Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods - Jia, Xuan; Yin, ZhiXiang; Peng, Yu (Frontiers in Microbiology, 2023)
Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This paper is based on a study of a dataset containing data on gene expression on the X chromosome of patients with azoospermia, mild and severe oligozoospermia. In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes The interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways.