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

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" ) }