apply_snm: 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, ...)
## S3 method for class '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.

References

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

Examples

Run this code

# 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)