Description Usage Arguments Value Author(s) References Examples
Filter CpGs based on Intra-class Correlation Coefficients (ICCs). ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
1 | CpGFilterICC(dat, rep.design, REML = FALSE, logit.transform = TRUE, verbose = TRUE)
|
dat |
a matrix of CpG beta-values, row - CpG, column - sample |
rep.design |
a vector indicating the replicate design, it could be factor, character or numeric vectors. Example - c(1, 2, 3, 4, 4, 4, 5, 5) OR c('S1', 'S2', 'S2', 'S2', 'S1') |
REML |
If TRUE, Restricted Maximum Likelihood (REML) method will be used; Otherwise, Maximum Likelihood (ML) method will be used. Default is FALSE. |
logit.transform |
If TRUE, beta-value will be converted into M-value; Default is TRUE. |
verbose |
If TRUE, print run information |
ICCs for all probes
Jun Chen
Chen J, Just A, et al. CpGFilter:Model-based CpG probe filtering with replicates for epigenome-wide association studies (2016). Bioinformatics, 32(3): 469<e2><80><93>471
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