cpg.annotate: Annotate Illumina CpGs with their chromosome position and...

In timpeters82/DMRcate-devel: Methylation array and sequencing spatial analysis methods

Annotate Illumina CpGs with their chromosome position and test statistic

Description

Annotate a matrix/GenomicRatioSet representing EPICv2, EPICv1 or 450K data with probe weights and chromosomal position. Provides replicate filtering and remapping functions for EPICv2 probes.

Usage

cpg.annotate(datatype = c("array", "sequencing"), object,  
             what = c("Beta", "M"), arraytype = c("EPICv2", "EPICv1", "EPIC", 
             "450K"), epicv2Remap = TRUE, epicv2Filter = c("mean", 
             "sensitivity", "precision", "random"), analysis.type = 
             c("differential", "variability", "ANOVA", "diffVar"), 
             design, contrasts = FALSE, cont.matrix = NULL, fdr = 0.05, coef, 
             varFitcoef = NULL, topVarcoef = NULL, ...) 

Arguments

datatype	Character string representing the type of data being analysed.
object	Either: - A matrix of M-values, with unique Illumina probe IDs as rownames and unique sample IDs as column names or, - A GenomicRatioSet, appropriately annotated.
what	Does the data matrix contain Beta or M-values? Not needed if object is a GenomicRatioSet.
arraytype	Is the data matrix sourced from EPIC or 450K data? Not needed if object is a GenomicRatioSet.
epicv2Remap	Logical indicating whether to remap 11,878 cross-hybridising EPICv2 probes to their more likely CpG target (see Peters et al. 2024).
epicv2Filter	Strategy for filtering probe replicates that map to the same CpG site. "mean" takes the mean of the available probes; "sensitivity" takes the available probe most sensitive to methylation change; "precision" either selects the available probe with the lowest variation from the consensus value (most precise), or takes the mean if that confers the lowest variation instead, "random" takes a single probe at random from each replicate group.
analysis.type	"differential" for dmrcate() to return DMRs; "variability" to return VMRs; "ANOVA" to return "whole experiment" DMRs, incorporating all possible contrasts from the design matrix using the moderated F-statistics; "diffVar" to return differentially variable methylated regions, using the missMethyl package to generate t-statistics.
design	Study design matrix. Identical context to differential analysis pipeline in limma. Must have an intercept if contrasts=FALSE. Applies only when analysis.type %in% c("differential", "ANOVA", "diffVar").
contrasts	Logical denoting whether a limma-style contrast matrix is specified. Only applicable when datatype="array" and analysis.type %in% c("differential", "diffVar").
cont.matrix	Limma-style contrast matrix for explicit contrasting. For each call to cpg.annotate, only one contrast will be fit. Only applicable when datatype="array" and analysis.type %in% c("differential", "diffVar").
fdr	FDR cutoff (Benjamini-Hochberg) for which CpG sites are individually called as significant. Used to index default thresholding in dmrcate(). Highly recommended as the primary thresholding parameter for calling DMRs. Not used when analysis.type == "variability".
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. Only applicable when analysis.type == "differential".
varFitcoef	The columns of the design matrix containing the comparisons to test for differential variability. If left NULL, will test all columns. Identical context to missMethyl::varFit(). Only applicable when analysis.type %in% "diffVar".
topVarcoef	Column number or column name specifying which coefficient of the linear model fit is of interest. It should be the same coefficient that the differential variability testing was performed on. Default is last column of fit object. Identical context to missMethyl::topVar(). Only applicable when analysis.type %in% "diffVar".
...	Extra arguments passed to the limma function lmFit() (analysis.type="differential").

Value

A CpGannotated-class.

Author(s)

Tim J. Peters <[e-mail]>

References

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.

Phipson, B., & Oshlack, A. (2014). DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging. Genome Biol, 15(9), 465.

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.

Peters, T.J., Meyer, B., Ryan, L., Achinger-Kawecka, J., Song, J., Campbell, E.M., Qu, W., Nair, S., Loi-Luu, P., Stricker, P., Lim, E., Stirzaker, C., Clark, S.J. and Pidsley, R. (2024). Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling. BMC Genomics, 25, 251. doi:10.1186/s12864-024-10027-5.

Examples

library(AnnotationHub)
ah <- AnnotationHub()
EPICv2manifest <- ah[["AH116484"]]
object <- minfi::logit2(matrix(rbeta(10000, 3, 1), 1000, 10))
rownames(object) <- sample(rownames(EPICv2manifest), 1000)
type <- rep(c("Ctrl", "Treat"), each=5)
design <- model.matrix(~type)
myannotation <- cpg.annotate("array", object, what = "M", arraytype = "EPICv2",
                             analysis.type="differential", design=design, coef=2)

timpeters82/DMRcate-devel documentation built on Oct. 2, 2024, 10:33 a.m.