Description Usage Arguments Details Value
Perform SVA confounder analysis and association tests between the Surrogate Variables of the given input values and the phenotype data.
1 2 | sva_analysis(values, pdata, main_formula, null_formula = NULL,
vfilter = NULL, rgset = NULL)
|
values |
A matrix containing the data to analyze. Rows represent variables and columns samples. |
pdata |
A data.frame containing the phenotype information for the samples. |
main_formula |
A formula describing the main model of interest. |
null_formula |
A formula describing the null model of interest. |
vfilter |
The number of most variable rows to use in order to estimate the number of surrogate variables. |
rgset |
If not NULL, compute association with control probes data contained in the provided RGChannelSet. |
This function performs a series of association tests between the Surrogate Variables of the input values and the provided phenotype data.
If a RGChannelSet is given, it can also compute the association with the control probes. This implementation uses Linear models.
A list containing the number of Surrogate Variables (num_sv), the results of the confounding analysis tests (significance), and the Surrogate Variables obtained by SVA (surrogates).
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.