epoc.survival {epoc} | R Documentation |
epoc.survival
Description
Survival analysis
Usage
epoc.svd(model, k=1, C=1, numload=NULL)
epoc.survival(G.svd, Y, U, surv, C=1, type=NULL)
epoc.svdplot(G.svd, C=1)
## S3 method for class 'EPoC.survival'
plot(x,...)
## S3 method for class 'EPoC.survival'
summary(object,...)
## S3 method for class 'summary.EPoC.survival'
print(x,...)
Arguments
model |
An object from |
k |
In case |
C |
Default 1. For |
numload |
Number of loadings in the sparse components, a vector for each component. Default 10 for all components. |
G.svd |
The list obtained from |
Y |
mRNA, samples x genes. |
U |
CNA, samples x genes. |
surv |
Survival data for the samples. |
type |
|
x |
An object from |
object |
An object from |
... |
Parameters passed down to underlying functions, |
Details
Applies survival analysis using the first SVD component, but other components can also be used by changing the input value of C
. Survival scores are generated as described in Subsect. 2.4 in the second paper referenced. A simple non-parametric survival analysis is performed, comparing survival between patientswith positive or negative scores (tumor fitness).
Value
The epoc.survival object contains the summary information from a log-rank test comparing survival (survdiff) and survival fit objects.
References
Rebecka Jörnsten, Tobias Abenius, Teresia Kling, Linnéa Schmidt, Erik Johansson, Torbjörn Nordling, Bodil Nordlander, Chris Sander, Peter Gennemark, Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander. (2011) Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Molecular Systems Biology 7
Tobias Abenius, Rebecka Jörnsten, Teresia Kling, Linnéa Schmidt, José Sánchez, Sven Nelander. (2012) System-scale network modeling of cancer using EPoC. Advances in experimental medicine and biology
See Also
epoc
, epoc.validation
and spca