Phelan, A. L., Katz, R. & Gostin, L. O. The novel coronavirus originating in wuhan, china: challenges for global health governance. JAMA 323, 709–710. https://doi.org/10.1001/jama.2020.1097 (2020).
Fu, L. et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in china: A systematic review and meta-analysis. J. Infect. 80, 656–665. https://doi.org/10.1016/j.jinf.2020.03.041 (2020).
Williamson, E. J. et al. Factors associated with COVID-19-related death using opensafely. Nature 584, 430–436. https://doi.org/10.1038/s41586-020-2521-4 (2020).
Tavasolian, F. et al. Immune response, and susceptibility to COVID-19. Front. Immunol. 11, 601886. https://doi.org/10.3389/fimmu.2020.601886 (2020).
Schafer, A. & Baric, R. S. Epigenetic Landscape during Coronavirus Infection. Pathogens https://doi.org/10.3390/pathogens6010008 (2017).
Islam, A. et al. Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other SARS-CoV-2 infection models reveal disparate host responses against SARS-CoV-2. J. Transl Med. 19, 32. https://doi.org/10.1186/s12967-020-02695-0 (2021).
Baranova, A., Cao, H., Teng, S. & Zhang, F. A phenome-wide investigation of risk factors for severe COVID-19. J. Med. Virol. 95, e28264. https://doi.org/10.1002/jmv.28264 (2023).
Baranova, A., Cao, H. & Zhang, F. Causal associations and shared genetics between hypertension and COVID-19. J. Med. Virol. 95, e28698. https://doi.org/10.1002/jmv.28698 (2023).
Cao, H., Baranova, A., Wei, X., Wang, C. & Zhang, F. Bidirectional causal associations between type 2 diabetes and COVID-19. J. Med. Virol. 95, e28100. https://doi.org/10.1002/jmv.28100 (2023).
Polack, F. P. et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl. J. Med. 383, 2603–2615. https://doi.org/10.1056/NEJMoa2034577 (2020).
Voysey, M. et al. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in brazil, South africa, and the UK. Lancet 397, 99–111. https://doi.org/10.1016/S0140-6736(20)32661-1 (2021).
Kindler, E., Thiel, V. & SARS-CoV Too little, too late. Cell. Host Microbe. 19, 139–141. https://doi.org/10.1016/j.chom.2016.01.012 (2016).
Fehr, A. R., Channappanavar, R. & Perlman, S. Middle East respiratory syndrome: emergence of a pathogenic human coronavirus. Annu. Rev. Med. 68, 387–399. https://doi.org/10.1146/annurev-med-051215-031152 (2017).
Chen, Y., Liu, Q. & Guo, D. Emerging coronaviruses: genome structure, replication, and pathogenesis. J. Med. Virol. 92, 418–423. https://doi.org/10.1002/jmv.25681 (2020).
Channappanavar, R. et al. Dysregulated type I interferon and inflammatory Monocyte-Macrophage responses cause lethal pneumonia in SARS-CoV-Infected mice. Cell. Host Microbe. 19, 181–193. https://doi.org/10.1016/j.chom.2016.01.007 (2016).
Montazersaheb, S. et al. COVID-19 infection: an overview on cytokine storm and related interventions. Virol. J. 19, 92. https://doi.org/10.1186/s12985-022-01814-1 (2022).
de Wit, E., van Doremalen, N., Falzarano, D. & Munster, V. J. SARS and MERS: recent insights into emerging coronaviruses. Nat. Rev. Microbiol. 14, 523–534. https://doi.org/10.1038/nrmicro.2016.81 (2016).
Ramatillah, D. L. et al. Impact of cytokine storm on severity of COVID-19 disease in a private hospital in West Jakarta prior to vaccination. PLoS One. 17, e0262438. https://doi.org/10.1371/journal.pone.0262438 (2022).
Hojyo, S. et al. How COVID-19 induces cytokine storm with high mortality. Inflamm. Regen. 40, 37. https://doi.org/10.1186/s41232-020-00146-3 (2020).
Xiong, Y. et al. Transcriptomic characteristics of Bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients. Emerg. Microbes Infect. 9, 761–770. https://doi.org/10.1080/22221751.2020.1747363 (2020).
Ilieva, M., Tschaikowski, M., Vandin, A. & Uchida, S. The current status of gene expression profilings in COVID-19 patients. Clin. Transl Discov. 2, e104. https://doi.org/10.1002/ctd2.104 (2022).
Wargodsky, R. et al. RNA sequencing in COVID-19 patients identifies neutrophil activation biomarkers as a promising diagnostic platform for infections. PLoS One. 17, e0261679. https://doi.org/10.1371/journal.pone.0261679 (2022).
Jain, R. et al. Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes. Comput. Struct. Biotechnol. J. 19, 153–160. https://doi.org/10.1016/j.csbj.2020.12.016 (2021).
Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in wuhan, China. Lancet 395, 497–506. https://doi.org/10.1016/S0140-6736(20)30183-5 (2020).
Chen, N. et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: a descriptive study. Lancet 395, 507–513. https://doi.org/10.1016/S0140-6736(20)30211-7 (2020).
Xu, Z. et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 8, 420–422. https://doi.org/10.1016/S2213-2600(20)30076-X (2020).
Carabelli, A. M. et al. SARS-CoV-2 variant biology: immune escape, transmission and fitness. Nat. Rev. Microbiol. 21, 162–177. https://doi.org/10.1038/s41579-022-00841-7 (2023).
Herold, T. et al. Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19. J Allergy Clin Immunol 146, 128-136.e124. https://doi.org/10.1016/j.jaci.2020.05.008 (2020).
Luporini, R. L. et al. IL-6 and IL-10 are associated with disease severity and higher comorbidity in adults with COVID-19. Cytokine 143, 155507. https://doi.org/10.1016/j.cyto.2021.155507 (2021).
Santamaria, P. Cytokines and chemokines in autoimmune disease: an overview. Adv. Exp. Med. Biol. 520, 1–7. https://doi.org/10.1007/978-1-4615-0171-8_1 (2003).
Yang, B. et al. Novel function of Trim44 promotes an antiviral response by stabilizing VISA. J. Immunol. 190, 3613–3619. https://doi.org/10.4049/jimmunol.1202507 (2013).
Basu, A., Pal, D. & Blaydes, R. Differential effects of protein kinase C-eta on apoptosis versus senescence. Cell. Signal. 55, 1–7. https://doi.org/10.1016/j.cellsig.2018.12.003 (2019).
Liu, H. Y., Pedros, C., Kong, K. F., Canonigo-Balancio, A. J. & Altman, A. Protein kinase C-eta deficiency does not impair antiviral immunity and CD8(+) T cell activation. J. Immunol. 204, 2439–2446. https://doi.org/10.4049/jimmunol.1900963 (2020).
Renner, C. et al. RP1, a new member of the adenomatous polyposis coli-binding EB1-like gene family, is differentially expressed in activated T cells. J. Immunol. 159, 1276–1283 (1997).
Kerbrat, S. et al. Absence of the adaptor protein PEA-15 is associated with altered pattern of Th cytokines production by activated CD4 + T lymphocytes in vitro, and defective red blood cell alloimmune response in vivo. PLoS One. 10, e0136885. https://doi.org/10.1371/journal.pone.0136885 (2015).
Gorska, M. M., Stafford, S. J., Cen, O., Sur, S. & Alam, R. Unc119, a novel activator of lck/fyn, is essential for T cell activation. J. Exp. Med. 199, 369–379. https://doi.org/10.1084/jem.20030589 (2004).
Blank, C. U. et al. Defining ‘T cell exhaustion’. Nat. Rev. Immunol. 19, 665–674. https://doi.org/10.1038/s41577-019-0221-9 (2019).
Malissen, B., Gregoire, C., Malissen, M. & Roncagalli, R. Integrative biology of T cell activation. Nat. Immunol. 15, 790–797. https://doi.org/10.1038/ni.2959 (2014).
Peng, J., Zhu, X., Zhuang, W., Luo, H. & Wang, E. From sleep deprivation to severe COVID-19: A comprehensive analysis of shared differentially expressed genes and potential diagnostic biomarkers. Front. Biosci. (Landmark Ed). 29, 107. https://doi.org/10.31083/j.fbl2903107 (2024).
Berg, T. et al. Expression of MATE1, P-gp, OCTN1 and OCTN2, in epithelial and immune cells in the lung of COPD and healthy individuals. Respir Res. 19, 68. https://doi.org/10.1186/s12931-018-0760-9 (2018).
Saeki, K., Miura, Y., Aki, D., Kurosaki, T. & Yoshimura, A. The B cell-specific major raft protein, raftlin, is necessary for the integrity of lipid raft and BCR signal transduction. EMBO J. 22, 3015–3026. https://doi.org/10.1093/emboj/cdg293 (2003).
Rydyznski Moderbacher, C. et al. Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity. Cell 183, 996-1012.e1019. https://doi.org/10.1016/j.cell.2020.09.038 (2020).
Kalfaoglu, B., Almeida-Santos, J., Tye, C. A., Satou, Y. & Ono, M. T-Cell hyperactivation and paralysis in severe COVID-19 infection revealed by Single-Cell analysis. Front. Immunol. 11, 589380. https://doi.org/10.3389/fimmu.2020.589380 (2020).
Chang, H. et al. CD97 negatively regulates the innate immune response against RNA viruses by promoting RNF125-mediated RIG-I degradation. Cell. Mol. Immunol. 20, 1457–1471. https://doi.org/10.1038/s41423-023-01103-z (2023).
Zhou, Z. et al. Decreased HD-MIR2911 absorption in human subjects with the SIDT1 polymorphism fails to inhibit SARS-CoV-2 replication. Cell. Discov. 6, 63. https://doi.org/10.1038/s41421-020-00206-5 (2020).
Nguyen, T. A. et al. SIDT1 localizes to endolysosomes and mediates Double-Stranded RNA transport into the cytoplasm. J. Immunol. 202, 3483–3492. https://doi.org/10.4049/jimmunol.1801369 (2019).
Krummel, M. F. Immunological synapses: breaking up May be good to do. Cell 129, 653–655. https://doi.org/10.1016/j.cell.2007.05.008 (2007).
Banerjee, A. K. et al. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell 183, 1325-1339.e1321. https://doi.org/10.1016/j.cell.2020.10.004 (2020).
Agrawal, P., Sambaturu, N., Olgun, G., Hannenhalli, S. A. & Path-Based Analysis of infected cell line and COVID-19 patient transcriptome reveals novel potential targets and drugs against SARS-CoV-2. Front. Immunol. 13, 918817. https://doi.org/10.3389/fimmu.2022.918817 (2022).
Mohammadhosayni, M. et al. Matrix metalloproteinases are involved in the development of neurological complications in patients with coronavirus disease 2019. Int. Immunopharmacol. 100, 108076. https://doi.org/10.1016/j.intimp.2021.108076 (2021).
Sharif-Askari, S. Upregulation of oxidative stress gene markers during SARS-COV-2 viral infection. Free Radic Biol. Med. 172, 688–698. https://doi.org/10.1016/j.freeradbiomed.2021.06.018 (2021).
Dexiu, C., Xianying, L. & Yingchun, H. Jiafu, L. Advances in CD247. Scand. J. Immunol. 96, e13170. https://doi.org/10.1111/sji.13170 (2022).
Moni, M. A. & Lio, P. Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinform. 15, 333. https://doi.org/10.1186/1471-2105-15-333 (2014).
Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629. https://doi.org/10.1093/bioinformatics/btz931 (2020).
Jiang, Y. et al. Characterization of cytokine/chemokine profiles of severe acute respiratory syndrome. Am. J. Respir Crit. Care Med. 171, 850–857. https://doi.org/10.1164/rccm.200407-857OC (2005).
Alosaimi, B. et al. MERS-CoV infection is associated with downregulation of genes encoding Th1 and Th2 cytokines/chemokines and elevated inflammatory innate immune response in the lower respiratory tract. Cytokine 126, 154895. https://doi.org/10.1016/j.cyto.2019.154895 (2020).
Alakus, T. B. & Turkoglu, I. Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals. 140, 110120. https://doi.org/10.1016/j.chaos.2020.110120 (2020).
Dhar, S. K., Damodar, K. V., Gujar, S., Das, M. & S. & IL-6 and IL-10 as predictors of disease severity in COVID-19 patients: results from meta-analysis and regression. Heliyon 7, e06155. https://doi.org/10.1016/j.heliyon.2021.e06155 (2021).
Anwardeen, N. R. et al. The retrospective study of the metabolic patterns of BCG-vaccination in type-2 diabetic individuals in COVID-19 infection. Front. Immunol. 14, 1146443. https://doi.org/10.3389/fimmu.2023.1146443 (2023).
Cyprian, F. S. et al. Complement C5a and clinical markers as predictors of COVID-19 disease severity and mortality in a Multi-Ethnic population. Front. Immunol. 12, 707159. https://doi.org/10.3389/fimmu.2021.707159 (2021).
Shuaib, M. et al. Impact of the SARS-CoV-2 nucleocapsid 203K/204R mutations on the inflammatory immune response in COVID-19 severity. Genome Med. 15, 54. https://doi.org/10.1186/s13073-023-01208-0 (2023).
Yin, Z., Pascual, C. & Klionsky, D. J. Autophagy: machinery and regulation. Microb. Cell. 3, 588–596. https://doi.org/10.15698/mic2016.12.546 (2016).
Bonam, S. R., Bayry, J., Tschan, M. P. & Muller, S. Progress and Challenges in The Use of MAP1LC3 as a Legitimate Marker for Measuring Dynamic Autophagy In Vivo. Cells https://doi.org/10.3390/cells9051321 (2020).
Yu, C. et al. TMEM74, a lysosome and autophagosome protein, regulates autophagy. Biochem. Biophys. Res. Commun. 369, 622–629. https://doi.org/10.1016/j.bbrc.2008.02.055 (2008).
Ziegler-Heitbrock, L. et al. Nomenclature of monocytes and dendritic cells in blood. Blood 116, e74–80. https://doi.org/10.1182/blood-2010-02-258558 (2010).
Michel, T. et al. Human CD56bright NK cells: an update. J. Immunol. 196, 2923–2931. https://doi.org/10.4049/jimmunol.1502570 (2016).
Su, Y. et al. Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19. Cell 183, 1479-1495.e1420. https://doi.org/10.1016/j.cell.2020.10.037 (2020).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587.e3529. https://doi.org/10.1016/j.cell.2021.04.048 (2021).
Puhach, O., Meyer, B. & Eckerle, I. SARS-CoV-2 viral load and shedding kinetics. Nat. Rev. Microbiol. 21, 147–161. https://doi.org/10.1038/s41579-022-00822-w (2023).
Cevik, M. et al. SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis. Lancet Microbe. 2, e13–e22. https://doi.org/10.1016/S2666-5247(20)30172-5 (2021).
Leung, B. P. et al. A role for IL-18 in neutrophil activation. J. Immunol. 167, 2879–2886. https://doi.org/10.4049/jimmunol.167.5.2879 (2001).
Henderson, L. A. et al. On the alert for cytokine storm: immunopathology in COVID-19. Arthritis Rheumatol. 72, 1059–1063. https://doi.org/10.1002/art.41285 (2020).
Jamison, D. A. Jr. et al. A comprehensive SARS-CoV-2 and COVID-19 review, part 1: intracellular overdrive for SARS-CoV-2 infection. Eur. J. Hum. Genet. 30, 889–898. https://doi.org/10.1038/s41431-022-01108-8 (2022).
Daamen, A. R. et al. COVID-19 patients exhibit unique transcriptional signatures indicative of disease severity. Front. Immunol. 13, 989556. https://doi.org/10.3389/fimmu.2022.989556 (2022).
Thompson, R. C. et al. Molecular States during acute COVID-19 reveal distinct etiologies of long-term sequelae. Nat. Med. 29, 236–246. https://doi.org/10.1038/s41591-022-02107-4 (2023).
Juan Guardela, B. M. et al. 50-gene risk profiles in peripheral blood predict COVID-19 outcomes: A retrospective, multicenter cohort study. EBioMedicine 69, 103439. https://doi.org/10.1016/j.ebiom.2021.103439 (2021).
Lipman, D., Safo, S. E. & Chekouo, T. Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status. BMC Genom. 24, 319. https://doi.org/10.1186/s12864-023-09410-5 (2023).
Sette, A. & Crotty, S. Adaptive immunity to SARS-CoV-2 and COVID-19. Cell 184, 861–880. https://doi.org/10.1016/j.cell.2021.01.007 (2021).
Merad, M., Blish, C. A., Sallusto, F. & Iwasaki, A. The immunology and immunopathology of COVID-19. Science 375, 1122–1127. https://doi.org/10.1126/science.abm8108 (2022).
Grifoni, A. et al. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell 181, 1489-1501.e1415. https://doi.org/10.1016/j.cell.2020.05.015 (2020).
Kamanaka, M. et al. Memory/effector (CD45RB(lo)) CD4 T cells are controlled directly by IL-10 and cause IL-22-dependent intestinal pathology. J. Exp. Med. 208, 1027–1040. https://doi.org/10.1084/jem.20102149 (2011).
Crepaldi, L. et al. Up-regulation of IL-10R1 expression is required to render human neutrophils fully responsive to IL-10. J. Immunol. 167, 2312–2322. https://doi.org/10.4049/jimmunol.167.4.2312 (2001).
Chazen, G. D., Pereira, G. M., LeGros, G., Gillis, S. & Shevach, E. M. Interleukin 7 is a T-cell growth factor. Proc. Natl. Acad. Sci. U S A. 86, 5923–5927. https://doi.org/10.1073/pnas.86.15.5923 (1989).
Barata, J. T., Durum, S. K. & Seddon, B. Flip the coin: IL-7 and IL-7R in health and disease. Nat. Immunol. 20, 1584–1593. https://doi.org/10.1038/s41590-019-0479-x (2019).
Overmyer, K. A. et al. Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Syst 12, 23-40.e27. https://doi.org/10.1016/j.cels.2020.10.003 (2021).
Lodigiani, C. et al. Venous and arterial thromboembolic complications in COVID-19 patients admitted to an academic hospital in milan, Italy. Thromb. Res. 191, 9–14. https://doi.org/10.1016/j.thromres.2020.04.024 (2020).
Loo, J., Spittle, D. A. & Newnham, M. COVID-19, immunothrombosis and venous thromboembolism: biological mechanisms. Thorax 76, 412–420. https://doi.org/10.1136/thoraxjnl-2020-216243 (2021).
Othman, H. Y., Zaki, I. A. H., Isa, M. R., Ming, L. C. & Zulkifly, H. H. A systematic review of thromboembolic complications and outcomes in hospitalised COVID-19 patients. BMC Infect. Dis. 24, 484. https://doi.org/10.1186/s12879-024-09374-1 (2024).
Piazza, G. et al. Registry of arterial and venous thromboembolic complications in patients with COVID-19. J. Am. Coll. Cardiol. 76, 2060–2072. https://doi.org/10.1016/j.jacc.2020.08.070 (2020).
Mohamed, M. F. H. et al. Prevalence of venous thromboembolism in critically ill COVID-19 patients: systematic review and Meta-Analysis. Front. Cardiovasc. Med. 7, 598846. https://doi.org/10.3389/fcvm.2020.598846 (2020).
Burn, E. et al. Venous or arterial thrombosis and deaths among COVID-19 cases: a European network cohort study. Lancet Infect. Dis. 22, 1142–1152. https://doi.org/10.1016/S1473-3099(22)00223-7 (2022).
Klok, F. A. et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb. Res. 191, 145–147. https://doi.org/10.1016/j.thromres.2020.04.013 (2020).
Meisinger, C. et al. Elevated Plasma D-Dimer Concentrations in Adults after an Outpatient-Treated COVID-19 Infection. Viruses https://doi.org/10.3390/v14112441 (2022).
Al-Samkari, H. et al. COVID-19 and coagulation: bleeding and thrombotic manifestations of SARS-CoV-2 infection. Blood 136, 489–500. https://doi.org/10.1182/blood.2020006520 (2020).
Smilowitz, N. R. et al. C-reactive protein and clinical outcomes in patients with COVID-19. Eur. Heart J. 42, 2270–2279. https://doi.org/10.1093/eurheartj/ehaa1103 (2021).
Grint, D. J. et al. Severity of severe acute respiratory system coronavirus 2 (SARS-CoV-2) alpha variant (B.1.1.7) in England. Clin. Infect. Dis. 75, e1120–e1127. https://doi.org/10.1093/cid/ciab754 (2022).
Alefishat, E., Jelinek, H. F., Mousa, M., Tay, G. K. & Alsafar, H. S. Immune response to SARS-CoV-2 variants: A focus on severity, susceptibility, and preexisting immunity. J. Infect. Public. Health. 15, 277–288. https://doi.org/10.1016/j.jiph.2022.01.007 (2022).
Wolter, N. et al. Early assessment of the clinical severity of the SARS-CoV-2 Omicron variant in South africa: a data linkage study. Lancet 399, 437–446. https://doi.org/10.1016/S0140-6736(22)00017-4 (2022).
Monaco, G. et al. RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Rep 26, 1627-1640.e1627. https://doi.org/10.1016/j.celrep.2019.01.041 (2019).
Kalatskaya, I. et al. Revealing the immune cell subtype reconstitution profile in patients from the CLARITY study using Deconvolution algorithms after cladribine tablets treatment. Sci. Rep. 13, 8067. https://doi.org/10.1038/s41598-023-34384-5 (2023).
Baker, R. E. et al. Infectious disease in an era of global change. Nat. Rev. Microbiol. 20, 193–205. https://doi.org/10.1038/s41579-021-00639-z (2022).
Wiersinga, W. J. et al. Transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review. JAMA 324, 782–793. https://doi.org/10.1001/jama.2020.12839 (2020).
WHO. COVID-19: symptoms, (2023). https://www.who.int/westernpacific/emergencies/covid-19/information/asymptomatic-covid-19#:~:text=Symptoms%20of%20COVID%2D19%20can,should%20seek%20immediate%20medical%20attention>
Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. https://doi.org/10.1093/bioinformatics/btu638 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated Estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
Wu, T. et al. ClusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov. (Camb). 2, 100141. https://doi.org/10.1016/j.xinn.2021.100141 (2021).
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–462. https://doi.org/10.1093/nar/gkv1070 (2016).
Russo, P. S. T. et al. CEMiTool: a bioconductor package for performing comprehensive modular co-expression analyses. BMC Bioinform. 19, 56. https://doi.org/10.1186/s12859-018-2053-1 (2018).
Nadel, B. B. et al. The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data. Gigascience https://doi.org/10.1093/gigascience/giab002 (2021).