odp {edge} | R Documentation

## The optimal discovery procedure

### Description

`odp` performs the optimal discovery procedure, which is a framework for optimally performing many hypothesis tests in a high-dimensional study. When testing whether a feature is significant, the optimal discovery procedure uses information across all features when testing for significance.

### Usage

    odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100,
      n.mods = 50, seed = NULL, verbose = TRUE, ...)

    ## S4 method for signature 'deSet,missing'
    odp(object, de.fit, odp.parms = NULL,
      weights = NULL, bs.its = 100, n.mods = 50, seed = NULL,
      verbose = TRUE, ...)

    ## S4 method for signature 'deSet,deFit'
    odp(object, de.fit, odp.parms = NULL,
      weights = NULL, bs.its = 100, n.mods = 50, seed = NULL,
      verbose = TRUE, ...)

### Arguments

`object` | `S4 object`: `deSet`
`de.fit` | `S4 object`: `deFit`. Optional.
`odp.parms` | `list`: parameters for each cluster. See `kl_clust`.
`weights` | `matrix`: weights for each observation. Default is NULL.
`bs.its` | `numeric`: number of null bootstrap iterations. Default is 100.
`n.mods` | `integer`: number of clusters used in `kl_clust`. Default is 50.
`seed` | `integer`: set the seed value. Default is NULL.
`verbose` | `boolean`: print iterations for bootstrap method. Default is TRUE.
`...` | Additional arguments for `qvalue` and `empPvals`.

### Details

The full ODP estimator computationally grows quadratically with respect to the number of genes. This becomes computationally taxing at a certain point. Therefore, an alternative method called mODP is used which has been shown to provide results that are very similar. mODP utilizes a clustering algorithm where genes are assigned to a cluster based on the Kullback-Leiber distance. Each gene is assigned an module-average parameter to calculate the ODP score and it reduces the computations time to approximately linear (see Woo, Leek and Storey 2010). If the number of clusters is equal to the number of genes then the original ODP is implemented. Depending on the number of hypothesis tests, this can take some time.

### Value

`deSet` object

### Author(s)

John Storey, Jeffrey Leek, Andrew Bass

### References

Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.

Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.

Woo S, Leek JT, Storey JD (2010) A computationally efficient modular optimal discovery procedure. Bioinformatics, 27(4): 509-515.

### See Also

`kl_clust`, `build_models` and `deSet`

### Examples

    # import data
    library(splines)
    data(kidney)
    age <- kidney$age
    sex <- kidney$sex
    kidexpr <- kidney$kidexpr
    cov <- data.frame(sex = sex, age = age)

    # create models
    null_model <- ~sex
    full_model <- ~sex + ns(age, df = 4)

    # create deSet object from data
    de_obj <- build_models(data = kidexpr, cov = cov,
    null.model = null_model, full.model = full_model)

    # odp method
    de_odp <- odp(de_obj, bs.its = 30)

    # input a deFit object or ODP parameters ... not necessary
    de_fit <- fit_models(de_obj, stat.type = "odp")
    de_clust <- kl_clust(de_obj, n.mods = 10)
    de_odp <- odp(de_obj, de.fit = de_fit, odp.parms = de_clust,
    bs.its = 30)

    # summarize object
    summary(de_odp)

[Package edge version 2.18.0 Index]