champ.refbase: Applying References-Base Methold to beta valued methylation...

In YuanTian1991/ChAMP: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

Applying References-Base Methold to beta valued methylation data.

Description

Applying References-Based Methold to correct cell-proportion in a methylation dataset. Reference-based method use purified whole blood cell-type specific methylation value to correct beta value dataset. Cell Proportions for each cell-type will be detected, and lm function will be used to correct beta value for 5 largest cell types. Cell type with smallest cell proportion will not be corrected.

Usage

    champ.refbase(beta=myNorm,
                  arraytype="450K")

Arguments

beta	whole blood beta methylation dataset user want to correct. (default = myNorm)
arraytype	There are two types of purified cell-type specific references can be chosen, "450K" and "27K". By default, 450K value will be used, but user may choose 27K as well. (default = myNorm)

Value

CorrectedBea	A beta valued matrix, with all value get corrected with RefBaseEWAS method. Be aware, champ.refbase will only correct top 5 cell types with largest mean cell proportions, and leave the cell with smallest mean cell proportion. User may check CellFraction result to find out which cell types are get corrected.
CellFraction	Proportion for each cell type.

Author(s)

Houseman EA, Yuan Tian, Andrew Teschendorff

References

Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, et al. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13: 86. doi: 10.1186/1471-2105-13-86. pmid:22568884

Examples

    ## Not run: 
        myLoad <- champ.load(directory=system.file("extdata",package="ChAMPdata"))
        myNorm <- champ.norm()
        myRefbase <- champ.refbase()
    
## End(Not run)

YuanTian1991/ChAMP documentation built on Feb. 21, 2023, 1:13 p.m.