Train a supervised PCA model

Computes feature scores for \(p_{path}\) features of a pathway via a linear model fit.

superpc.train(
  data,
  type = c("survival", "regression", "categorical"),
  s0.perc = NULL
)

Arguments

data	A list of test data: x : A "tall" pathway data frame (\(p_{path} \times N\)). y : A response vector corresponding to type. censoring.status : If type = "survival", the censoring indicator (\(1 - \) the observed event indicator. Otherwise, NULL. featurenames : A character vector of the measured -Omes in x.
type	What model relates y and x? Options are "survival", "regression", or "categorical".
s0.perc	A stabilization parameter on the interval \([0,1]\). This is an internal argument to each of the called functions. The default value is NULL to ensure an appropriate value is determined internally.

Value

A list containing:

• feature.scores : The scaled \(p\)-dimensional score vector: each value has been divided by its respective standard deviation plus epsilon (governed by s0.perc). NA values returned by the logistic model are replaced with 0.

• type : The argument for type.

• s0.perc : The user-supplied value of s0.perc, or the internally-calculated default value from the chosen model.

• call : The output of match.call for the user- supplied function arguments.

Details

This function is a switch call to coxTrain_fun (for type = "survival"), olsTrain_fun (for type = "regression"), or glmTrain_fun (for type = "categorical").

See also

superpc.st; SuperPCA_pVals

Examples

  # DO NOT CALL THIS FUNCTION DIRECTLY.
  # Use SuperPCA_pVals() instead

if (FALSE) {
  data("colon_pathwayCollection")
  data("colonSurv_df")

  colon_OmicsSurv <- CreateOmics(
    assayData_df = colonSurv_df[,-(2:3)],
    pathwayCollection_ls = colon_pathwayCollection,
    response = colonSurv_df[, 1:3],
    respType = "surv"
  )

  asthmaGenes_char <-
    getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs

  data_ls <- list(
    x = t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ],
    y = getEventTime(colon_OmicsSurv),
    censoring.status = getEvent(colon_OmicsSurv),
    featurenames = asthmaGenes_char
  )

  superpc.train(
    data = data_ls,
    type = "surv"
  )
}