DaMiR.SV: Identification of Surrogate Variables

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/sv_identification.R

Description

This function returns a matrix of surrogate variables (sv) using the implementation by Chiesa-Piacentini or the sva method by Leek et al.

Usage

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DaMiR.SV(data, method = c("fve", "leek", "be"), th.fve = 0.95,
  second.var = NULL)

Arguments

data

A SummarizedExpression object

method

The method used to identify sv. If missing, the "fve" method will be selected. Otherwise the method "leek" or "be" should be choosen

th.fve

This argument sets the threshold of maximum fraction of variance explained (fve) to be used in conjunction with "fve" method; default is 0.95

second.var

A factor or a numeric vector corresponding to an additional variable to take into account during the sv identification. This variable together with 'class' in the data object will be used to design the model matrix (~ class + second.var)

Details

This function helps the user to identify the appropriate number of sv: it is possible to select a different strategy to be used by changing the option in method argument. Three methods are available:

Value

A matrix of sv. A plot with the sv identified by "fve" method is also returned. A red dot shows the maximum number of variables to be included for a specific "fve".

Author(s)

Mattia Chiesa, Luca Piacentini

References

Jeffrey T. Leek, W. Evan Johnson, Hilary S. Parker, Elana J. Fertig, Andrew E. Jaffe and John D. Storey (2016). sva: Surrogate Variable Analysis. R package version 3.22.0.

See Also

sva

Examples

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# use example data:
data(data_norm)
sv <- DaMiR.SV(data_norm, method = "fve", th.fve=0.95)

DaMiRseq documentation built on Nov. 8, 2020, 5:53 p.m.