TY  - JOUR
AU  - Luo, Hengrui
AU  - Lin, Shili
PY  - 2018
DA  - 2018/12/10
TI  - Evaluation of Classical Statistical Methods for Analyzing BS-Seq Data
JO  - OBM Genetics
SP  - 053
VL  - 02
IS  - 04
AB  - DNA methylation is an epigenetic mark that is not only important in normal cell development, but also plays a significant role in human health and diseases. As such, studies of DNA methylation to understand its precise role in disease etiology and its potential as disease biomarkers have been actively pursued. One key issue in analyzing DNA methylation data is detection of significant differences in methylation levels between two diseased individuals and normal controls. In recent years, molecular technology has been developed to produce Bisulfite-sequencing (BS-Seq) data, which are of single-base resolution. For such data, methylation count at a single CpG site follows a binomial distribution, and the probability of this binomial distribution reflects the methylation level at this site. Traditional hypothesis testing methods, such as Fisher’s exact (FE) test, have been applied to detect differentially methylated cytosines (DMCs). 
Although the FE test is widely used, its “fixed margin” assumption has been called into question in such applications. Further, biological variability between samples within a group cannot be accounted for in FE test. Statistical tests that do not rely on such an assumption exist, including the computationally efficient Storer-Kim (SK) test. However, whether such methods would outperform the FE test for detecting DMCs is unknown, with or without the presence of within group variation. In this paper, we compare the performance of several traditional hypothesis testing methods from both statistical and biological perspectives and based on simulated and real data as well as theoretical analyses. Our results show that the unconditional SK test uniformly outperforms the conditional FE test. This advantage is especially noteworthy in an experiment that has limited sequencing depth.
SN  - 2577-5790
UR  - https://doi.org/10.21926/obm.genet.1804053
DO  - 10.21926/obm.genet.1804053
ID  - Luo2018
ER  -