References
- Allen, G. I., and Liu, Z. (2013), “A Local Poisson Graphical Model for Inferring Networks From Sequencing Data,” IEEE Transactions on Nanobioscience 12, 189–198. DOI: https://doi.org/10.1109/TNB.2013.2263838.
- Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone, G. J., Gormley, P., Malik, R., et al. (2018), “Analysis of Shared Heritability in Common Disorders of the Brain,” Science 360, eaap8757.
- Arnold, B. C., and Press, S. J. (1989), “Compatible Conditional Distributions,” Journal of the American Statistical Association, 84, 152–156.
- Athanas, K. M., Mauney, S. L., and Woo, T.-U. W. (2015), “Increased Extracellular Clusterin in the Prefrontal Cortex in Schizophrenia,” Schizophrenia Research, 169, 381–385. DOI: https://doi.org/10.1016/j.schres.2015.10.002.
- Aubin-Frankowski, P.-C., and Vert, J.-P. (2018), “Gene Regulation Inference From Single-Cell RNA-Seq Data With Linear Differential Equations and Velocity Inference,” BioRxiv, 464–479.
- Besag, J. (1974), “Spatial Interaction and the Statistical Analysis of Lattice Systems,” Journal of the Royal Statistical Society, Series B, 36, 192–225.
- Browaeys, R., Saelens, W., and Saeys, Y. (2020), “NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes,” Nature Methods, 17, 159–162. DOI: https://doi.org/10.1038/s41592-019-0667-5.
- Cai, T., Liu, W., and Luo, X. (2011), “A Constrained l1 Minimization Approach to Sparse Precision Matrix Estimation,” Journal of the American Statistical Association, 106, 594–607.
- Cattane, N., Richetto, J., and Cattaneo, A. (2018), “Prenatal Exposure to Environmental Insults and Enhanced Risk of Developing Schizophrenia and Autism Spectrum Disorder: Focus on Biological Pathways and Epigenetic Mechanisms,” Neuroscience & Biobehavioral Reviews, 117, 253–278.
- Cerami, E. G., Gross, B. E., Demir, E., Rodchenkov, I., Babur, Ö., Anwar, N., Schultz, N., Bader, G. D., and Sander, C. (2010), “Pathway Commons, A Web Resource for Biological Pathway Data,” Nucleic Acids Research, 39, D685–D690. DOI: https://doi.org/10.1093/nar/gkq1039.
- Chen, J., and Chen, Z. (2008), “Extended Bayesian Information Criteria for Model Selection With Large Model Spaces,” Biometrika, 95, 759–771.
- Chen, S., Witten, D. M., and Shojaie, A. (2014), “Selection and Estimation for Mixed Graphical Models,” Biometrika, 102, 47–64. DOI: https://doi.org/10.1093/biomet/asu051.
- Chiquet, J., Mariadassou, M., and Robin, S. (2018), “Variational Inference for Sparse Network Reconstruction From Count Data,” arXiv: 1806.03120.
- Chiu, Y.-C., Hsiao, T.-H., Wang, L.-J., Chen, Y., and Shao, Y.-H. J. (2018), “scdNet: A Computational Tool for Single-Cell Differential Network Analysis,” BMC Systems Biology, 12, 124. DOI: https://doi.org/10.1186/s12918-018-0652-0.
- Choi, K., Chen, Y., Skelly, D. A., and Churchill, G. A. (2020), “Bayesian Model Selection Reveals Biological Origins of Zero Inflation in Single-Cell Transcriptomics,” bioRxiv.
- Choi, Y., Coram, M., Peng, J., and Tang, H. (2017), “A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-Seq Data,” Journal of Computational Biology, 24, 721–731. DOI: https://doi.org/10.1089/cmb.2017.0053.
- Cordero, P., and Stuart, J. M. (2017), “Tracing Co-Regulatory Network Dynamics in Noisy, Single-Cell Transcriptome Trajectories,” in Pacific Symposium on Biocomputing 2017, World Scientific, pp. 576–587. http://psb.stanford.edu/psb-online/proceedings/psb17/
- Costas, J., Carrera, N., Alonso, P., Gurriarán, X., Segalàs, C., Real, E., López-Solà, C., Mas, S., Gassó, P., Domènech, L., Morell, M., Quintela, I., Lázaro, L., Menchón, J. M., Estivill, X., and Carracedo, A.́ (2016), “Exon-Focused Genome-Wide Association Study of Obsessive-Compulsive Disorder and Shared Polygenic Risk With Schizophrenia,” Translational Psychiatry, 6, e768–e768. DOI: https://doi.org/10.1038/tp.2016.34.
- Dai, H., Li, L., Zeng, T., and Chen, L. (2019), “Cell-Specific Network Constructed by Single-Cell RNA Sequencing Data,” Nucleic Acids Research, 47, e62–e62. DOI: https://doi.org/10.1093/nar/gkz172.
- Danaher, P., Wang, P., and Witten, D. M. (2014), “The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes,” Journal of the Royal Statistical Society, Series B, 76, 373. DOI: https://doi.org/10.1111/rssb.12033.
- Ding, J., Aronow, B. J., Kaminski, N., Kitzmiller, J., Whitsett, J. A., and Bar-Joseph, Z. (2018), “Reconstructing Differentiation Networks and their Regulation From Time Series Single-Cell Expression Data,” Genome Research, 28, 383–395. DOI: https://doi.org/10.1101/gr.225979.117.
- Efremova, M., Vento-Tormo, M., Teichmann, S. A., and Vento-Tormo, R. (2020), “CellPhoneDB: Inferring Cell–Cell Communication From Combined Expression of Multi-Subunit Ligand–Receptor Complexes,” Nature Protocols, pp. 1–23.
- Friedman, J., Hastie, T., and Tibshirani, R. (2008), “Sparse Inverse Covariance Estimation With the Graphical Lasso,” Biostatistics, 9, 432–441. DOI: https://doi.org/10.1093/biostatistics/kxm045.
- Fukumoto, K., Tamada, K., Toya, T., Nishino, T., Yanagawa, Y., and Takumi, T. (2018), “Identification of Genes Regulating GABAergic Interneuron Maturation,” Neuroscience Research, 134, 18–29. DOI: https://doi.org/10.1016/j.neures.2017.11.010.
- Gan, L., Yang, X., Narisetty, N., and Liang, F. (2019), “Bayesian Joint Estimation of Multiple Graphical Models,” in Advances in Neural Information Processing Systems, Curran Associates, Inc. (Vol. 32), pp. 9802– 9812.
- Gierahn, T. M., Wadsworth II, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S., Love, J. C., and Shalek, A. K. (2017), “Seq-Well: Portable, Low-Cost RNA Sequencing of Single Cells at High Throughput,” Nature methods, 14, 395. DOI: https://doi.org/10.1038/nmeth.4179.
- Guo, J., Levina, E., Michailidis, G., and Zhu, J. (2011), “Joint Estimation of Multiple Graphical Models,” Biometrika, 98, 1–15. DOI: https://doi.org/10.1093/biomet/asq060.
- Inouye, D. I., Yang, E., Allen, G. I., and Ravikumar, P. (2017), “A Review of Multivariate Distributions for Count Data Derived From the Poisson Distribution,” Wiley Interdisciplinary Reviews: Computational Statistics, 9, e1398.
- Intosalmi, J., Mannerstrom, H., Hiltunen, S., and Lahdesmaki, H. (2018), “SCHiRM: Single Cell Hierarchical Regression Model to Detect Dependencies in Read Count Data,” bioRxiv, 335–695.
- Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019), “Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms Across Eight Psychiatric Disorders,” Cell, 179, 1469–1482. DOI: https://doi.org/10.1016/j.cell.2019.11.020.
- Li, L., Dai, H., Fang, Z., and Chen, L. (2020), “CCSN: Single Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network,” bioRxiv.
- Liu, H., Han, F., Yuan, M., Lafferty, J., Wasserman, L. (2012), “High-Dimensional Semiparametric Gaussian Copula Graphical Models,” The Annals of Statistics, 40, 2293–2326. DOI: https://doi.org/10.1214/12-AOS1037.
- Liu, H., Lafferty, J., and Wasserman, L. (2009), “The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs,” Journal of Machine Learning Research, 10, 2295–2328.
- Liu, J., Sun, W., and Liu, Y. (2019), “Joint Skeleton Estimation of Multiple Directed Acyclic Graphs for Heterogeneous Population,” Biometrics, 75, 36–47. DOI: https://doi.org/10.1111/biom.12941.
- Lord, C., Elsabbagh, M., Baird, G., and Veenstra-Vanderweele, J. (2018), “Autism Spectrum Disorder,” The Lancet, 392, 508–520. DOI: https://doi.org/10.1016/S0140-6736(18)31129-2.
- Mackrill, J. J. (2010), “Ryanodine Receptor Calcium Channels and Their Partners as Drug Targets,” Biochemical pharmacology, 79, 1535–1543. DOI: https://doi.org/10.1016/j.bcp.2010.01.014.
- Matsumoto, H., Kiryu, H., Furusawa, C., Ko, M. S., Ko, S. B., Gouda, N., Hayashi, T., and Nikaido, I. (2017), “SCODE: an Efficient Regulatory Network Inference Algorithm From Single-Cell RNA-Seq During Differentiation,” Bioinformatics, 33, 2314–2321. DOI: https://doi.org/10.1093/bioinformatics/btx194.
- McDavid, A., Gottardo, R., Simon, N., and Drton, M. (2019), “Graphical Models for Zero-Inflated Single Cell Gene Expression,” The Annals of Applied Statistics, 13, 848–873. DOI: https://doi.org/10.1214/18-AOAS1213.
- Meinshausen, N. and Bühlmann, P. (2006), “High-Dimensional Graphs and Variable Selection With the Lasso,” The Annals of Statistics, 34, 1436–1462. DOI: https://doi.org/10.1214/009053606000000281.
- Mohammadi, S., Davila-Velderrain, J., and Kellis, M. (2019), “Reconstruction of Cell-Type-Specific Interactomes at Single-Cell Resolution,” Cell Systems, 9, 559–568. DOI: https://doi.org/10.1016/j.cels.2019.10.007.
- Mohammadi, S., Ravindra, V., Gleich, D. F., and Grama, A. (2018), “A Geometric Approach to Characterize the Functional Identity of Single Cells,” Nature Communications, 9, 1–10. DOI: https://doi.org/10.1038/s41467-018-03933-2.
- Osorio, D., Zhong, Y., Li, G., Huang, J. Z., and Cai, J. J. (2020), “scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-Wide Gene Regulatory Networks From Single-Cell Data,” bioRxiv.
- Peng, H., Zeng, X., Zhou, Y., Zhang, D., Nussinov, R., and Cheng, F. (2019), “A Component Overlapping Attribute Clustering (COAC) Algorithm for Single-Cell RNA Sequencing Data Analysis and Potential Pathobiological Implications,” PLoS Computational Biology, 15, e1006772. DOI: https://doi.org/10.1371/journal.pcbi.1006772.
- Prata, J., Santos, S. G., Almeida, M. I., Coelho, R., and Barbosa, M. A. (2017), “Bridging Autism Spectrum Disorders and Schizophrenia Through Inflammation and Biomarkers-Pre-Clinical and Clinical Investigations,” Journal of Neuroinflammation, 14, 179. DOI: https://doi.org/10.1186/s12974-017-0938-y.
- Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., and Kannan, S. (2020), “Inferring Causal Gene Regulatory Networks From Coupled Single-Cell Expression Dynamics Using Scribe,” Cell Systems, 10, 265–274. DOI: https://doi.org/10.1016/j.cels.2020.02.003.
- Ren, X., Zhong, G., Zhang, Q., Zhang, L., Zhang, Z. (2020), “Reconstruction of Cell Spatial Organization Based on Ligand-Receptor Mediated Self-Assembly,” bioRxiv.
- Ruzzo, E. K., Pérez-Cano, L., Jung, J.-Y., Wang, L.-k., Kashef-Haghighi, D., Hartl, C., Singh, C., Xu, J., Hoekstra, J. N., Leventhal, O., Leppä, V. M., Gandal, M. J., Paskov, K., Stockham, N., Polioudakis, D., Lowe, J. K., Prober, D. A., Geschwind, D. H., Wall, D. P. (2019), “Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks,” Cell, 178, 850–866. DOI: https://doi.org/10.1016/j.cell.2019.07.015.
- Sanchez-Castillo, M., Blanco, D., Tienda-Luna, I. M., Carrion, M., and Huang, Y. (2018), “A Bayesian Framework for the Inference of Gene Regulatory Networks From Time and Pseudo-Time Series Data,” Bioinformatics, 34, 964–970. DOI: https://doi.org/10.1093/bioinformatics/btx605.
- Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., Stevens, C., Reichert, J., Mulhern, M. S., Artomov, M., Gerges, S., Sheppard, B., Xu, X., Bhaduri, A., Norman, U., Brand, H., Schwartz, G., Nguyen, R., Guerrero, E. E., Dias, C.; Autism Sequencing Consortium; iPSYCH-Broad Consortium, Betancur, C., Cook, E. H., Gallagher, L., Gill, M., Sutcliffe, J. S., Thurm, A., Zwick, M. E., Børglum, A. D., State, M. W., Cicek, A. E., Talkowski, M. E., Cutler, D. J., Devlin, B., Sanders, S. J., Roeder, K., Daly, M. J., Buxbaum, J. D. (2020), “Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism,” Cell, 180, 568–584. DOI: https://doi.org/10.1016/j.cell.2019.12.036.
- Sinclair, D., and Hooker, G. (2017), “Sparse Inverse Covariance Estimation for High-Throughput MicroRNA Sequencing Data in the Poisson Log-Normal Graphical Model,” arXiv: 1708.04490.
- Soueid, J., Kourtian, S., Makhoul, N. J., Makoukji, J., Haddad, S., Ghanem, S. S., Kobeissy, F., and Boustany, R.-M. (2016), “RYR2, PTDSS1 and AREG Genes are Implicated in a Lebanese Population-Based Study of Copy Number Variation in Autism,” Scientific Reports, 6, 1–11. DOI: https://doi.org/10.1038/srep19088.
- Sullivan, J. M., De Rubeis, S., and Schaefer, A. (2019), “Convergence of Spectrums: Neuronal Gene Network States in Autism Spectrum Disorder,” Current Opinion in Neurobiology, 59, 102–111. DOI: https://doi.org/10.1016/j.conb.2019.04.011.
- Svensson, V. (2020), “Droplet scRNA-Seq is not Zero-Inflated,” Nature Biotechnology, 38, 147–150. DOI: https://doi.org/10.1038/s41587-019-0379-5.
- Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., and Teichmann, S. A. (2017), “Power Analysis of Single-Cell RNA-Sequencing Experiments,” Nature Methods, 14, 381. DOI: https://doi.org/10.1038/nmeth.4220.
- Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016), “Dissecting the Multicellular Ecosystem of Metastatic Melanoma by Single-Cell RNA-Seq,” Science, 352, 189–196. DOI: https://doi.org/10.1126/science.aad0501.
- Velmeshev, D., Schirmer, L., Jung, D., Haeussler, M., Perez, Y., Mayer, S., Bhaduri, A., Goyal, N., Rowitch, D. H., and Kriegstein, A. R. (2019), “Single-Cell Genomics Identifies Cell Type–Specific Molecular Changes in Autism,” Science, 364, 685–689. DOI: https://doi.org/10.1126/science.aav8130.
- Wang, H., and Leng, C. (2007), “Unified Lasso Estimation by Least Squares Approximation,” Journal of the American Statistical Association, 102, 1039–1048. DOI: https://doi.org/10.1198/016214507000000509.
- Wang, Q., Chen, R., Cheng, F., Wei, Q., Ji, Y., Yang, H., Zhong, X., Tao, R., Wen, Z., Sutcliffe, J. S., Liu, C., Cook, E. H., Cox, N. J., and Li, B. (2019), “A Bayesian Framework that Integrates Multi-Omics Data and Gene Networks Predicts Risk Genes From Schizophrenia GWAS Data,” Nature Neuroscience, 22, 691–699. DOI: https://doi.org/10.1038/s41593-019-0382-7.
- Wang, Y. (2020), “Talklr Uncovers Ligand-Receptor Mediated Intercellular Crosstalk,” BioRxiv.
- Woodhouse, S., Piterman, N., Wintersteiger, C. M., Göttgens, B., and Fisher, J. (2018), “SCNS: A Graphical Tool for Reconstructing Executable Regulatory Networks From Single-Cell Genomic Data,” BMC Systems Biology, 12, 59. DOI: https://doi.org/10.1186/s12918-018-0581-y.
- Wu, H., Deng, X., and Ramakrishnan, N. (2018), “Sparse Estimation of Multivariate Poisson Log-Normal Models From Count Data,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 11, 66–77.
- Xie, Y., Liu, Y., and Valdar, W. (2016), “Joint Estimation of Multiple Dependent Gaussian Graphical Models With Applications to Mouse Genetics,” Biometrika, 103, 493–511. DOI: https://doi.org/10.1093/biomet/asw035.
- Yang, E., Allen, G., Liu, Z., and Ravikumar, P. K. (2012), “Graphical Models Via Generalized Linear Models,” in Advances in Neural Information Processing Systems, eds. F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Curran Associates, Inc. pp. 1358–1366.
- Yang, E., Ravikumar, P. K., Allen, G. I., and Liu, Z. (2013), “On Poisson Graphical Models,” in Advances in Neural Information Processing Systems, eds. C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Curran Associates, Inc. pp, 1718–1726.
- Yuan, M., and Lin, Y. (2007), “Model Selection and Estimation in the Gaussian Graphical Model,” Biometrika, 94, 19–35.
- Yuan, Y., and Bar-Joseph, Z. (2018), “CNNC: Convolutional Neural Networks for Co-Expression Analysis,” bioRxiv p. 365007.
- Zhang, M., Cui, Z., Neumann, M., and Chen, Y. (2018), “An End-To-End Deep Learning Architecture for Graph Classification,” in Thirty-Second AAAI Conference on Artificial Intelligence.
- Zhang, R., Ren, Z., and Chen, W. (2018), “SILGGM: An Extensive R Package for Efficient Statistical Inference in Large-Scale Gene Networks,” PLOS Computational Biology, 14, e1006369. DOI: https://doi.org/10.1371/journal.pcbi.1006369.
- Zheng, G. X., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017), “Massively Parallel Digital Transcriptional Profiling of Single Cells,” Nature Communications, 8, 14049. DOI: https://doi.org/10.1038/ncomms14049.
- Zhou, S. (2014), “Gemini: Graph Estimation With Matrix Variate Normal Instances,” The Annals of Statistics, 42, 532–562. DOI: https://doi.org/10.1214/13-AOS1187.
- Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., and Enard, W. (2017), “Comparative Analysis of Single-Cell RNA Sequencing Methods,” Molecular Cell, 65, 631–643. DOI: https://doi.org/10.1016/j.molcel.2017.01.023.