apply_sva: Estimate surrogate variables

In StoreyLab/edge: Extraction of Differential Gene Expression

Description

Runs sva on the null and full models in deSet. See sva for additional details.

Usage

apply_sva(object, ...)

## S4 method for signature 'deSet'
apply_sva(object, ...)

Arguments

object	S4 object: deSet
...	Additional arguments for sva

Value

deSet object where the surrogate variables estimated by sva are added to the full model and null model matrices.

Author(s)

John Storey, Jeffrey Leek, Andrew Bass

References

Leek JT, Storey JD (2007) Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet 3(9): e161. doi:10.1371/journal.pgen.0030161

Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences, 105: 18718- 18723.

See Also

deSet, odp and lrt

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)

# run surrogate variable analysis
de_sva <- apply_sva(de_obj)

# run odp/lrt with surrogate variables added
de_odp <- odp(de_sva, bs.its = 30)
summary(de_odp)

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