scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

被引:3
|
作者
Dautle, Madison [1 ]
Zhang, Shaoqiang [2 ]
Chen, Yong [1 ]
机构
[1] Rowan Univ, Dept Biol & Biomed Sci, Glassboro, NJ 08028 USA
[2] Tianjin Normal Univ, Coll Comp & Informat Engn, Tianjin 300387, Peoples R China
关键词
scRNA-seq; gene regulatory network; deep learning; gene co-differential expression network; memory formation; prostate cancer; LONG-TERM-MEMORY; PROSTATE-CANCER; MULTI-OMICS; INTEGRATION; INFERENCE;
D O I
10.3390/ijms241713339
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) 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 none infer GRNs by directly using the paired datasets of case-versus-control experiments. 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. 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-the-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 cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.
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页数:19
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