cpg.annotate: 450k probe annotation

In timpeters82/DMRcate-release: Illumina 450K methylation array spatial analysis methods

Description

Annotates a matrix of M-values (logit transform of beta) with weights (depending on analysis.type) and other relevant information including gene association.

Usage

cpg.annotate(object, annotation=c(array="IlluminaHumanMethylation450k",
                                      annotation="ilmn12.hg19"),
         analysis.type=c("differential", "variability"),
         design, contrasts=FALSE, cont.matrix=NULL, coef, ...)

Arguments

object	A matrix of M-values, with unique Illumina probe IDs as rownames and unique sample IDs as column names.
annotation	A vector describing the type of annotation to affix to object. Identical context to minfi, i.e. annotation <- annotation(minfiobject) where minfiobject is a [Genomic](Methyl|Ratio)Set). Default (ilmn12.hg) is recommended.
analysis.type	"differential" for dmrcate() to return DMRs and "variability" to return VMRs.
design	Study design matrix. Identical context to differential analysis pipeline in limma. Must have an intercept if contrasts=FALSE. Applies only when analysis.type="differential".
contrasts	Logical denoting whether a limma-style contrast matrix is specified.
cont.matrix	Limma-style contrast matrix for explicit contrasting. For each call to cpg.annotate, only one contrast will be fit.
coef	The column index in design corresponding to the phenotype comparison. Corresponds to the comparison of interest in design when contrasts=FALSE, otherwise must be a column name in cont.matrix. Applies only when analysis.type="differential".
...	Extra arguments passed to the limma function lmFit(). Applies only when analysis.type="differential".

Value

An object of class "annot", for passing to dmrcate, containing the vectors:

• ID: Illumina probe ID

• weights: t-statistic between phenotypes for each probe

• CHR: Chromosome which the probe maps to

• pos: hg19 position (on CHR) that the probe maps to

• gene: Matching UCSC_RefGene_Name

• group: Matching UCSC_RefGene_Group

• betafc: The beta fold change according to the given design

• indfdr: The post-kernel fitting limma fdr value

Author(s)

Tim J. Peters <[e-mail]>

References

Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420.

Peters T.J., Buckley M.J., Statham, A., Pidsley R., Samaras K., Lord R.V., Clark S.J. and Molloy P.L. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin 2015, 8:6, doi:10.1186/1756-8935-8-6.

Examples

## Not run: 
data(dmrcatedata)
myMs <- logit2(myBetas)
myMs.noSNPs <- rmSNPandCH(myMs, dist=2, mafcut=0.05)
patient <- factor(sub("-.*", "", colnames(myMs)))
type <- factor(sub(".*-", "", colnames(myMs)))
design <- model.matrix(~patient + type) 
myannotation <- cpg.annotate(myMs.noSNPs, analysis.type="differential",
    design=design, coef=39)
## End(Not run)

timpeters82/DMRcate-release documentation built on May 31, 2019, 2:29 p.m.