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
