AESPCA_pVals {pathwayPCA} | R Documentation

## Test pathway association with AES-PCA

### Description

Given a supervised `OmicsPath` object (one of `OmicsSurv`, `OmicsReg`, or `OmicsCateg`), extract the first k adaptive, elastic-net, sparse principal components (PCs) from each pathway-subset of the features in 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

    AESPCA_pVals(object, numPCs = 1, numReps = 0L, parallel = FALSE,
      numCores = NULL, asPCA = FALSE, adjustpValues = TRUE,
      adjustment = c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD",
      "BH", "BY", "ABH", "TSBH"), ...)

    ## S4 method for signature 'OmicsPathway'
    AESPCA_pVals(object, numPCs = 1,
      numReps = 1000, parallel = FALSE, numCores = NULL, asPCA = FALSE,
      adjustpValues = TRUE, adjustment = c("Bonferroni", "Holm",
      "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH"), ...)

### Arguments

`object` | An object of class `OmicsPathway` with a response matrix or vector.
`numPCs` | The number of PCs to extract from each pathway. Defaults to 1.
`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 1.
`asPCA` | Should the computation return the eigenvectors and eigenvalues instead of the adaptive, elastic-net, sparse principal components and their corresponding loadings. Defaults to `FALSE`; this should be used for diagnostic or comparative purposes only.
`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 `ExtractAESPCs`, `PermTestSurv`, `PermTestReg`, and `PermTestCateg` functions.

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

### Value

A results list with class `aespcOut`. This list has three components: a data frame of pathway details, pathway p-values, and potential adjustments to those values (`pVals_df`); a list of the first `numPCs` score vectors for each pathway (`PCs_ls`); and a list of the first `numPCs` feature loading vectors for each pathway (`loadings_ls`). The p-value data frame has 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).

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

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

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

- `...` : Additional columns of adjusted p-values 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`; `ExtractAESPCs`; `PermTestSurv`; `PermTestReg`; `PermTestCateg`; `TabulatepValues`; `clusterApply`

### Examples

      ###  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"
      )

      ###  Calculate Pathway p-Values  ###
      colonSurv_aespc <- AESPCA_pVals(
        object = colon_Omics,
        numReps = 0,
        parallel = TRUE,
        numCores = 2,
        adjustpValues = TRUE,
        adjustment = c("Hoch", "SidakSD")
      )

[Package pathwayPCA version 1.4.0 Index]