SuperPCA_pVals: Test pathways with Supervised PCA

Description Usage Arguments Details Value See Also Examples

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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:

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
  ###  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")
  )

pathwayPCA documentation built on Dec. 15, 2020, 6:14 p.m.