Master of Science in Bioinformatics
Biological and Biomedical Sciences
College of Science & Mathematics
Yong Chen, Ph.D.
Committee Member 1
Benjamin Carone, Ph.D.
Committee Member 2
Chun Wu, Ph.D.
gene co-differential expression network, prostate cancer, remote memory formation, scRNA-seq
Gene regulatory networks; Deep learning (Machine learning)
Biomedical Informatics | Life Sciences | Medicine and Health Sciences
Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of prostate cancer cells and detected potential AR-mediated GRNs. Then, when applied to mouse neurons with and without fear memory and detected CREB-mediated GRNs. The results show scTIGER can be applied to general case-versus-control scRNA-seq datasets with high performance.
Dautle, Madison, "SCTIGER: A DEEP-LEARNING METHOD FOR INFERRING GENE REGULATORY NETWORKS FROM SINGLE-CELL GENE EXPRESSION DATA" (2023). Theses and Dissertations. 3155.