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A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data

BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbab568-bbab568页

作者： Zhao, Mengyuan He, Wenying Tang, Jijun Zou, Quan Guo, Fei Tianjin Univ Beijing Peoples R China Hubei Univ Technol Sch Artificial Intellingence Wuhan Peoples R China Univ South Carolina Columbia SC USA Univ Elect Sci & Technol China Chengdu Peoples R China Cent South Univ Changsha Peoples R China

Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the bulk expression data, single-cell transcriptomic data embody cell-to-cell variance and diverse biological information, such as tissue characteristics, transformation of cell types, etc. Inferring GRNs based on such data offers unprecedented advantages for making a profound study of cell phenotypes, revealing gene functions and exploring potential interactions. However, the high sparsity, noise and dropout events of single-cell transcriptomic data pose new challenges for regulation identification. We develop a hybrid deep learning framework for GRN inference from single-cell transcriptomic data, DGRNS, which encodes the raw data and fuses recurrent neural network and convolutional neural network (CNN) to train a model capable of distinguishing related gene pairs from unrelated gene pairs. To overcome the limitations of such datasets, it applies sliding windows to extract valuable features while preserving the direction of regulation. DGRNS is constructed as a deep learning model containing gated recurrent unit network for exploring time-dependent information and CNN for learning spatially related information. Our comprehensive and detailed comparative analysis on the dataset of mouse hematopoietic stem cells illustrates that DGRNS outperforms state-of-the-art methods. The networks inferred by DGRNS are about 16% higher than the area under the receiver operating characteristic curve of other unsupervised methods and 10% higher than the area under the precision recall curve of other supervised methods. Experiments on human datasets show the strong robustness and excellent generalization of DGRNS. By comparing the predictions with standard network, we discover a series of novel interactions which are proved to be true in som

关键词： gene regulatory network single-cell transcriptomic data network inference methods deep learning

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A comprehensive overview and critical evaluation of gene regulatory network inference technologies

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BRIEFINGS IN BIOINFORMATICS 2021年第5期22卷 bbab009-bbab009页

作者： Zhao, Mengyuan He, Wenying Tang, Jijun Zou, Quan Guo, Fei Tianjin Univ Tianjin Peoples R China Univ South Carolina Columbia SC 29208 USA Univ Elect Sci & Technol China Chengdu Peoples R China

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

关键词： gene regulatory network gene expression data network inference methods machine learning

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