| Type: | Package |
| Title: | CpG Filtering Method Based on Intra-Class Correlation Coefficients |
| Version: | 1.1 |
| Date: | 2017-08-23 |
| Author: | Jun Chen <Chen.Jun2@mayo.edu> |
| Maintainer: | Jun Chen <chen.jun2@mayo.edu> |
| Description: | Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. 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. |
| License: | GPL-3 |
| Depends: | R (≥ 3.1.0) |
| Imports: | stats, matrixStats |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2017-08-23 21:59:33 UTC; m123485 |
| Repository: | CRAN |
| Date/Publication: | 2017-08-23 22:09:07 UTC |
CpG filtering method based on intra-class correlation coefficients.
Description
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.
Usage
CpGFilterICC(dat, rep.design, REML = FALSE, logit.transform = TRUE, verbose = TRUE)
Arguments
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 |
Value
ICCs for all probes
Author(s)
Jun Chen
References
Chen J, Just A, et al. CpGFilter:Model-based CpG probe filtering with replicates for epigenome-wide association studies (2016). Bioinformatics, 32(3): 469–471
Examples
require(CpGFilter)
# 10 samples replicated twice, 5 samples replicated four times.
rep.design <- c(1:100, 101:110, 101:110, 111:115, 111:115, 111:115, 111:115)
rho <- CpGFilterICC(matrix(rnorm(140*1000), 1000, 140), rep.design, logit=FALSE)