detectOutliers: Step 5: Detect (but do not remove) outliers Makes a...

In NeilPearson-Lilly/MethyLiution: MethyLiution: A QC Pipeline For Methylation Data

Description

This probably isn't something you want to run directly. Just use the runPipeline() function.

Usage

detectOutliers(begin, mset_file, outliers_file, logfile, max_plot, outdir,
  col.name = NULL, mset.lumi = NULL)

Arguments

begin	Step the pipeline begins at
mset_file	Path to file in which dataset object is stored between steps (auto-filled by runPipeline() function)
outliers_file	Path to outliers output file
logfile	Path to log file (auto-filled by runPipeline() function)
max_plot	Maximum number of plots to create at this step
outdir	Path to output directory
col.name	List of column names, generated by setColumnNames() (auto-filled by runPipeline() function)
mset.lumi	Microarray dataset object (auto-filled by runPipeline() function)

Value

list - mset.lumi dataset, breakdown of sub-assays

NeilPearson-Lilly/MethyLiution documentation built on May 21, 2019, 11:29 a.m.