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Inferring Gene Regulatory Networks from Case Versus Control scRNA-seq Datasets

Madison Dautle1, Ruoyu Chen2, Shaoqiang Zhang3, Yong Chen1* 1 Department of Biological and Biomedical Sciences, Rowan University, NJ 08028, USA 2 Moorestown High School, Moorestown, NJ 08057, USA 3 College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China


Poster # 39


Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-se) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and especially none of them works on condition specific GRNs by directly using the paired datasets of case-versus-control experiments that are widely available in diverse biological and biomedical research projects. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. After constructing a gene co-differential expression network, scTIGER employs cell type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same cell type between prostatic and benign conditions, and two cell types within prostatic environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks of BDNF, CREB1 and MAPK4. Meanwhile, scTIGER is tested to be robust against high levels of dropout noise and has high speed in proceeding scRNA-seq data with large-scale cell numbers. The results demonstrate scTIGER's high performance in inferring GRNs and its applicability to general case-versus-control scRNA-seq datasets, enabling comprehensive understanding of how a single cell type modifies its regulatory patterns in response to environmental perturbations and how different cell types exhibit similar mechanisms within a specific condition.

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