mixedClusters {csaw} | R Documentation

## Tests for mixed DB clusters

### Description

Intersects two one-sided tests to determine if a cluster contains DB events in both directions.

### Usage

    mixedClusters(ids, tab, weight=NULL, pval.col=NULL, fc.col=NULL)

### Arguments

`ids` | an integer vector or factor containing the cluster ID for each test
`tab` | a dataframe of results with `PValue` and at least one `logFC` field for each test
`weight` | a numeric vector of weights for each window, defaults to 1 for each test
`pval.col` | an integer scalar or string specifying the column of `tab` containing the p-values
`fc.col` | an integer scalar or string specifying the columns of `tab` containing the log-fold changes

### Details

This function converts two-sided p-values to one-sided counterparts for each direction of log-fold change. For each direction, the corresponding one-sided p-values are combined to yield a combined p-value for each cluster. Each cluster is associated with two combined p-values (one in each direction), which are intersected using the Berger's intersection-union test (IUT).

The IUT p-value provides evidence against the null hypothesis that either direction is not significant. In short, a low p-value is only possible if there is DB in both directions. This formally identifies mixed clusters corresponding to complex DB events.

The two-sided p-values in `pval.col` should be independent of the sign of the log-fold change under the null hypothesis. This may not be true for all hypothesis tests, so caution is required when supplying values in `tab`.

### Value

A DataFrame containing one row per cluster, with various fields:

- A numeric field containing the IUT p-value for each cluster. This field is named `PValue` if `pval.col=NULL`, otherwise its name is set to `colnames(tab[,pval.col]`.

- A numeric field `FDR`, containing the adjusted p-value for each cluster.

All other fields are the same as those returned by `combineTests`. The exception is the `direction` field, which is not returned as the test is done explicitly for mixed clusters.

### Author(s)

Aaron Lun

### References

Berger RL and Hsu JC (1996). Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statist. Sci. 11, 283-319.

### See Also

`combineTests`

### Examples

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

[Package csaw version 1.18.0 Index]