Chapter 2 🚩 Biomarker Evaluation
2.1 Integrate analysis
The integrate_analysis() function returns the results of both the differential analysis and survival analysis for a gene or gene set within a dataset (or datasets).
integrate_analysis(SE=MEL_GSE91061, geneSet="CD274")2.2 Differential analysis
You can use diff_biomk() to visualize differential analysis result between Pre-Treatment and Post-Treatment patients or Responders and Non-Responders in specified gene.
Pre-Treatment vs Post-Treatment
diff_biomk(SE=MEL_GSE91061,gene='CD274',type='Treatment') +
ggtitle("Pre-Treatment vs Post-Treatment) +
theme(plot.title = element_text(hjust = 0.5)) Responder vs Non-Responder
diff_biomk(SE=MEL_GSE91061,gene='CD274',type='Response') +
ggtitle("Responder vs Non-Responder") +
theme(plot.title = element_text(hjust = 0.5))2.3 Suvival analysis
You can use diff_biomk() to visualize survival analysis result in specified gene.
P <- surv_biomk(SE=MEL_GSE91061,gene='CD274')
P$plot <- P$plot +
ggtitle("Survival analysis") +
theme(plot.title = element_text(hjust = 0.5))
P2.4 Calculate comprehensive signature score
By employing the score_biomk() function, you can obtain a comprehensive signature score matrix for the 23 signatures in TigeR. In this matrix, the columns represent the signature scores, and the rows denote the sample names.
| Signature | Method | PMID |
|---|---|---|
| IRS | multivariate Cox analysis | 35280438 |
| tGE8 | median of Z-score | 31686036 |
| MEMTS | Average mean | 35769483 |
| PRGScore | Average mean | 35479097 |
| Angiogenesis | Average mean | 29867230 |
| Teffector | Average mean | 29867230 |
| Myeloid_inflammatory | Average mean | 29867230 |
| IFNG_Sig | Average mean | 29150430 |
| TLS | Weighted mean | 31942071 |
| MSKCC | Weighted mean | 34421886 |
| LMRGPI | Weighted mean | 35582412 |
| PRS | Weighted mean | 35085103 |
| Stemnesssignatures | Weighted mean | 35681225 |
| GRIP | Weighted mean | 35492358 |
| IPS | Weighted mean | 32572951 |
| Tcell_inflamed_GEP | Weighted mean | 30309915 |
| DDR | Z-score;PCA | 29443960 |
| CD8Teffector | Z-score;PCA | 29443960 |
| CellCycleReg | Z-score;PCA | 29443960 |
| PanFTBRs | Z-score;PCA | 29443960 |
| EMT1 | Z-score;PCA | 29443960 |
| EMT2 | Z-score;PCA | 29443960 |
| EMT3 | Z-score;PCA | 29443960 |
sig_res <- score_biomk(MEL_GSE78220)Columns represent signatures and rows represent sample.
2.5 Assess the Performance of Signature
By employing the roc_biomk() function, you can assess the performance of built-in and custom signatures in different datasets. The function will generate a roc object and a curve to assess the predictive performance.
sig_roc <-
roc_biomk(MEL_PRJEB23709,
Weighted_mean_Sigs$Tcell_inflamed_GEP,
method = "Weighted_mean",
rmBE=TRUE,
response_NR=TRUE)