1 PCA plot：pcaTCGA

Plots Two Main Components of Principal Component Analysis

用法如下：
pcaTCGA(x, group.names, title = "", return.pca = FALSE, scale = TRUE, center = TRUE, var.scale = 1, obs.scale = 1, ellipse = TRUE, circle = TRUE, var.axes = FALSE, alpha = 0.8, add.lines = TRUE, ...)

expressionsTCGA(BRCA.rnaseq, OV.rnaseq, LIHC.rnaseq) %>%
  dplyr::rename(cohort = dataset) %>%  
  filter(substr(bcr_patient_barcode, 14, 15) == "01") -> BRCA.OV.LIHC.rnaseq.cancer
pcaTCGA(BRCA.OV.LIHC.rnaseq.cancer, "cohort") -> pca_plot

plot(pca_plot)

Rplot.jpeg

2 生存分析kmTCGA()

• Kaplan-Meier 生存曲线评估乳腺癌和卵巢癌病人中TP53基因突变与生存关系

library(RTCGA.mutations)
# library(dplyr) if did not load at start
library(survminer)
mutationsTCGA(BRCA.mutations, OV.mutations) %>%
   filter(Hugo_Symbol == 'TP53') %>%
   filter(substr(bcr_patient_barcode, 14, 15) ==
   "01") %>% # cancer tissue
   mutate(bcr_patient_barcode =
   substr(bcr_patient_barcode, 1, 12)) ->
  BRCA_OV.mutations

library(RTCGA.clinical)
survivalTCGA(
  BRCA.clinical,
  OV.clinical,
  extract.cols = "admin.disease_code"
  ) %>%
   dplyr::rename(disease = admin.disease_code) ->
  BRCA_OV.clinical

BRCA_OV.clinical %>%
   left_join(
     BRCA_OV.mutations,
     by = "bcr_patient_barcode"
     ) %>%
   mutate(TP53 =
   ifelse(!is.na(Variant_Classification), "Mut","WILDorNOINFO")) ->
  BRCA_OV.clinical_mutations

BRCA_OV.clinical_mutations %>%
select(times, patient.vital_status, disease, TP53) -> BRCA_OV.2plot

kmTCGA(
  BRCA_OV.2plot,
  explanatory.names = c("TP53", "disease"),
  break.time.by = 400,
  xlim = c(0,2000),
  pval = TRUE) -> km_plot

print(km_plot)

Rplot01.jpeg

更多的看这里