varFilter {methylumi} | R Documentation

## Variation-based Filtering of Features (CpG sites) in a MethyLumiSet or MethyLumiM object

### Description

The function `varFilter` removes features exhibiting little variation across samples. Such non-specific filtering can be advantageous for downstream data analysis.

### Usage

    varFilter(eset, var.func=IQR, var.cutoff=0.5, filterByQuantile=TRUE, ...)

### Arguments

`eset` | An `MethyLumiSet` or `MethyLumiM` object.
`var.func` | The function used as the per-feature filtering statistics.
`var.cutoff` | A numeric value indicating the cutoff value for variation. If `filterByQuantile` is `TRUE`, features whose value of `var.func` is less than `var.cutoff`-quantile of all `var.func` value will be removed. It `FALSE`, features whose values are less than `var.cutoff` will be removed.
`filterByQuantile` | A logical indicating whether `var.cutoff` is to be interprested as a quantile of all `var.func` (the default), or as an absolute value.
`...` | Unused, but available for specializing methods.

### Details

This function is a counterpart of functions `nsFilter` and `varFilter` available from the `genefilter` package. See R. Bourgon et. al. (2010) and `nsFilter` for detail.

It is proven that non-specific filtering, for which the criteria does not depend on sample class, can increase the number of discoverie. Inappropriate choice of test statistics, however, might have adverse effect. `limma`'s moderated t-statistics, for example, is based on empirical Bayes approach which models the conjugate prior of gene-level variance with an inverse of χ^2 distribution scaled by observed global variance. As the variance-based filtering removes the set of genes with low variance, the scaled inverse χ^2 no longer provides a good fit to the data passing the filter, causing the `limma` algorithm to produce a posterior degree-of-freedom of infinity (Bourgon 2010). This leads to two consequences: (i) gene-level variance estimate will be ignore, and (ii) the p-value will be overly optimistic (Bourgon 2010).

### Value

The function `featureFilter` returns a list consisting of:

`eset` | The filtered `MethyLumiSet` or `MethyLumiM` object.
`filter.log` | Shows many low-variant features are removed.

### Author(s)

Chao-Jen Wong cwon2@fhcrc.org

### References

R. Bourgon, R. Gentleman, W. Huber, Independent filtering increases power for detecting differentially expressed genes, PNAS, vol. 107, no. 21, pp:9546-9551, 2010.

### See Also

`nsFilter`

### Examples

      data(mldat)
      ## keep top 75 percent
      filt <- varFilter(mldat, var.cutoff=0.25)
      filt$filter.log
      dim(filt$eset)

[Package methylumi version 2.30.0 Index]