TY  - JOUR
AU  - Lin, Shili
AU  - Cho, Min Ho
AU  - Gao, Yuan
AU  - Lou, Shuyuan
AU  - Han, Chenggong
AU  - Tang, Hancong
PY  - 2018
DA  - 2018/10/14
TI  - Evaluation of Recent Statistical Methods for Detecting Differential Methylation Using BS-seq Data
JO  - OBM Genetics
SP  - 041
VL  - 02
IS  - 04
AB  - Whole genome profiling of differential DNA methylation between diseased and normal samples has significant implications in research to understand the role of epigenetic regulations of cells. In recent years, the development of bisulfite sequencing (BS-seq) based molecular technology has enabled the measurement of DNA methylation at a nucleotide resolution throughout the genome. Given the availability of this new type of DNA methylation data with certain features challenging traditional analytical methods such as the Fisher exact rest, a number of new statistical methods have recently been proposed for analyzing them. However, the relative performances of many of these methods are still not fully understood, despite a couple of recent review papers. To arm researchers with the knowledge so that they can select the best suited method and software for analyzing their data, in this paper, we selected and evaluated four methods that are able to account for between-sample variability, a prominent feature of DNA methylation data. The commonality and differences of these methods in terms of several important characteristics, including modeling, data filtering, statistical testing, and covariates handling, were reviewed. Simulation studies as well as applications to two datasets were carried out to compare and contrast the methods. To be as unbiased as possible, our simulation modeling took into account characteristics of real data but did not use any of the specific models in these four methods.
SN  - 2577-5790
UR  - https://doi.org/10.21926/obm.genet.1804041
DO  - 10.21926/obm.genet.1804041
ID  - Lin2018
ER  -