PermTestSurv {pathwayPCA} | R Documentation

## AES-PCA permutation test of survival response for pathway PCs

### Description

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.

### Usage

    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,
      ...
    )

### Arguments

`OmicsSurv` | A data object of class `OmicsSurv`, created by the `CreateOmics` function.
`pathwayPCs_ls` | A list of pathway PC matrices returned by the `ExtractAESPCs` function.
`numReps` | How many permutations to estimate the p-value? Defaults to 0 (that is, to estimate the p-value parametrically). If `numReps` > 0, then the non-parametric, permutation p-value will be returned based on the number of random samples specified.
`parallel` | Should the computation be completed in parallel? Defaults to `FALSE`.
`numCores` | If `parallel = TRUE`, how many cores should be used for computation? Internally defaults to the number of available cores minus 2.
`...` | Dots for additional internal arguments (currently unused).

### Details

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.

### Value

A named vector of pathway permutation p-values.

### See Also

`CreateOmics`; `ExtractAESPCs`; `coxph`; `SampleSurv`

### Examples

      # 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)

[Package pathwayPCA version 1.8.0 Index]