SuperPCA_pVals {pathwayPCA} | R Documentation

## Test pathways with Supervised PCA

### Description

Given a supervised `OmicsPath` object (one of `OmicsSurv`, `OmicsReg`, or `OmicsCateg`), extract the first k principal components (PCs) from each pathway-subset of the -Omics assay design matrix, test their association with the response matrix, and return a data frame of the adjusted p-values for each pathway.

### Usage

    SuperPCA_pVals(
      object,
      n.threshold = 20,
      numPCs = 1,
      parallel = FALSE,
      numCores = NULL,
      adjustpValues = TRUE,
      adjustment = c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY",
        "ABH", "TSBH"),
      ...
    )

    ## S4 method for signature 'OmicsPathway'
    SuperPCA_pVals(
      object,
      n.threshold = 20,
      numPCs = 1,
      parallel = FALSE,
      numCores = NULL,
      adjustpValues = TRUE,
      adjustment = c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY",
        "ABH", "TSBH"),
      ...
    )

### Arguments

`object` | An object of superclass `OmicsPathway` with a response matrix or vector.
`n.threshold` | The number of bins into which to split the feature scores in the fit object returned internally by the `superpc.train` function to the `pathway_tScores` and `pathway_tControl` functions. Defaults to 20. Smaller values may result in less accurate pathway p-values while larger values increase computation time.
`numPCs` | The number of PCs to extract from each pathway. Defaults to 1.
`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 1.
`adjustpValues` | Should you adjust the p-values for multiple comparisons? Defaults to TRUE.
`adjustment` | Character vector of procedures. The returned data frame will be sorted in ascending order by the first procedure in this vector, with ties broken by the unadjusted p-value. If only one procedure is selected, then it is necessarily the first procedure. See the documentation for the `ControlFDR` function for the adjustment procedure definitions and citations.
`...` | Dots for additional internal arguments.

### Details

This is a wrapper function for the `pathway_tScores`, `pathway_tControl`, `OptimGumbelMixParams`, `GumbelMixpValues`, and `TabulatepValues` functions.

Please see our Quickstart Guide for this package: https://gabrielodom.github.io/pathwayPCA/articles/Supplement1-Quickstart_Guide.html

### Value

A data frame with columns:

- `pathways` : The names of the pathways in the `Omics*` object (given in `object@trimPathwayCollection$pathways`.)

- `setsize` : The number of genes in each of the original pathways (given in the `object@trimPathwayCollection$setsize` object).

- `terms` : The pathway description, as given in the `object@trimPathwayCollection$TERMS` object.

- `rawp` : The unadjusted p-values of each pathway.

- `...` : Additional columns as specified through the `adjustment` argument.

The data frame will be sorted in ascending order by the method specified first in the `adjustment` argument. If `adjustpValues = FALSE`, then the data frame will be sorted by the raw p-values. If you have the suggested `tidyverse` package suite loaded, then this data frame will print as a `tibble`. Otherwise, it will print as a data frame.

### See Also

`CreateOmics`; `TabulatepValues`; `pathway_tScores`; `pathway_tControl`; `OptimGumbelMixParams`; `GumbelMixpValues`; `clusterApply`

### Examples

      ###  Load the Example Data  ###
      data("colonSurv_df")
      data("colon_pathwayCollection")

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

      ###  Calculate Pathway p-Values  ###
      colonSurv_superpc <- SuperPCA_pVals(
        object = colon_OmicsSurv,
        parallel = TRUE,
        numCores = 2,
        adjustpValues = TRUE,
        adjustment = c("Hoch", "SidakSD")
      )

[Package pathwayPCA version 1.10.0 Index]