ControlFDR {pathwayPCA} | R Documentation

## Adjust p-values for simple multiple-testing procedures

### Description

This is a modification of the `mt.rawp2adjp` function from the Bioconductor package `multtest`. We fixed an error wherein selecting the `"TSBH"` option overwrote the results of any previous adjustment methods, and another error created when the `"BY"` and `"TSBH"` methods were called simultaneously. We did not write the original function. For more information, see https://www.bioconductor.org/packages/3.7/bioc/manuals/multtest/man/multtest.pdf.

### Usage

    ControlFDR(rawp, proc = c("BH", "BY", "ABH", "TSBH", "Bonferroni",
      "Holm", "Hochberg", "SidakSS", "SidakSD"), alpha = 0.05,
      na.rm = FALSE, as.multtest.out = FALSE)

### Arguments

`rawp` | A vector of raw (unadjusted) p-values for each hypothesis under consideration. These could be nominal p-values, for example, from t-tables, or permutation p-values.
`proc` | A vector of character strings containing the names of the multiple testing procedures for which adjusted p-values are to be computed. This vector should include any of the options listed in the "Details" Section. Adjusted p-values are computed for simple FWER- and FDR- controlling procedures based on a vector of raw (unadjusted) p-values. Defaults to `"BH"`.
`alpha` | A nominal Type-I error rate, or a vector of error rates, used for estimating the number of true null hypotheses in the two-stage Benjamini & Hochberg procedure (`"TSBH"`). Default is 0.05.
`na.rm` | An option for handling `NA` values in a list of raw p- values. If `FALSE`, the number of hypotheses considered is the length of the vector of raw p-values. Otherwise, if `TRUE`, the number of hypotheses is the number of raw p-values which were not `NA`s.
`as.multtest.out` | Should the output match the output from the `mt.rawp2adjp` function? If not, the output will match the input (a vector). Defaults to `FALSE`.

### Details

This function computes adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values. The procedures include the Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for strong control of the family-wise Type-I error rate (FWER), and the Benjamini & Hochberg (1995) and Benjamini & Yekutieli (2001) procedures for (strong) control of the false discovery rate (FDR). The less conservative adaptive Benjamini & Hochberg (2000) and two-stage Benjamini & Hochberg (2006) FDR-controlling procedures are also included.

The `proc` options are

- `"BH"` : Adjusted p-values for the Benjamini & Hochberg (1995) step-up FDR-controlling procedure (independent and positive regression dependent test statistics).

- `"BY"` : Adjusted p-values for the Benjamini & Yekutieli (2001) step-up FDR-controlling procedure (general dependency structures).

- `"ABH"` : Adjusted p-values for the adaptive Benjamini & Hochberg (2000) step-up FDR-controlling procedure. This method amends the original step-up procedure using an estimate of the number of true null hypotheses obtained from p-values. This method is not guaranteed to return finite values.

- `"TSBH"` : Adjusted p-values for the two-stage Benjamini & Hochberg (2006) step-up FDR-controlling procedure. This method amends the original step-up procedure using an estimate of the number of true null hypotheses obtained from a first-pass application of `"BH"`. The adjusted p-values are α- dependent, therefore α must be set in the function arguments when using this procedure.

- `"Bonferroni"` : Bonferroni single-step adjusted p- values for strong control of the FWER.

- `"Holm"` : Holm (1979) step-down adjusted p-values for strong control of the FWER.

- `"Hochberg"` : Hochberg (1988) step-up adjusted p- values for strong control of the FWER (for raw (unadjusted) p- values satisfying the Simes inequality).

- `"SidakSS"` : Sidak single-step adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).

- `"SidakSD"` : Sidak step-down adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).

### Value

A vector of the same length and order as `rawp`, unless the user specifies that the output should match the output from the `multtest` package. In that case, the use should specify `as.multtest.out = TRUE` and this function will return output identical to that of the `mt.rawp2adjp` function from package `multtest`. That output is as follows:

- `adjp` : A matrix of adjusted p-values, with rows corresponding to hypotheses and columns to multiple testing procedures. Hypotheses are sorted in increasing order of their raw (unadjusted) p-values.

- `index` : A vector of row indices, between 1 and `length(rawp)`, where rows are sorted according to their raw (unadjusted) p-values. To obtain the adjusted p-values in the original data order, use `adjp\[order(index),\]`.

- `h0.ABH` : The estimate of the number of true null hypotheses (as proposed by Benjamini & Hochberg (2000)) used when computing adjusted p-values for the `"ABH"` procedure (see Dudoit et al., 2007).

- `h0.TSBH` : The estimate (or vector of estimates) of the number of true null hypotheses (as proposed by Benjamini et al. (2006)) when computing adjusted p-values for the `"TSBH"` procedure (see Dudoit et al., 2007).

### Author(s)

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine

Yongchao Ge, yongchao.ge@mssm.edu

Houston Gilbert, http://www.stat.berkeley.edu/~houston

### See Also

`AESPCA_pVals` `SuperPCA_pVals`

### Examples

      # DO NOT CALL THIS FUNCTION DIRECTLY.
      # Call this function through AESPCA_pVals() or SuperPCA_pVals() instead.

    ## Not run:
      ###  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"
      )

      ###  Extract Pathway PCs and Loadings  ###
      colonPCs_ls <- ExtractAESPCs(
        object = colon_Omics,
        parallel = TRUE,
        numCores = 2
      )

      ###  Pathway p-Values  ###
      pVals <- PermTestSurv(
        OmicsSurv = colon_Omics,
        pathwayPCs_ls = colonPCs_ls$PCs,
        parallel = TRUE,
        numCores = 2
      )

      ###  Adjust p-Values  ###
      ControlFDR(rawp = pVals)

    ## End(Not run)

[Package pathwayPCA version 1.2.0 Index]