Description Usage Arguments Details Value Author(s)
Use minfi
-package to determine either differentially methylated positions (DMPs, dmpFinder
) and/or
differentially methylated regions (DMRs, bumphunter
).
1 2 3 |
GRset |
GenomicRatioSet or MethylSet |
projectfolder |
character with directory for output files (will be generated if not exisiting). |
projectname |
optional character prefix for output file names. |
phenotype |
character with phenotype for differential methylation analysis. Must be given within the
phenotype data of |
type.covar |
character with type of phenotype variable, either |
returnResults |
character indicating differentially methylated elements to return.
Either |
qCutoff |
numeric with q-value (FDR) threshold for DMPs to be reported by |
bumpcutoff |
numeric start value to find the appropriate |
nResamples |
numeric, number of resamples to use when computing null distributions with |
maxGap |
numeric with maximum location gap used to define clusters of probes for |
The dmpFinder
function from minfi
identifies differentially methylated CpG-sites using
linear regression for continuous phenotypes and F-test for categorical phenotypes. In case of a categorical
phenotype with many groups (e.g. experimental groups), F-test is applied over all groups.
Tests are performed on M values, which are logit transformed Beta values
(beta = Methylated allele intensity / (Unmethylated allele intensity + Methylated allele intensity + 100)).
Annotation data of GRset
is added to the result table.
Instead of looking for association between a single genomic location and a phenotype of interest,
bumphunter
looks for genomic regions that are differentially methylated (beta values). In the context of the 450k array,
the algorithm first defines clusters of probes such that two consecutive probe locations in the cluster
are not separated by more than distance mapGap
. Briefly, the algorithm first computes a t-statistic for beta values
at each genomic location, with optional smoothing. Then, it defines a candidate region to be a cluster of probes
for which all the t-statistics exceed a predefined threshold. To test for significance of the candidate regions,
the algorithm uses permutations given in nResamples
.
list with up to 2 elements depending on selection in returnResults
.
Result tables are also stored as side-effects in projectfolder
DMP
dataframe of CpG positions sorted by differential methylation p-value annotated by respective annotation package.
DMR
An object of class bumps
with the components: tab, coef, fitted, pvaluesMarginal, null, algorithm
.
See help(bumphunter) for details. The dataframe in tab
is annotated with meta data for all CpG-sites residing
within the respective DMR.
Frank Ruehle
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