Description Usage Arguments Details Value See Also Examples
Given an OmicsSurv
object and a list of pathway principal
components (PCs) from the ExtractAESPCs
function, test if
each pathway with features recorded in the bio-assay design matrix is
significantly related to the survival output.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | PermTestSurv(
OmicsSurv,
pathwayPCs_ls,
numReps = 0L,
parallel = FALSE,
numCores = NULL,
...
)
## S4 method for signature 'OmicsSurv'
PermTestSurv(
OmicsSurv,
pathwayPCs_ls,
numReps = 0L,
parallel = FALSE,
numCores = NULL,
...
)
|
OmicsSurv |
A data object of class |
pathwayPCs_ls |
A list of pathway PC matrices returned by the
|
numReps |
How many permutations to estimate the p-value? Defaults
to 0 (that is, to estimate the p-value parametrically). If
|
parallel |
Should the computation be completed in parallel? Defaults to
|
numCores |
If |
... |
Dots for additional internal arguments (currently unused). |
This function takes in a list of the first principal components
from each pathway and an object of class OmicsSurv
. This function
will then calculate the AIC of a Cox Proportional Hazards model (via the
coxph
function) with the original observations as
response and the pathway principal components as the predictor matrix. Note
that the AIC and log-likelihood are proportional because the number of
parameters in each pathway is constant.
Then, this function will create numReps
permutations of the survival
response, fit models to each of these permuted responses (holding the path
predictor matrix fixed), and calculate the AIC of each model. This function
will return a named vector of permutation p-values, where the value
for each pathway is the proportion of models for which the AIC of the
permuted response model is less than the AIC of the original model.
A named vector of pathway permutation p-values.
CreateOmics
; ExtractAESPCs
;
coxph
; SampleSurv
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # DO NOT CALL THIS FUNCTION DIRECTLY.
# Use AESPCA_pVals() instead
## Not run:
### Load the Example Data ###
data("colonSurv_df")
data("colon_pathwayCollection")
### Create an OmicsSurv Object ###
colon_Omics <- CreateOmics(
assayData_df = colonSurv_df[, -(2:3)],
pathwayCollection_ls = colon_pathwayCollection,
response = colonSurv_df[, 1:3],
respType = "surv"
)
### Extract Pathway PCs and Loadings ###
colonPCs_ls <- ExtractAESPCs(
object = colon_Omics,
parallel = TRUE,
numCores = 2
)
### Pathway p-Values ###
PermTestSurv(
OmicsSurv = colon_Omics,
pathwayPCs_ls = colonPCs_ls$PCs,
parallel = TRUE,
numCores = 2
)
## End(Not run)
|
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