TCGAanalyze_SurvivalKM: survival analysis (SA) univariate with Kaplan-Meier (KM)...

In BioinformaticsFMRP/TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

survival analysis (SA) univariate with Kaplan-Meier (KM) method.

Description

TCGAanalyze_SurvivalKM perform an univariate Kaplan-Meier (KM) survival analysis (SA). It performed Kaplan-Meier survival univariate using complete follow up with all days taking one gene a time from Genelist of gene symbols. For each gene according its level of mean expression in cancer samples, defining two thresholds for quantile expression of that gene in all samples (default ThreshTop=0.67,ThreshDown=0.33) it is possible to define a threshold of intensity of gene expression to divide the samples in 3 groups (High, intermediate, low). TCGAanalyze_SurvivalKM performs SA between High and low groups using following functions from survival package

1. survival::Surv

2. survival::survdiff

3. survival::survfit

Usage

TCGAanalyze_SurvivalKM(
  clinical_patient,
  dataGE,
  Genelist,
  Survresult = FALSE,
  ThreshTop = 0.67,
  ThreshDown = 0.33,
  p.cut = 0.05,
  group1,
  group2
)

Arguments

clinical_patient	is a data.frame using function 'clinic' with information related to barcode / samples such as bcr_patient_barcode, days_to_death , days_to_last_follow_up , vital_status, etc
dataGE	is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare
Genelist	is a list of gene symbols where perform survival KM.
Survresult	is a parameter (default = FALSE) if is TRUE will show KM plot and results.
ThreshTop	is a quantile threshold to identify samples with high expression of a gene
ThreshDown	is a quantile threshold to identify samples with low expression of a gene
p.cut	p.values threshold. Default: 0.05
group1	a string containing the barcode list of the samples in in control group
group2	a string containing the barcode list of the samples in in disease group

Value

table with survival genes pvalues from KM.

Examples

 # Selecting only 20 genes for example
 dataBRCAcomplete <- log2(dataBRCA[1:20,] + 1)

 # clinical_patient_Cancer <- GDCquery_clinic("TCGA-BRCA","clinical")
 clinical_patient_Cancer <- data.frame(
      bcr_patient_barcode = substr(colnames(dataBRCAcomplete),1,12),
      vital_status = c(rep("alive",3),"dead",rep("alive",2),rep(c("dead","alive"),2)),
      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)
 )

 group1 <- TCGAquery_SampleTypes(colnames(dataBRCAcomplete), typesample = c("NT"))
 group2 <- TCGAquery_SampleTypes(colnames(dataBRCAcomplete), typesample = c("TP"))

 tabSurvKM <- TCGAanalyze_SurvivalKM(
    clinical_patient = clinical_patient_Cancer,
    dataGE = dataBRCAcomplete,
    Genelist = rownames(dataBRCAcomplete),
    Survresult = FALSE,
    p.cut = 0.4,
    ThreshTop = 0.67,
    ThreshDown = 0.33,
    group1 = group1, # Control group
    group2 = group2
  ) # Disease group

 # If the groups are not specified group1 == group2 and all samples are used
 ## Not run: 
 tabSurvKM <- TCGAanalyze_SurvivalKM(
   clinical_patient_Cancer,
   dataBRCAcomplete,
   Genelist = rownames(dataBRCAcomplete),
   Survresult = TRUE,
   p.cut = 0.2,
    ThreshTop = 0.67,
    ThreshDown = 0.33
  )

## End(Not run)

BioinformaticsFMRP/TCGAbiolinks documentation built on April 12, 2024, 2:08 a.m.