Description Usage Arguments Value Examples
Creates a survival plot from TCGA patient clinical data using survival library. It uses the fields days_to_death and vital, plus a columns for groups.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | TCGAanalyze_survival(
data,
clusterCol = NULL,
legend = "Legend",
labels = NULL,
risk.table = TRUE,
xlim = NULL,
main = "Kaplan-Meier Overall Survival Curves",
ylab = "Probability of survival",
xlab = "Time since diagnosis (days)",
filename = "survival.pdf",
color = NULL,
height = 8,
width = 12,
dpi = 300,
pvalue = TRUE,
conf.int = TRUE,
...
)
|
data |
TCGA Clinical patient with the information days_to_death |
clusterCol |
Column with groups to plot. This is a mandatory field, the caption will be based in this column |
legend |
Legend title of the figure |
labels |
labels of the plot |
risk.table |
show or not the risk table |
xlim |
x axis limits e.g. xlim = c(0, 1000). Present narrower X axis, but not affect survival estimates. |
main |
main title of the plot |
ylab |
y axis text of the plot |
xlab |
x axis text of the plot |
filename |
The name of the pdf file. |
color |
Define the colors/Pallete for lines. |
height |
Image height |
width |
Image width |
dpi |
Figure quality |
pvalue |
show p-value of log-rank test |
conf.int |
show confidence intervals for point estimates of survival curves. |
... |
Further arguments passed to ggsurvplot. |
Survival plot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # clin <- GDCquery_clinic("TCGA-BRCA","clinical")
clin <- data.frame(
vital_status = c("alive","alive","alive","dead","alive",
"alive","dead","alive","dead","alive"),
days_to_death = c(NA,NA,NA,172,NA,NA,3472,NA,786,NA),
days_to_last_follow_up = c(3011,965,718,NA,1914,423,NA,5,656,1417),
gender = c(rep("male",5),rep("female",5))
)
TCGAanalyze_survival(clin, clusterCol="gender")
TCGAanalyze_survival(clin, clusterCol="gender", xlim = 1000)
TCGAanalyze_survival(clin,
clusterCol="gender",
risk.table = FALSE,
conf.int = FALSE,
color = c("pink","blue"))
TCGAanalyze_survival(clin,
clusterCol="gender",
risk.table = FALSE,
xlim = c(100,1000),
conf.int = FALSE,
color = c("Dark2"))
|
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