tnsKmInteraction-methods: Kaplan-Meier analysis for dual regulons

In csgroen/RTNsurvival: Survival analysis using transcriptional networks inferred by the RTN package

Description

Kaplan-Meier analysis for dual regulons, assessing the interaction between regulons.

Usage

## S4 method for signature 'TNS'
tnsKmInteraction(tns, stepFilter = TRUE,
  pValueCutoff = 0.05, verbose = TRUE)

Arguments

tns	A TNS object, which must have passed GSEA2 analysis.
stepFilter	A single logical value specifying to use a step-filter algorithm, testing dual regulons that have at least one significant predictor in the 'tnsKM' method (when stepFilter=TRUE) or not (when stepFilter=FALSE).
pValueCutoff	An numeric value. The p-value cutoff applied to the results from the previous steps of the analysis pipeline (when stepFilter=TRUE).
verbose	A logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).

Value

Results from 'survfit' and 'survdiff', including log-rank statistics.

Examples

# load survival data
data(survival.data)

# load TNI-object
data(stni, package = "RTN")

stns <- tni2tnsPreprocess(stni, survivalData = survival.data, 
keycovar = c('Grade','Age'), time = 1, event = 2)
stns <- tnsGSEA2(stns)

# KM analysis for dual regulons
# stns <- tnsKmInteraction(stns, stepFilter = FALSE)
# tnsGet(stns, "kmInteractionTable")

csgroen/RTNsurvival documentation built on May 20, 2019, 1:49 p.m.