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Inferring dynamic regulatory interaction graphs from time series data with perturbations (2306.07803v1)

Published 13 Jun 2023 in cs.LG

Abstract: Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for understanding and predicting system behavior. In this paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time graph attentions and graph neural ordinary differential equations (ODEs). RiTINI leverages time-lapse signals on a graph prior, as well as perturbations of signals at various nodes in order to effectively capture the dynamics of the underlying system. This approach is distinct from traditional causal inference networks, which are limited to inferring acyclic and static graphs. In contrast, RiTINI can infer cyclic, directed, and time-varying graphs, providing a more comprehensive and accurate representation of complex systems. The graph attention mechanism in RiTINI allows the model to adaptively focus on the most relevant interactions in time and space, while the graph neural ODEs enable continuous-time modeling of the system's dynamics. We evaluate RiTINI's performance on various simulated and real-world datasets, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.

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References (56)
  1. Discovering modulators of gene expression. Nucleic Acids Res., 38(17):5648–5656, September 2010.
  2. Partial directed coherence: a new concept in neural structure determination. Biological Cybernetics, 84(6):463–474, May 2001. ISSN 1432-0770. doi: 10.1007/PL00007990. URL https://doi.org/10.1007/PL00007990.
  3. Cocomac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics, 6:30, 2012. doi: 10.3389/fninf.2012.00030.
  4. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, 22(7):815–822, 2006.
  5. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology, 38(12):1408–1414, 2020. doi: 10.1038/s41587-020-0719-9.
  6. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol., 7(5):R36, May 2006.
  7. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput, 5:415–426, 2000.
  8. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics, 20(14):2242–2250, September 2004.
  9. Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst., 5(3):251–267.e3, September 2017.
  10. Neural ordinary differential equations. Advances in Neural Information Processing Systems, 31, 2018.
  11. DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations. Science Advances, 8(48):abq3745, November 2022. doi: 10.1126/sciadv.abq3745.
  12. Gene regulatory networks and the evolution of animal body plans. Science, 311(5762):796–800, February 2006.
  13. Latent convergent cross mapping. In International Conference on Learning Representations, 2021.
  14. Ongoing cortical activity at rest: Criticality, multistability, and ghost attractors. The Journal of Neuroscience, 32(9):3366–3375, 2012. doi: 10.1523/JNEUROSCI.2523-11.2012.
  15. Identification of optimal structural connectivity using functional connectivity and neural modeling. The Journal of Neuroscience, 34(23):7910–7916, 2014. doi: 10.1523/JNEUROSCI.4423-13.2014.
  16. SERGIO: a single-cell expression simulator guided by gene regulatory networks. Cell systems, 11(3):252–271.e11, September 2020. ISSN 2405-4712. doi: 10.1016/j.cels.2020.08.003. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530147/.
  17. Perturb-seq: Dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell, 167(7):1853–1866.e17, December 2016.
  18. Least angle regression. Ann. Stat., 32(2):407–499, April 2004.
  19. Granger causality revisited. NeuroImage, 81:446–465, 2013.
  20. Inferring genetic networks and identifying compound mode of action via expression profiling. Science, 301(5629):102–105, 2003.
  21. John Geweke. Measures of conditional linear dependence and feedback between time series. Journal of the American Statistical Association, 79(388):907–915, 1984.
  22. C. W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37(3):424–438, 1969a.
  23. C. W. J. Granger. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3):424–438, 1969b. ISSN 0012-9682. doi: 10.2307/1912791. URL https://www.jstor.org/stable/1912791. Publisher: [Wiley, Econometric Society].
  24. Tigress: Trustful inference of gene regulation using stability selection. BMC Systems Biology, 6(1), 2012.
  25. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55, February 1970.
  26. Robustification of the PC-Algorithm for Directed Acyclic Graphs. Journal of Computational and Graphical Statistics, 17(4):773–789, 2008. ISSN 1061-8600. URL https://www.jstor.org/stable/25651227. Publisher: [American Statistical Association, Taylor & Francis, Ltd., Institute of Mathematical Statistics, Interface Foundation of America].
  27. Dissecting cell identity via network inference and in silico gene perturbation. Nature, 614(7949):742–751, February 2023.
  28. Systems biology. conditional density-based analysis of T cell signaling in single-cell data. Science, 346(6213):1250689, November 2014.
  29. Edward Laurence and et al. Spectral dimension reduction of complex dynamical networks. Physical Review X, 9(1):011042, 2019. doi: 10.1103/PhysRevX.9.011042.
  30. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc. Natl. Acad. Sci. U. S. A., 103(50):19033–19038, December 2006.
  31. Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data. PLoS One, 4(8):e6522, August 2009.
  32. Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences, 107(14):6286–6291, 2010.
  33. Information-theoretic feature selection in microarray data using variable complementarity. IEEE Journal of Selected Topics in Signal Processing, 1(3):379–388, 2007.
  34. Leveraging chromatin accessibility for transcriptional regulatory network inference in T helper 17 cells. Genome Res., 29(3):449–463, March 2019.
  35. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Briefings in Bioinformatics, 22(3), 09 2020. ISSN 1477-4054. doi: 10.1093/bib/bbaa190. URL https://doi.org/10.1093/bib/bbaa190.
  36. Graph neural ordinary differential equations. arXiv preprint arXiv:2003.08063, 2020.
  37. Predicting network dynamics without requiring the knowledge of the interaction graph. Proceedings of the National Academy of Sciences, 119(44):e2205517119, November 2022. doi: 10.1073/pnas.2205517119. URL https://www.pnas.org/doi/10.1073/pnas.2205517119. Publisher: Proceedings of the National Academy of Sciences.
  38. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods, 17(2):147–154, 2020. doi: 10.1038/s41592-019-0690-6. URL https://doi.org/10.1038/s41592-019-0690-6.
  39. Universal differential equations for scientific machine learning. arXiv preprint arXiv:2001.04385, 2020.
  40. Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder. Entropy, 23(9):1204, September 2021. ISSN 1099-4300. doi: 10.3390/e23091204. URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8465687/.
  41. Thomas Schreiber. Measuring Information Transfer. Physical Review Letters, 85(2):461–464, July 2000. doi: 10.1103/PhysRevLett.85.461. URL https://link.aps.org/doi/10.1103/PhysRevLett.85.461. Publisher: American Physical Society.
  42. Directed functional connectivity using dynamic graphical models. bioRxiv, 2017.
  43. Information processing architecture of functionally defined clusters in the macaque cortex. Journal of Neuroscience, 32(50):17465–17476, 2012. doi: 10.1523/JNEUROSCI.2709-12.2012.
  44. Nest 3.4, February 2023. URL https://doi.org/10.5281/zenodo.6867800.
  45. Causation, Prediction, and Search. MIT press, 2nd edition, 2000.
  46. Detecting causality in complex ecosystems. science, 338(6106):496–500, 2012.
  47. Causal Network Inference by Optimal Causation Entropy. SIAM Journal on Applied Dynamical Systems, 14(1):73–106, January 2015. ISSN 1536-0040. doi: 10.1137/140956166. URL http://arxiv.org/abs/1401.7574. arXiv:1401.7574 [cs, math].
  48. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.
  49. TrajectoryNet: A dynamic optimal transport network for modeling cellular dynamics. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 9526–9536. PMLR, 13–18 Jul 2020. URL https://proceedings.mlr.press/v119/tong20a.html.
  50. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
  51. Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat. Biotechnol., 27(9):829–839, September 2009.
  52. Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1):1–24, January 1972. doi: 10.1016/S0006-3495(72)86068-5.
  53. Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data. Scientific Reports, 11(1):7817, April 2021. ISSN 2045-2322. doi: 10.1038/s41598-021-87316-6. URL https://www.nature.com/articles/s41598-021-87316-6. Number: 1 Publisher: Nature Publishing Group.
  54. IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal of Open Source Software, 4(34):1081, February 2019. ISSN 2475-9066. doi: 10.21105/joss.01081. URL https://joss.theoj.org/papers/10.21105/joss.01081.
  55. Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis. NeuroImage, 191:641–656, 2019.
  56. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301–320, 2005.
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