apply_snm {edge} | R Documentation

## Supervised normalization of data in edge

### Description

Runs `snm` on a deSet object based on the null and full models in `deSet`. See `snm` for additional details on the algorithm.

### Usage

    apply_snm(object, int.var = NULL, ...)

    ## S4 method for signature 'deSet'
    apply_snm(object, int.var = NULL, ...)

### Arguments

`object` | `S4 object`: `deSet`
`int.var` | `data frame`: intensity-dependent effects (see `snm` for details)
`...` | Additional arguments for `snm`

### Value

`apply_snm` returns a `deSet` object where assayData (the expression data) that has been passed to apply_snm is replaced with the normalized data that `snm` returns. Specifically, `exprs(object)` is replaced by `$norm.dat` from `snm`, where `object` is the `deSet` object.

### Author(s)

John Storey, Andrew Bass

### References

Mechan BH, Nelson PS, Storey JD. Supervised normalization of microarrays. Bioinformatics 2010;26:1308-1315.

### See Also

`deSet`, `odp` and `lrt`

### Examples

    # simulate data
    library(snm)
    singleChannel <- sim.singleChannel(12345)
    data <- singleChannel$raw.data

    # create deSet object using build_models (can use ExpressionSet see manual)
    cov <- data.frame(grp = singleChannel$bio.var[,2])
    full_model <- ~grp
    null_model <- ~1

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

    # run snm using intensity-dependent adjustment variable
    de_snm <- apply_snm(de_obj, int.var = singleChannel$int.var,
    verbose = FALSE, num.iter = 1)

[Package edge version 2.19.0 Index]