DM.R: Run the pipeline
In ben-laufer/DMRichR: Enrich your DMR Analysis with the Tidyverse

View source: R/DM.R

DM.R	R Documentation

Run the pipeline

Description

Performs the entire DMRichR analysis pipeline, which runs most functions in the package.

Usage

DM.R(
  genome = c("hg38", "hg19", "mm10", "mm9", "rheMac10", "rheMac8", "rn6", "danRer11",
    "galGal6", "bosTau9", "panTro6", "dm6", "susScr11", "canFam3", "TAIR10", "TAIR9"),
  coverage = 1,
  perGroup = 0.75,
  minCpGs = 5,
  maxPerms = 10,
  maxBlockPerms = 10,
  cutoff = 0.05,
  testCovariate = testCovariate,
  adjustCovariate = NULL,
  matchCovariate = NULL,
  cores = 20,
  GOfuncR = TRUE,
  sexCheck = FALSE,
  EnsDb = FALSE
)

Arguments

`genome`	Character specifying the genome.
`coverage`	Numeric specifying the CpG coverage cutoff (1x recommended).
`perGroup`	Numeric indicating percent of samples per a group to apply the CpG coverage cutoff to (from 0 to 1).
`minCpGs`	Numeric for minimum number of CpGs for a DMR.
`maxPerms`	Numeric indicating number of permutations for the DMR analysis.
`maxBlockPerms`	Numeric indicating number of permutations for the block analysis.
`cutoff`	Numeric indicating the cutoff value for the single CpG coefficient utilized to discover testable background regions. Values range from 0 to 1 and 0.05 (5 percent) is the default. If you get too many DMRs you should try 0.1 (10 percent).
`testCovariate`	Character indicating factor of interest from the design matrix. Only case vs control is supported.
`adjustCovariate`	Character vector indicating discrete and continuous variables to adjust for based on the design matrix. Multiple variables can be provided.
`matchCovariate`	Character indicating the variable in the design matrix to block for when constructing permutations. Only a single variable can be provided and it cannot also be an adjustCovariate.
`cores`	Numeric specifying the number of cores to use. 20 is recommended.
`GOfuncR`	Logical indicating whether to run a GOfuncR GO analysis.
`sexCheck`	Logical indicating whether to confirm sex of each sample. This is highly recommended if your analysis has males and females and will also drop the sex chromosomes. You should also include the sex variable as an adjustCovariate.
`EnsDb`	Logical indicating whether to to select Ensembl transcript annotation database. This is recommended for non-model organisms.