varFilter: Variation-based Filtering of Features (CpG sites) in a...

In methylumi: Handle Illumina methylation data

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 [e-mail]

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)

methylumi documentation built on Nov. 8, 2020, 6:26 p.m.