apply_snm: Supervised normalization of data in edge

In StoreyLab/edge: Extraction of Differential Gene Expression

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)

StoreyLab/edge documentation built on May 9, 2019, 3:09 p.m.