SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation

被引:1
|
作者
Jiang H. [1 ]
Wang Y. [1 ]
Yin C. [1 ]
Pan H. [2 ]
Chen L. [1 ]
Feng K. [1 ]
Chang Y. [1 ,3 ,4 ]
Sun H. [1 ,3 ,4 ]
机构
[1] School of Artificial Intelligence, Jilin University, Changchun
[2] College of Software, Jilin University, Changchun
[3] International Center of Future Science, Jilin University, Changchun
[4] Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE
基金
中国国家自然科学基金;
关键词
Causal discovery; Directed acyclic graphs; Gene regulatory network; Large-scale network; Latent low-dimensional space;
D O I
10.1016/j.compbiomed.2024.108690
中图分类号
学科分类号
摘要
Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of β cells under high insulin demand. © 2024 Elsevier Ltd
引用
收藏
相关论文
共 50 条
  • [1] Reconstructing gene regulatory networks in single-cell transcriptomic data analysis
    Hao Dai
    Qi-Qi Jin
    Lin Li
    Luo-Nan Chen
    Zoological Research, 2020, 41 (06) : 599 - 604
  • [2] Reconstructing gene regulatory networks in single-cell transcriptomic data analysis
    Dai, Hao
    Jin, Qi-Qi
    Li, Lin
    Chen, Luo-Nan
    ZOOLOGICAL RESEARCH, 2020, 41 (06) : 599 - 604
  • [3] GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data
    Wang, Jia-Cheng
    Chen, Yao-Jia
    Zou, Quan
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 1 - 13
  • [4] Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN
    Gan, Yanglan
    Hu, Xin
    Zou, Guobing
    Yan, Cairong
    Xu, Guangwei
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [5] Inferring gene regulatory network from single-cell transcriptomic data by integrating multiple prior networks
    Gan, Yanglan
    Xin, Yongchang
    Hu, Xin
    Zou, Guobing
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2021, 93 (93)
  • [6] Reconstruction of gene regulatory networks from single cell transcriptomic data
    Rybakov, M. A.
    Omelyanchuk, N. A.
    Zemlyanskaya, E. V.
    VAVILOVSKII ZHURNAL GENETIKI I SELEKTSII, 2024, 28 (08): : 974 - 981
  • [7] A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data
    Zhao, Mengyuan
    He, Wenying
    Tang, Jijun
    Zou, Quan
    Guo, Fei
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (02)
  • [8] Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
    Aditya Pratapa
    Amogh P. Jalihal
    Jeffrey N. Law
    Aditya Bharadwaj
    T. M. Murali
    Nature Methods, 2020, 17 : 147 - 154
  • [9] Mapping gene regulatory networks from single-cell omics data
    Fiers, Mark W. E. J.
    Minnoye, Liesbeth
    Aibar, Sara
    Gonzalez-Blas, Carmen Bravo
    Atak, Zeynep Kalender
    Aerts, Stein
    BRIEFINGS IN FUNCTIONAL GENOMICS, 2018, 17 (04) : 246 - 254
  • [10] Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
    Pratapa, Aditya
    Jalihal, Amogh P.
    Law, Jeffrey N.
    Bharadwaj, Aditya
    Murali, T. M.
    NATURE METHODS, 2020, 17 (02) : 147 - +