Prognosis of the three subtypes in the AMC data set

Description

These functions performs survival analysis for the AMC data set, and generate a kaplan-meier plot to diagnose the prognosis of three subtypes.

Usage

progAMC(AMC_CRC_clinical, AMC_sample_head, clu.pred)
figKM(surv, survstats)

Arguments

Details

The function progAMC performs survival analysis for the AMC data set and compares the prognosis of the three subtypes. The function figKM helps visualize the prognosis in a kaplan-meier plot.

Value

This function progAMC will save surv (result of survfit containing survival curves of the AMC data set), survstats (result of survdiff), data4surv (the data to perform survival analysis) to a file called survival.RData in the current working directory.

Author(s)

Xin Wang xw264@cam.ac.uk

References

De Sousa E Melo, F. and Wang, X. and Jansen, M. et al. Poor prognosis colon cancer is defined by a molecularly distinct subtype and precursor lesion. accepted

Examples

library(survival)
data(AMC, source="DeSousa2013")
data(predAMC, source="DeSousa2013")
prog <- progAMC(AMC_CRC_clinical, AMC_sample_head, clu.pred)
surv <- prog$surv
survstats <- prog$survstats
figKM(surv, survstats)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(DeSousa2013)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/DeSousa2013/progAMC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: progAMC
> ### Title: Prognosis of the three subtypes in the AMC data set
> ### Aliases: progAMC figKM
> 
> ### ** Examples
> 
> library(survival)
> data(AMC, source="DeSousa2013")
Warning message:
In data(AMC, source = "DeSousa2013") : data set 'DeSousa2013' not found
> data(predAMC, source="DeSousa2013")
Warning message:
In data(predAMC, source = "DeSousa2013") : data set 'DeSousa2013' not found
> prog <- progAMC(AMC_CRC_clinical, AMC_sample_head, clu.pred)
Call:
survdiff(formula = Surv(time, status) ~ x, data = data4surv)

     N Observed Expected (O-E)^2/E (O-E)^2/V
x=1 44        3    10.22     5.104    11.107
x=2 22        4     4.83     0.143     0.191
x=3 24       12     3.95    16.436    21.049

 Chisq= 22  on 2 degrees of freedom, p= 1.68e-05 
> surv <- prog$surv
> survstats <- prog$survstats
> figKM(surv, survstats)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>