SFnormalize {SigFuge} | R Documentation

## SigFuge normalize read counts

### Description

Function for normalizing read count data as specified in the SigFuge method. The normalization procedure is applied prior to SigFuge clustering to remove the effect of sample-locus specific expression from the analysis. This allows the method to identify clusters based on expression patterns across the genomic locus. It is recommended to flag and remove low expression samples from the normalization and analysis since their shapes may be overwhelmed by noise. A threshold based method for identifying low expression samples is included in the function, but users may also specify their own flags for low expression samples.

### Usage

      SFnormalize(data, flag = 1)

### Arguments

`data` | a d x n matrix of read counts at d positions for n samples.
`flag` | a n x 1 logical vector of samples flagged as low expression. If `flag == 1`, default low expression cutoffs are used. If `flag == 0`, no samples are flagged as low expression (equivalent to setting flag = zeros(n, 1)).

### Value

`SFnormalize` returns a list containing:

- data.norm a d x (n-m) matrix of normalized read counts where m is the number of low expression samples.

- flag a n x 1 logical vector of flagged samples.

### Author(s)

Patrick Kimes <pkimes@live.unc.edu>

### Examples

    data(geneDepth)
    depthnorm <- SFnormalize(geneDepth, flag = 1)

[Package SigFuge version 1.18.0 Index]