallis test
- Categorical vs Categorical: Chi-square/Fisher's exact test
Enrichment Analysis:
- Differential expression: Wilcoxon/limma
- Pathway enrichment: GSEA (fgsea)
Survival Analysis:
- Single variable: Kaplan-Meier + Cox regression
- Multiple variables: Multivariate Cox (Forest plot)
- Cutoff selection: Optimal (survminer) or Median
TCGA cancer types supported:
- BRCA: Breast invasive carcinoma
- LUAD: Lung adenocarcinoma
- LUSC: Lung squamous cell carcinoma
- COAD: Colon adenocarcinoma
- READ: Rectum adenocarcinoma
- KIRC: Kidney renal clear cell carcinoma
- KIRP: Kidney renal papillary cell carcinoma
- HNSC: Head and neck squamous cell carcinoma
- LIHC: Liver hepatocellular carcinoma
- THCA: Thyroid carcinoma
- PRAD: Prostate adenocarcinoma
- STAD: Stomach adenocarcinoma
- SKCM: Skin cutaneous melanoma
- BLCA: Bladder urothelial carcinoma
- UCEC: Uterine corpus endometrial carcinoma
- GBM: Glioblastoma multiforme
- LGG: Brain lower grade glioma
- OV: Ovarian serous cystadenocarcinoma
- ESCA: Esophageal carcinoma
- PAAD: Pancreatic adenocarcinoma
- KICH: Kidney chromophobe
- PCPG: Pheochromocytoma and paraganglioma
- SARC: Sarcoma
- TGCT: Testicular germ cell tumors
- THYM: Thymoma
- MESO: Mesothelioma
- ACC: Adrenocortical carcinoma
- UVM: Uveal melanoma
- DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma
- CESC: Cervical squamous cell carcinoma
- CHOL: Cholangiocarcinoma
- UCS: Uterine carcinosarcoma
- CRC: Colorectal cancer (COAD+READ)
Breast Cancer (BRCA):
- BRCA_IDC: Invasive ductal carcinoma
- BRCA_ILC: Invasive lobular carcinoma
- BRCA_TNBC: Triple-negative breast cancer
Colon Cancer (COAD):
- COAD_LCC: Left-sided colon cancer
- COAD_RCC: Right-sided colon cancer
- COAD_MAC: Mucinous adenocarcinoma
- COAD_TC: Transverse colon
Lung Cancer:
- NSCLC: Non-small cell lung cancer (LUAD+LUSC)
Kidney Cancer:
- KRCC: Kidney renal cell carcinoma (KIRC+KIRP+KICH)
Glioma:
- GLIOMA: Glioma (GBM+LGG)
- LGG_ASTROCYTOMA: Astrocytoma
- LGG_OLIGODENDROGLIOMA: Oligodendroglioma
- LGG_OLIGOASTROCYTOMA: Oligoastrocytoma
Esophageal Cancer (ESCA):
- ESCA_ESCC: Esophageal squamous cell carcinoma
- ESCA_EAC: Esophageal adenocarcinoma
Head and Neck Cancer (HNSC):
- HNSC_OSCC: Oral squamous cell carcinoma
- HNSC_LSCC: Laryngeal squamous cell carcinoma
- HNSC_OPSCC: Oropharyngeal squamous cell carcinoma
Cervical Cancer (CESC):
- CESC_CSCC: Cervical squamous cell carcinoma
And more...
All analyses automatically save:
- Plots (PNG, 300 DPI):
sltcga_output/[analysis]_[cancer]_[variables]_[modality].png - Statistics (TSV): Results tables with p-values, correlations, etc.
- Raw Data (TSV): Merged data matrix for downstream analysis
If you use SLTCGA in your research, please cite:
Liu Z, et al. (2025). SLTCGA: A Comprehensive R Package for Multi-Omics
Analysis of The Cancer Genome Atlas. [Journal], [Volume]([Issue]), [Pages].
- Author: Zaoqu Liu; Yuyao Liu
- Email: liuzaoqu@163.com
- GitHub: https://github.com/SolvingLab/SLTCGA
GPL-3.0 License. See LICENSE.md for details.
- The Cancer Genome Atlas (TCGA) Research Network
- Bioconductor community
- All package dependencies maintainers
Last updated: 2025