Open AccessDensity distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes
Author(s) -
Han-Ming Liu,
Dan Yang,
Zhao-Fa Liu,
Shengzhou Hu,
Yan Shen-hai,
Xian-Wen He
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0219551
Subject(s) - gene , expression (computer science) , gene expression , probability distribution , noise (video) , computational biology , statistical analysis , statistics , normal distribution , adaptability , biology , probability density function , data mining , bioinformatics , mathematics , genetics , computer science , artificial intelligence , ecology , image (mathematics) , programming language
The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.