An Interaction-Based Method for Refining Results From Gene Set Enrichment Analysis - Wang, Yishen; Hong, Yiwen; Mao, Shudi; Jiang, Yukang; Cui, Yamei; Pan, Jianying; Luo, Yan (Frontiers in Genetics, 2022)
Purpose: To demonstrate an interaction-based method for the refinement of GSEA results. Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Results: A total of 115140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d, Pkrcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of ‖d‖ to downstream genes among all frequent transactions. Conclusions: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.