Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease

References

• Abbas, A. R., Wolslegel, K., Seshasayee, D., Modrusan, Z., and Clark, H. F. (2009), “Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus,” PloS One, 4, e6098. DOI: 10.1371/journal.pone.0006098.
   PubMed Web of Science ®Google Scholar
• Alzheimer’s Association. (2019), “2019 Alzheimer’s Disease Facts and Figures,” Alzheimer’s & Dementia, 15, 321–387.
   Web of Science ®Google Scholar
• Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000), “Gene Ontology: Tool for the Unification of Biology,” Nature Genetics, 25, 25–29. DOI: 10.1038/75556.
   PubMed Web of Science ®Google Scholar
• Bennett, D. A., Buchman, A. S., Boyle, P. A., Barnes, L. L., Wilson, R. S., and Schneider, J. A. (2018), “Religious Orders Study and Rush Memory and Aging Project,” Journal of Alzheimer’s Disease, 64, S161–S189. DOI: 10.3233/JAD-179939.
   PubMed Web of Science ®Google Scholar
• Bickel, P. J., and Levina, E. (2008a), “Covariance Regularization by Thresholding,” The Annals of Statistics, 36, 2577–2604. DOI: 10.1214/08-AOS600.
   Web of Science ®Google Scholar
• ——- (2008b), “Regularized Estimation of Large Covariance Matrices,” The Annals of Statistics, 36, 199–227.
   Web of Science ®Google Scholar
• 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. DOI: 10.1198/jasa.2011.tm10155.
   Web of Science ®Google Scholar
• Cai, Z., and Xiao, M. (2016), “Oligodendrocytes and Alzheimer’s Disease,” International Journal of Neuroscience, 126, 97–104. DOI: 10.3109/00207454.2015.1025778.
   PubMed Web of Science ®Google Scholar
• Chu, T., Wang, Z., Pe’er, D., and Danko, C. G. (2022), “Cell Type and Gene Expression Deconvolution with BayesPrism Enables Bayesian Integrative Analysis Across Bulk and Single-Cell RNA Sequencing in Oncology,” Nature Cancer, 3, 505–517. DOI: 10.1038/s43018-022-00356-3.
   PubMedGoogle Scholar
• Cobos, F. A., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., and De Preter, K. (2020), “Benchmarking of Cell Type Deconvolution Pipelines for Transcriptomics Data,” Nature Communications, 11, 1–14. DOI: 10.1038/s41467-020-20288-9.
   PubMed Web of Science ®Google Scholar
• Consortium, T. G. O. (2021), “The Gene Ontology Resource: Enriching a GOld Mine,” Nucleic Acids Research, 49, D325–D334. DOI: 10.1093/nar/gkaa1113.
   PubMed Web of Science ®Google Scholar
• 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. DOI: 10.1093/nar/gkz172.
   PubMed Web of Science ®Google Scholar
• De Strooper, B., and Karran, E. (2016), “The Cellular Phase of Alzheimer’s Disease,” Cell, 164, 603–615. DOI: 10.1016/j.cell.2015.12.056.
   PubMed Web of Science ®Google Scholar
• Deming, Y., Filipello, F., Cignarella, F., Cantoni, C., Hsu, S., Mikesell, R., Li, Z., Del-Aguila, J. L., Dube, U., Farias, F. G., et al. (2019), “The MS4A Gene Cluster is a Key Modulator of Soluble TREM2 and Alzheimer’s Disease Risk,” Science Translational Medicine, 11, eaau2291. DOI: 10.1126/scitranslmed.aau2291.
   Web of Science ®Google Scholar
• Denisenko, E., Guo, B. B., Jones, M., Hou, R., De Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020), “Systematic Assessment of Tissue Dissociation and Storage Biases in Single-Cell and Single-Nucleus RNA-Seq Workflows,” Genome Biology, 21, 1–25. DOI: 10.1186/s13059-020-02048-6.
   PubMed Web of Science ®Google Scholar
• Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., and Jiang, Y. (2021), “SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References,” Briefings in Bioinformatics, 22, 416–427. DOI: 10.1093/bib/bbz166.
   PubMed Web of Science ®Google Scholar
• El Karoui, N. (2008), “Operator Norm Consistent Estimation of Large-Dimensional Sparse Covariance Matrices,” The Annals of Statistics, 36, 2717–2756. DOI: 10.1214/07-AOS559.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties,” Journal of the American statistical Association, 96, 1348–1360. DOI: 10.1198/016214501753382273.
   Web of Science ®Google Scholar
• Fujita, M., Gao, Z., Zeng, L., McCabe, C., White, C. C., Ng, B., Green, G. S., Rozenblatt-Rosen, O., Phillips, D., Amir-Zilberstein, L., et al. (2022), “Cell-Subtype Specific Effects of Genetic Variation in the Aging and Alzheimer Cortex,” bioRxiv, 2022–11.
   Google Scholar
• Gaiteri, C., Ding, Y., French, B., Tseng, G. C., and Sibille, E. (2014), “Beyond Modules and Hubs: The Potential of Gene Coexpression Networks for Investigating Molecular Mechanisms of Complex Brain Disorders,” Genes, Brain and Behavior, 13, 13–24. DOI: 10.1111/gbb.12106.
   PubMed Web of Science ®Google Scholar
• Griciuc, A., Serrano-Pozo, A., Parrado, A. R., Lesinski, A. N., Asselin, C. N., Mullin, K., Hooli, B., Choi, S. H., Hyman, B. T., and Tanzi, R. E. (2013), “Alzheimer’s Disease Risk Gene CD33 Inhibits Microglial Uptake of Amyloid Beta,” Neuron, 78, 631–643. DOI: 10.1016/j.neuron.2013.04.014.
   PubMed Web of Science ®Google Scholar
• Heintzman, N. D., Hon, G. C., Hawkins, R. D., Kheradpour, P., Stark, A., Harp, L. F., Ye, Z., Lee, L. K., Stuart, R. K., Ching, C. W., et al. (2009), “Histone Modifications at Human Enhancers Reflect Global Cell-Type-Specific Gene Expression,” Nature, 459, 108–112. DOI: 10.1038/nature07829.
   PubMed Web of Science ®Google Scholar
• Heneka, M. T., Carson, M. J., El Khoury, J., Landreth, G. E., Brosseron, F., Feinstein, D. L., Jacobs, A. H., Wyss-Coray, T., Vitorica, J., Ransohoff, R. M., et al. (2015), “Neuroinflammation in Alzheimer’s Disease,” The Lancet Neurology, 14, 388–405. DOI: 10.1016/S1474-4422(15)70016-5.
   PubMed Web of Science ®Google Scholar
• Huang, P., Cai, M., Lu, X., McKennan, C., and Wang, J. (2023), “Accurate Estimation of Rare Cell Type Fractions from Tissue Omics Data via Hierarchical Deconvolution,” bioRxiv, 2023–03.
   Google Scholar
• Hwang, B., Lee, J. H., and Bang, D. (2018), “Single-Cell RNA Sequencing Technologies and Bioinformatics Pipelines,” Experimental & Molecular Medicine, 50, 1–14. DOI: 10.1038/s12276-018-0071-8.
   Web of Science ®Google Scholar
• Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., and Halperin, E. (2020), “Accurate Estimation of Cell Composition in Bulk Expression through Robust Integration of Single-Cell Information,” Nature Communications, 11, 1–11. DOI: 10.1038/s41467-020-16607-9.
   PubMed Web of Science ®Google Scholar
• Jiang, B. (2013), “Covariance Selection by Thresholding the Sample Correlation Matrix,” Statistics & Probability Letters, 83, 2492–2498. DOI: 10.1016/j.spl.2013.07.008.
   Web of Science ®Google Scholar
• Kiselev, V. Y., Andrews, T. S., and Hemberg, M. (2019), “Challenges in Unsupervised Clustering of Single-Cell RNA-Seq Data,” Nature Reviews Genetics, 20, 273–282. DOI: 10.1038/s41576-018-0088-9.
   PubMed Web of Science ®Google Scholar
• Kosoy, R., Fullard, J., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S., Shao, Z., Humphrey, J., de Paiva Lopes, K., et al. (2021), “Genetics of the Human Microglia Regulome Refines Alzheimer’s Disease Risk Loci,” medRxiv.
   Google Scholar
• Langfelder, P., and Horvath, S. (2008), “WGCNA: An R Package for Weighted Correlation Network Analysis,” BMC Bioinformatics, 9, 1–13. DOI: 10.1186/1471-2105-9-559.
   PubMed Web of Science ®Google Scholar
• Li, B., Severson, E., Pignon, J.-C., Zhao, H., Li, T., Novak, J., Jiang, P., Shen, H., Aster, J. C., Rodig, S., et al. (2016), “Comprehensive Analyses of Tumor Immunity: Implications for Cancer Immunotherapy,” Genome Biology, 17, 1–16. DOI: 10.1186/s13059-016-1028-7.
   PubMedGoogle Scholar
• Love, M. I., Huber, W., and Anders, S. (2014), “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2,” Genome Biology, 15, 1–21. DOI: 10.1186/s13059-014-0550-8.
   Web of Science ®Google Scholar
• Lu, S., and Keles, S. (2023), “Debiased Personalized Gene Coexpression Networks for Population-Scale scRNA-seq Data,” Genome Research, gr–277363. DOI: 10.1101/gr.277363.122.
   Google Scholar
• Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019), “Single-Cell Transcriptomic Analysis of Alzheimer’s Disease,” Nature, 570, 332–337. DOI: 10.1038/s41586-019-1195-2.
   PubMed Web of Science ®Google Scholar
• Meng, G., and Mei, H. (2019), “Transcriptional Dysregulation Study Reveals a Core Network Involving the Progression of Alzheimer’s Disease,” Frontiers in Aging Neuroscience, 11, 101. DOI: 10.3389/fnagi.2019.00101.
   PubMed Web of Science ®Google Scholar
• Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., and Swarup, V. (2021), “Single-Nucleus Chromatin Accessibility and Transcriptomic Characterization of Alzheimer’s Disease,” Nature Genetics, 53, 1143–1155. DOI: 10.1038/s41588-021-00894-z.
   PubMed Web of Science ®Google Scholar
• Mostafavi, S., Gaiteri, C., Sullivan, S. E., White, C. C., Tasaki, S., Xu, J., Taga, M., Klein, H.-U., Patrick, E., Komashko, V., et al. (2018), “A Molecular Network of the Aging Human Brain Provides Insights into the Pathology and Cognitive Decline of Alzheimer’s Disease,” Nature Neuroscience, 21, 811–819. DOI: 10.1038/s41593-018-0154-9.
   PubMed Web of Science ®Google Scholar
• Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., and Alizadeh, A. A. (2015), “Robust Enumeration of Cell Subsets from Tissue Expression Profiles,” Nature Methods, 12, 453–457. DOI: 10.1038/nmeth.3337.
   PubMed Web of Science ®Google Scholar
• Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D., et al. (2019), “Determining Cell Type Abundance and Expression from Bulk Tissues with Digital Cytometry,” Nature Biotechnology, 37, 773–782. DOI: 10.1038/s41587-019-0114-2.
   PubMed Web of Science ®Google Scholar
• Pimenova, A. A., Raj, T., and Goate, A. M. (2018), “Untangling Genetic Risk for Alzheimer’s Disease,” Biological Psychiatry, 83, 300–310. DOI: 10.1016/j.biopsych.2017.05.014.
   PubMed Web of Science ®Google Scholar
• Qi, H., and Sun, D. (2006), “A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix,” SIAM Journal on Matrix Analysis and Applications, 28, 360–385. DOI: 10.1137/050624509.
   Web of Science ®Google Scholar
• Rothman, A. J., Bickel, P. J., Levina, E., and Zhu, J. (2008), “Sparse Permutation Invariant Covariance Estimation,” Electronic Journal of Statistics, 2, 494–515. DOI: 10.1214/08-EJS176.
   Web of Science ®Google Scholar
• Rothman, A. J., Levina, E., and Zhu, J. (2009), “Generalized Thresholding of Large Covariance Matrices,” Journal of the American Statistical Association, 104, 177–186. DOI: 10.1198/jasa.2009.0101.
   Web of Science ®Google Scholar
• Sims, R., Hill, M., and Williams, J. (2020), “The Multiplex Model of the Genetics of Alzheimer’s Disease,” Nature Neuroscience, 23, 311–322. DOI: 10.1038/s41593-020-0599-5.
   PubMed Web of Science ®Google Scholar
• Sims, R., Van Der Lee, S. J., Naj, A. C., Bellenguez, C., Badarinarayan, N., Jakobsdottir, J., Kunkle, B. W., Boland, A., Raybould, R., Bis, J. C., et al. (2017), “Rare Coding Variants in PLCG2, ABI3, and TREM2 Implicate Microglial-Mediated Innate Immunity in Alzheimer’s Disease,” Nature Genetics, 49, 1373–1384. DOI: 10.1038/ng.3916.
   PubMed Web of Science ®Google Scholar
• Skene, N. G., and Grant, S. G. (2016), “Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment,” Frontiers in Neuroscience, 10, 16. DOI: 10.3389/fnins.2016.00016.
   PubMed Web of Science ®Google Scholar
• Stower, H. (2019), “Single-Cell Insights into Neurology,” Nature Medicine, 25, 1799–1799. DOI: 10.1038/s41591-019-0686-x.
   Web of Science ®Google Scholar
• Su, C., Xu, Z., Shan, X., Cai, B., Zhao, H., and Zhang, J. (2022), “Cell-Type-Specific Co-Expression Inference from Single Cell RNA-Sequencing Data,” Nature Communications, 14, 4846. DOI: 10.1038/s41467-023-40503-7.
   Web of Science ®Google Scholar
• Sun, T., Song, D., Li, W. V., and Li, J. J. (2021), “scDesign2: A Transparent Simulator that Generates High-Fidelity Single-Cell Gene Expression Count Data with Gene Correlations Captured,” Genome Biology, 22, 1–37. DOI: 10.1186/s13059-021-02367-2.
   PubMed Web of Science ®Google Scholar
• Sun, Y., and Vandenberghe, L. (2015), “Decomposition Methods for Sparse Matrix Nearness Problems,” SIAM Journal on Matrix Analysis and Applications, 36, 1691–1717. DOI: 10.1137/15M1011020.
   Web of Science ®Google Scholar
• Tang, D., Park, S., and Zhao, H. (2020), “NITUMID: Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution,” Bioinformatics, 36, 1344–1350. DOI: 10.1093/bioinformatics/btz748.
   PubMed Web of Science ®Google Scholar
• Tansey, K. E., Cameron, D., and Hill, M. J. (2018), “Genetic Risk for Alzheimer’s Disease is Concentrated in Specific Macrophage and Microglial Transcriptional Networks,” Genome Medicine, 10, 1–10. DOI: 10.1186/s13073-018-0523-8.
   PubMed Web of Science ®Google Scholar
• Tian, J., Wang, J., and Roeder, K. (2021), “ESCO: Single Cell Expression Simulation Incorporating Gene Co-Expression,” Bioinformatics, 37, 2374–2381. DOI: 10.1093/bioinformatics/btab116.
   PubMed Web of Science ®Google Scholar
• Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., Van Baren, M. J., Salzberg, S. L., Wold, B. J., and Pachter, L. (2010), “Transcript Assembly and Quantification by RNA-Seq Reveals Unannotated Transcripts and Isoform Switching During Cell Differentiation,” Nature Biotechnology, 28, 511–515. DOI: 10.1038/nbt.1621.
   PubMed Web of Science ®Google Scholar
• Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020), “Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models,” Cell Reports, 32, 107908. DOI: 10.1016/j.celrep.2020.107908.
   PubMed Web of Science ®Google Scholar
• Wang, J., Roeder, K., and Devlin, B. (2021), “Bayesian Estimation of Cell Type-Specific Gene Expression with Prior Derived from Single-Cell Data,” Genome Research, gr–268722. DOI: 10.1101/gr.268722.120.
   Google Scholar
• Wang, M., Li, A., Sekiya, M., Beckmann, N. D., Quan, X., Schrode, N., Fernando, M. B., Yu, A., Zhu, L., Cao, J., et al. (2021a), “Transformative Network Modeling of Multi-Omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease,” Neuron, 109, 257–272. DOI: 10.1016/j.neuron.2020.11.002.
   PubMed Web of Science ®Google Scholar
• Wang, M., Roussos, P., McKenzie, A., Zhou, X., Kajiwara, Y., Brennand, K. J., De Luca, G. C., Crary, J. F., Casaccia, P., Buxbaum, J. D., et al. (2016), “Integrative Network Analysis of Nineteen Brain Regions Identifies Molecular Signatures and Networks Underlying Selective Regional Vulnerability to Alzheimer’s Disease,” Genome Medicine, 8, 1–21. DOI: 10.1186/s13073-016-0355-3.
   PubMedGoogle Scholar
• Wang, W., Yao, J., Wang, Y., Zhang, C., Tao, W., Zou, J., and Ni, T. (2021b), “Improved Estimation of Cell Type-Specific Gene Expression through Deconvolution of Bulk Tissues with Matrix Completion,” bioRxiv, 2021–06.
   Google Scholar
• Wang, X., Choi, D., and Roeder, K. (2021), “Constructing Local Cell-Specific Networks from Single-Cell Data,” Proceedings of the National Academy of Sciences, 118, e2113178118. DOI: 10.1073/pnas.2113178118.
   PubMed Web of Science ®Google Scholar
• Wang, X., Park, J., Susztak, K., Zhang, N. R., and Li, M. (2019), “Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference,” Nature Communications, 10, 1–9. DOI: 10.1038/s41467-018-08023-x.
   PubMedGoogle Scholar
• Winblad, B., Amouyel, P., Andrieu, S., Ballard, C., Brayne, C., Brodaty, H., Cedazo-Minguez, A., Dubois, B., Edvardsson, D., Feldman, H., et al. (2016), “Defeating Alzheimer’s Disease and Other Dementias: A Priority for European Science and Society,” The Lancet Neurology, 15, 455–532. DOI: 10.1016/S1474-4422(16)00062-4.
   PubMed Web of Science ®Google Scholar
• Yamazaki, Y., Zhao, N., Caulfield, T. R., Liu, C.-C., and Bu, G. (2019), “Apolipoprotein E and Alzheimer Disease: Pathobiology and Targeting Strategies,” Nature Reviews Neurology, 15, 501–518. DOI: 10.1038/s41582-019-0228-7.
   PubMed Web of Science ®Google Scholar
• Yang, T., Alessandri-Haber, N., Fury, W., Schaner, M., Breese, R., LaCroix-Fralish, M., Kim, J., Adler, C., Macdonald, L. E., Atwal, G. S., et al. (2021), “AdRoit is An Accurate and Robust Method to Infer Complex Transcriptome Composition,” Communications Biology, 4, 1–14. DOI: 10.1038/s42003-021-02739-1.
   PubMed Web of Science ®Google Scholar
• Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013), “Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease,” Cell, 153, 707–720. DOI: 10.1016/j.cell.2013.03.030.
   PubMed Web of Science ®Google Scholar
• Zhang, B., and Horvath, S. (2005), “A General Framework for Weighted Gene Co-expression Network Analysis,” Statistical Applications in Genetics and Molecular Biology, 4. DOI: 10.2202/1544-6115.1128.
   Web of Science ®Google Scholar
• Zhong, Y., and Liu, Z. (2012), “Gene Expression Deconvolution in Linear Space,” Nature Methods, 9, 8–9. DOI: 10.1038/nmeth.1830.
   Web of Science ®Google Scholar