TIMER deconvolution for cancer-specific immune estimation.
Examples
eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
#> ℹ Row number of original eset: 60483
#> ✔ 100% of probes in expression set were annotated
#> ℹ Found 2293 duplicate symbols, using "mean" method
#> ℹ Row number after filtering duplicated gene symbol: 50181
# \donttest{
res <- deconvo_timer(
eset = eset, project = "stad",
indications = rep("stad", ncol(eset))
)
#> ℹ Running TIMER deconvolution
#> ℹ Enter batch mode
#> ℹ Loading immune gene expression
#> ℹ Outlier genes: ACTB ACTG1 CD74 COL1A1 EEF1A1 ERBB2 FLNA IGHG1 IGKC MT-CO1 MT-CO2 MT-ND4 MT-RNR2 MYH11
#> ℹ Removing batch effects for stad
head(res)
#> ID ProjectID B_cell_TIMER T_cell_CD4_TIMER T_cell_CD8_TIMER
#> 1 TCGA-BR-6455 stad 0.06826546 0.21842268 0.15152999
#> 2 TCGA-BR-7196 stad 0.23999602 0.02734689 0.31100535
#> 3 TCGA-BR-8371 stad 0.09497758 0.23729463 0.00000000
#> 4 TCGA-BR-8380 stad 0.01639131 0.01427079 0.24176014
#> 5 TCGA-BR-8592 stad 0.09192814 0.27745725 0.08620296
#> 6 TCGA-BR-8686 stad 0.11028687 0.12045346 0.20750275
#> Neutrophil_TIMER Macrophage_TIMER DC_TIMER
#> 1 0.03064815 0.0000000 0.3631485
#> 2 0.03863240 0.2485108 0.4523019
#> 3 0.13239111 0.0000000 0.4644136
#> 4 0.18324600 0.1265858 0.4282349
#> 5 0.10411675 0.2958384 0.3811259
#> 6 0.01544118 0.0000000 0.2927147
# }