Survival_adaptable: Create a Kaplan Meier Estimator by using a TCGA Dataset

In w2felix/TCGADataFelix: Analyzing the TCGA Data according to gene expression and survival

Description

survival_adaptable() plots a highly modifyable survival estimator using the survminer package as a basis

Usage

Survival_adaptable(Eset, survival = "overall", clinical = FALSE,
  mutation = FALSE, expression = FALSE, optimal = FALSE, value,
  gene_signature = "no", groupings = FALSE, exclude, show_only = FALSE,
  p.val = FALSE, logrisk = TRUE, palette = "jco", xlabel, ylabel,
  plot_title = "", legend_position, legend_rows = 1,
  plot_cutpoint = FALSE, risk_table = TRUE, return_df = FALSE,
  return_fit = FALSE, factor_list = FALSE, conf_int = F, ...)

Arguments

Eset	An Expression Set
survival	For overall survival = "overall", for disease free survival: survival = "DFS"
clinical	a character vector with one or more factors of the clinical patient data
mutation	a character vector with one or more factors of the patients mutation data added to the ExpressionSet by add_mutation()
expression	a character vector with one or more gene expression data
optimal	calculate the optimal cutpoint using the max_stat ranking method, will overide the value value
value	Defines the value to subdivide the gene expression groups. Numeric: Devided into two groups "q": 25 % quantile, 25-75 % quantile and 75 % quantile
gene_signature	For more than one gene, how the genes for the signature are averaged, either "median" or "mean". If left out, all gene expression combinations will be plotted individually
groupings	You can group factors from the clinical data
exclude	Factors to exclude from any of the given clinical, expression or mutation data. The order of the to be exlcuded variables does not matter.
show_only	Works only if exclude is used. If you want to exclude specific values but only show the regression for either "gene expression", "clinical" data or "mutation data"
p.val	Displays the p-Value on the graph, only possible if a single factor is analyzed
logrisk	Displays how the p-value was calculated
xlabel	User defined x-axis label
ylabel	User defined y-axis label
plot_title	Title of the Plot, if not stated, no title will be shown
legend_position	Where should the legend be? doesn't work with theme_bw
legend_rows	How many rows are used for the legend
plot_cutpoint	Plot the graph how the optimal cutpoint was calculated, normal survival plot will be REPLACED by surv_cutpoint: Determine the optimal cutpoint for each variable using ’maxstat’
risk_table	If the risk table is shown or not, use FALSE or TRUE
return_df	Returns the Dataframe that is used for calculatinf the Cox Regression
return_fit	Returns the fit of the Regression. [[1]] = Fit, [[2]] = p-value, [[3]] = Coxph
factor_list	Returns the list of factors that are in the dataframe. These factors can be used e.g. for exclusen.
...	Additional variables that can be passed over to the ggsurvplot function of the survminer package

Value

The modified survival Estimator from the survminer package to be able to handle TCGA Data

Examples

## Not run: 
Survival_adaptable(expression = c("FOXA2"), Eset = Eset,
optimal = TRUE, p.val=TRUE,
xlabel="Days", legend_position = "top", average = "median",
plot_cutpoint=F,risk_table=TRUE)

## End(Not run)

w2felix/TCGADataFelix documentation built on May 19, 2019, 8:24 a.m.