minimalTests {csaw} | R Documentation

## Require rejection of a minimal number of tests

### Description

Compute a p-value for each cluster based around the rejection of a minimal number or proportion of tests from that cluster.

### Usage

    minimalTests(
      ids,
      tab,
      min.sig.n = 3,
      min.sig.prop = 0.4,
      weights = NULL,
      pval.col = NULL,
      fc.col = NULL,
      fc.threshold = 0.05
    )

### Arguments

`ids` | An integer vector or factor containing the cluster ID for each test.
`tab` | A data.frame of results with `PValue` and at least one `logFC` field for each test.
`min.sig.n` | Integer scalar containing the minimum number of significant barcodes when `method="holm-min"`.
`min.sig.prop` | Numeric scalar containing the minimum proportion of significant barcodes when `method="holm-min"`.
`weights` | A numeric vector of weights for each test. Defaults to 1 for all tests.
`pval.col` | An integer scalar or string specifying the column of `tab` containing the p-values. Defaults to `"PValue"`.
`fc.col` | An integer or character vector specifying the columns of `tab` containing the log-fold changes. Defaults to all columns in `tab` starting with `"logFC"`.
`fc.threshold` | A numeric scalar specifying the FDR threshold to use within each cluster for counting tests changing in each direction, see `?"cluster-direction"` for more details.

### Details

All tests with the same value of `ids` are used to define a single cluster. For each cluster, this function applies the Holm-Bonferroni correction to the p-values from all of its tests. It then chooses the xth-smallest adjusted p-value as the cluster-level p-value, where x is defined from the larger of `min.sig.n` and the product of `min.sig.prop` and the number of tests. (If x is larger than the total number of tests, the largest per-test p-value is used instead.)

Here, a cluster can only achieve a low p-value if at least x tests also have low p-values. This favors clusters that exhibit consistent changes across all tests, which is useful for detecting, e.g., systematic increases in binding across a broad genomic region spanning many windows. By comparison, `combineTests` will detect a strong change in a small subinterval of a large region, which may not be of interest in some circumstances.

The importance of each test within a cluster can be adjusted by supplying different relative `weights` values. This may be useful for downweighting low-confidence tests, e.g., those in repeat regions. In the weighted Holm procedure, weights are used to downscale the per-test p-values, effectively adjusting the distribution of per-test errors that contribute to family-wise errors. Note that these weights have no effect between clusters.

To obtain `ids`, a simple clustering approach for genomic windows is implemented in `mergeWindows`. However, anything can be used so long as it is independent of the p-values and does not compromise type I error control, e.g., promoters, gene bodies, independently called peaks. Any tests with `NA` values for `ids` will be ignored.

### Value

A DataFrame with one row per cluster and various fields:

- An integer field `num.tests`, specifying the total number of tests in each cluster.

- Two integer fields `num.up.*` and `num.down.*` for each log-FC column in `tab`, containing the number of tests with log-FCs significantly greater or less than 0, respectively. See `?"cluster-direction"` for more details.

- A numeric field containing the cluster-level p-value. If `pval.col=NULL`, this column is named `PValue`, otherwise its name is set to `colnames(tab[,pval.col])`.

- A numeric field `FDR`, containing the BH-adjusted cluster-level p-value.

- A character field `direction` (if `fc.col` is of length 1), specifying the dominant direction of change for tests in each cluster. See `?"cluster-direction"` for more details.

- One integer field `rep.test` containing the row index (for `tab`) of a representative test for each cluster. See `?"cluster-direction"` for more details.

- One numeric field `rep.*` for each log-FC column in `tab`, containing a representative log-fold change for the differential tests in the cluster. See `?"cluster-direction"` for more details.

Each row is named according to the ID of the corresponding cluster.

### Author(s)

Aaron Lun

### References

Holm S (1979). A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65-70.

### See Also

`groupedHolmMin`, which does the heavy lifting.

`combineTests` and `getBestTest`, for another method of combining p-values for each cluster.

`mergeWindows`, for one method of generating `ids`.

`glmQLFTest`, for one method of generating `tab`.

### Examples

    ids <- round(runif(100, 1, 10))
    tab <- data.frame(logFC=rnorm(100), logCPM=rnorm(100), PValue=rbeta(100, 1, 2))
    minimal <- minimalTests(ids, tab)
    head(minimal)

[Package csaw version 1.26.0 Index]