apply_snm {edge} | R Documentation |
Runs snm
on a deSet object based on the null and full models in
deSet
. See snm
for additional details
on the algorithm.
apply_snm(object, int.var = NULL, ...) ## S4 method for signature 'deSet' apply_snm(object, int.var = NULL, ...)
object |
|
int.var |
|
... |
Additional arguments for |
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 $norm.dat
from snm
,
where object
is the deSet
object.
John Storey, Andrew Bass
Mechan BH, Nelson PS, Storey JD. Supervised normalization of microarrays. Bioinformatics 2010;26:1308-1315.
# simulate data library(snm) singleChannel <- sim.singleChannel(12345) data <- singleChannel$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)