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1. Raine T, Verstockt B, Kopylov U, Karmiris K, Goldberg R, Atreya R, et al. ECCO topical review: Refractory inflammatory bowel disease. Journal of Crohn’s and Colitis. 2021;15:1605–20.
2. Jairath V, Feagan BG. Global burden of inflammatory bowel disease. The Lancet Gastroenterology & Hepatology. 2020;5:2–3.
3. Burisch J, Munkholm P. The epidemiology of inflammatory bowel disease. Scandinavian Journal of Gastroenterology. 2015;50:942–51.
4. Human Microbiome Project Consortium BA, Nelson KE, Pop M, Creasy HH, Giglio MG, Huttenhower C, et al. A framework for human microbiome research. Nature. 2012;486:21521.
5. Shaw KA, Bertha M, Hofmekler T, Chopra P, Vatanen T, Srivatsa A, et al. Dysbiosis, inflammation, and response to treatment: A longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease. Genome Medicine. 2016;8:75.
6. Mukhopadhya I, Hansen R, El-Omar EM, Hold GL. IBDwhat role do Proteobacteria play? Nature Reviews Gastroenterology & Hepatology. 2012;9:219–30.
7. Zhang Y-Z, Li Y-Y. Inflammatory bowel disease: pathogenesis. World Journal of Gastroenterology. 2014;20:91–9.
8. Hugot J-P. Genetic origin of IBD. Inflammatory Bowel Diseases. 2004;10:S11–5.
9. Silva FAR, Rodrigues BL, Ayrizono M de LS, Leal RF. The Immunological Basis of Inflammatory Bowel Disease. Gastroenterology Research and Practice. 2016;2016:2097274.
10. de Mattos BRR, Garcia MPG, Nogueira JB, Paiatto LN, Albuquerque CG, Souza CL, et al. Inflammatory Bowel Disease: An Overview of Immune Mechanisms and Biological Treatments. Mediators of Inflammation. 2015;2015:493012.
11. McGovern DPB, Kugathasan S, Cho JH. Genetics of Inflammatory Bowel Diseases. Gastroenterology. 2015;149:1163–1176.e2.
12. Satsangi J, Silverberg MS, Vermeire S, Colombel J-F. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut. 2006;55:749–53.
13. Horowitz JE, Warner N, Staples J, Crowley E, Gosalia N, Murchie R, et al. Mutation spectrum of NOD2 reveals recessive inheritance as a main driver of early onset crohns disease. Scientific Reports. 2021;11:5595.
14. Kumar M, Garand M, Al Khodor S. Integrating omics for a better understanding of inflammatory bowel disease: A step towards personalized medicine. Journal of Translational Medicine. 2019;17:419.
15. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Hostmicrobe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491:119–24.
16. Momozawa Y, Dmitrieva J, Théâtre E, Deffontaine V, Rahmouni S, Charloteaux B, et al. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nature Communications. 2018;9.
17. Khanna S, Tosh PK. A Clinician’s Primer on the Role of the Microbiome in Human Health and Disease. Mayo Clinic Proceedings. 2014;89:107–14.
18. Swidsinski A, Ladhoff A, Pernthaler A, Swidsinski S, LoeningBaucke V, Ortner M, et al. Mucosal flora in inflammatory bowel disease. Gastroenterology. 2002;122:44–54.
19. Tamboli CP, Neut C, Desreumaux P, Colombel JF. Dysbiosis in inflammatory bowel disease. Gut. 2004;53:1–4.
20. Ott SJ. Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease. Gut. 2004;53:685–93.
21. Kostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: Current status and the future ahead. Gastroenterology. 2014;146:1489–99.
22. Sender R, Fuchs S, Milo R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLOS Biology. 2016;14:e1002533.
23. Sankarasubramanian J, Ahmad R, Avuthu N, Singh AB, Guda C. Gut microbiota and metabolic specificity in ulcerative colitis and crohn’s disease. Frontiers in Medicine. 2020;7.
24. Lopez-Siles M, Martinez-Medina M, Busquets D, Sabat-Mir M, Duncan SH, Flint HJ, et al. Mucosa-associated Faecalibacterium prausnitzii and Escherichia coli co-abundance can distinguish Irritable Bowel Syndrome and Inflammatory Bowel Disease phenotypes. International journal of medical microbiology: IJMM. 2014;304:464–75.
25. Ferrer-Picón E, Dotti I, Corraliza AM, Mayorgas A, Esteller M, Perales JC, et al. Intestinal Inflammation Modulates the Epithelial Response to Butyrate in Patients With Inflammatory Bowel Disease. Inflammatory Bowel Diseases. 2020;26:43–55.
26. Darfeuille-Michaud A, Neut C, Barnich N, Lederman E, Di Martino P, Desreumaux P, et al. Presence of adherent Escherichia coli strains in ileal mucosa of patients with Crohn’s disease. Gastroenterology. 1998;115:1405–13.
27. Tsilingiri K, Barbosa T, Penna G, Caprioli F, Sonzogni A, Viale G, et al. Probiotic and postbiotic activity in health and disease: comparison on a novel polarised ex-vivo organ culture model. Gut. 2012;61:1007–15.
28. Vanderpool C, Yan F, Polk BD. Mechanisms of probiotic action: Implications for therapeutic applications in inflammatory bowel diseases. Inflammatory Bowel Diseases. 2008;14:1585–96.
29. Isaacs K, Herfarth H. Role of probiotic therapy in IBD. Inflammatory Bowel Diseases. 2008;14:1597–605.
30. Plaza-Diaz J, Ruiz-Ojeda FJ, Gil-Campos M, Gil A. Mechanisms of action of probiotics. Advances in Nutrition. 2019;10:S49–66.
31. Morelli L, Capurso L. FAO/WHO guidelines on probiotics: 10 years later. Journal of Clinical Gastroenterology. 2012;46:S1.
32. Okumura R, Takeda K. Roles of intestinal epithelial cells in the maintenance of gut homeostasis. Experimental & Molecular Medicine. 2017;49:e338–8.
33. Faria AMC, Mucida D, McCafferty D-M, Tsuji NM, Verhasselt V. Tolerance and inflammation at the gut mucosa. Clinical & Developmental Immunology. 2012;2012:738475.
34. Hisamatsu T, Kanai T, Mikami Y, Yoneno K, Matsuoka K, Hibi T. Immune aspects of the pathogenesis of inflammatory bowel disease. Pharmacology & Therapeutics. 2013;137:283–97.
35. Michielan A, D’Incà R. Intestinal Permeability in Inflammatory Bowel Disease: Pathogenesis, Clinical Evaluation, and Therapy of Leaky Gut. Mediators of Inflammation. 2015;2015:628157.
36. Mayorgas A. Human Primary Organoid-Derived Epithelial Monolayers as a Novel Strategy for the Study of Adherent Invasive Escherichia coli pathogenicity and the effects of Postbiotics on Intestinal Epithelial Function. PhD thesis. 2021.
37. Neurath MF, Fuss I, Kelsall BL, Presky DH, Waegell W, Strober W. Experimental granulomatous colitis in mice is abrogated by induction of TGF-beta-mediated oral tolerance. Journal of Experimental Medicine. 1996;183:2605–16.
38. Corraliza AM, Ricart E, López-García A, Carme Masamunt M, Veny M, Esteller M, et al. Differences in Peripheral and Tissue Immune Cell Populations Following Haematopoietic Stem Cell Transplantation in Crohns Disease Patients. Journal of Crohn’s and Colitis. https://doi.org/10.1093/ecco-jcc/jjy203.
39. Strachan DP. Hay fever, hygiene, and household size. BMJ : British Medical Journal. 1989;299:1259–60.
40. Scudellari M. News feature: Cleaning up the hygiene hypothesis. Proceedings of the National Academy of Sciences of the United States of America. 2017;114:1433–6.
41. Thomas GAO, Rhodes J, Green JT. Inflammatory bowel disease and smokinga review. Official journal of the American College of Gastroenterology | ACG. 1998;93:144149.
42. Cornish JA, Tan E, Simillis C, Clark SK, Teare J, Tekkis PP. The risk of oral contraceptives in the etiology of inflammatory bowel disease: a meta-analysis. The American Journal of Gastroenterology. 2008;103:2394–400.
43. Kaufmann HJ, Taubin HL. Nonsteroidal anti-inflammatory drugs activate quiescent inflammatory bowel disease. Annals of Internal Medicine. 1987;107:513–6.
44. Bitton A, Dobkin PL, Edwardes MD, Sewitch MJ, Meddings JB, Rawal S, et al. Predicting relapse in Crohn’s disease: a biopsychosocial model. Gut. 2008;57:1386–92.
45. Baumgart DC, Sandborn WJ. Crohn’s disease. The Lancet. 2012;380:1590–605.
46. Khanna S, Shin A, Kelly CP. Management of clostridium difficile infection in inflammatory bowel disease: Expert review from the clinical practice updates committee of the AGA institute. Clinical Gastroenterology and Hepatology. 2017;15:166–74.
47. Guardiola J, Lobatón T, Cerrillo E, Ferreiro-Iglesias R, Gisbert JP, Domènech E, et al. Recommendations of the Spanish Working Group on Crohn’s Disease and Ulcerative Colitis (GETECCU) on the utility of the determination of faecal calprotectin in inflammatory bowel disease. Gastroenterología y Hepatología (English Edition). 2018;41:514–29.
48. Sands BE. Biomarkers of Inflammation in Inflammatory Bowel Disease. Gastroenterology. 2015;149:1275–1285.e2.
49. Corraliza Márquez A. Immune mechanisms involved in inducing remission in Crohns disease patients undergoing hematopoietic stem cell transplant. PhD thesis. 2019.
50. Bassolas Molina H. Resposta T específica contra antígens de la microbiota comensal en la malaltia de Crohn i inhibició de ROR?t com a estratègia terapèutica. PhD thesis. 2018.
51. Daperno M, D’Haens G, Van Assche G, Baert F, Bulois P, Maunoury V, et al. Development and validation of a new, simplified endoscopic activity score for crohn’s disease: The SES-CD. Gastrointestinal Endoscopy. 2004;60:505–12.
52. Best WR, Becktel JM, Singleton JW, Kern F. Development of a Crohn’s Disease Activity Index: National Cooperative Crohn’s Disease Study. Gastroenterology. 1976;70:439–44.
53. Bhattacharya A, Rao BB, Koutroubakis IE, Click B, Vargas EJ, Regueiro M, et al. Silent crohn’s disease predicts increased bowel damage during multiyear follow-up: The consequences of under-reporting active inflammation. Inflammatory Bowel Diseases. 2016;22:2665–71.
54. Peyrin-Biroulet L, Loftus EVJ, Colombel J-F, Sandborn WJ. The natural history of adult crohn’s disease in population-based cohorts. Official journal of the American College of Gastroenterology | ACG. 2010;105:289297.
55. Etchevers MJ, Aceituno M, García-Bosch O, Ordás I, Sans M, Ricart E, et al. Risk factors and characteristics of extent progression in ulcerative colitis. Inflammatory Bowel Diseases. 2009;15:1320–5.
56. Boonstra K, van Erpecum KJ, van Nieuwkerk KMJ, Drenth JPH, Poen AC, Witteman BJM, et al. Primary sclerosing cholangitis is associated with a distinct phenotype of inflammatory bowel disease. Inflammatory Bowel Diseases. 2012;18:2270–6.
57. Mark-Christensen A, Laurberg S, Haboubi N. Dysplasia in Inflammatory Bowel Disease: Historical Review, Critical Histopathological Analysis, and Clinical Implications. Inflammatory Bowel Diseases. 2018;24:1895–903.
58. Schieffer KM, Williams ED, Yochum GS, Koltun WA. Review article: the pathogenesis of pouchitis. Alimentary Pharmacology & Therapeutics. 2016;44:817–35.
59. Schroeder KW, Tremaine WJ, Ilstrup DM. Coated Oral 5-Aminosalicylic Acid Therapy for Mildly to Moderately Active Ulcerative Colitis. New England Journal of Medicine. 1987;317:1625–9.
60. Irvine EJ. Development and Subsequent Refinement of the Inflammatory Bowel Disease Questionnaire: A Quality-of-Life Instrument for Adult Patients with Inflammatory Bowel Disease. Journal of Pediatric Gastroenterology & Nutrition. 1999;28 Supplement:S23–7.
61. Travis SPL, Schnell D, Krzeski P, Abreu MT, Altman DG, Colombel J-F, et al. Developing an instrument to assess the endoscopic severity of ulcerative colitis: The ulcerative colitis endoscopic index of severity (UCEIS). Gut. 2012;61:535–42.
62. Travis SPL, Stange EF, Lémann M, Öresland T, Chowers Y, Forbes A, et al. European evidence based consensus on the diagnosis and management of Crohns disease: current management. Gut. 2006;55 suppl 1:i16–35.
63. Akobeng AK, Zhang D, Gordon M, MacDonald JK. Oral 5-aminosalicylic acid for maintenance of medically-induced remission in Crohn’s disease. Cochrane Database of Systematic Reviews. 2016. https://doi.org/10.1002/14651858.CD003715.pub3.
64. Feller M, Huwiler K, Schoepfer A, Shang A, Furrer H, Egger M. Long-term antibiotic treatment for crohn’s disease: Systematic review and meta-analysis of placebo-controlled trials. Clinical Infectious Diseases. 2010;50:473–80.
65. Prantera C, Scribano ML. Antibiotics and probiotics in inflammatory bowel disease: Why, when, and how. Current Opinion in Gastroenterology. 2009;25:329333.
66. Rezaie A, Kuenzig ME, Benchimol EI, Griffiths AM, Otley AR, Steinhart AH, et al. Budesonide for induction of remission in Crohn’s disease. Cochrane Database of Systematic Reviews. 2015. https://doi.org/10.1002/14651858.CD000296.pub4.
67. Lichtenstein GR, Hanauer SB, Sandborn WJ, Gastroenterology TPPC of the AC of. Management of crohn’s disease in adults. Official journal of the American College of Gastroenterology | ACG. 2009;104:465483.
68. Ouellette AJ, Bevins CL. Paneth cell defensins and innate immunity of the small bowel. Inflammatory Bowel Diseases. 2001;7:43–50.
69. Warner B, Johnston E, Arenas-Hernandez M, Marinaki A, Irving P, Sanderson J. A practical guide to thiopurine prescribing and monitoring in IBD. Frontline Gastroenterology. 2018;9:10–5.
70. Chande N, Patton PH, Tsoulis DJ, Thomas BS, MacDonald JK. Azathioprine or 6-mercaptopurine for maintenance of remission in Crohn’s disease. Cochrane Database of Systematic Reviews. 2015. https://doi.org/10.1002/14651858.CD000067.pub3.
71. Gisbert JP, Linares PM, Mcnicholl AG, Maté J, Gomollón F. Meta-analysis: the efficacy of azathioprine and mercaptopurine in ulcerative colitis. Alimentary Pharmacology & Therapeutics. 2009;30:126–37.
72. Peyrin-Biroulet L, Lémann M. Review article: remission rates achievable by current therapies for inflammatory bowel disease. Alimentary Pharmacology & Therapeutics. 2011;33:870–9.
73. Billioud V, Sandborn WJ, Peyrin-Biroulet L. Loss of response and need for adalimumab dose intensification in crohn’s disease: A systematic review. Official journal of the American College of Gastroenterology | ACG. 2011;106:674684.
74. Hwang JM, Varma MG. Surgery for inflammatory bowel disease. World Journal of Gastroenterology : WJG. 2008;14:2678–90.
75. Gardiner KR, Dasari BVM. Operative Management of Small Bowel Crohn’s Disease. Surgical Clinics of North America. 2007;87:587–610.
76. Lewis RT, Maron DJ. Efficacy and complications of surgery for crohn’s disease. Gastroenterology & Hepatology. 2010;6:587–96.
77. Corraliza AM, Ricart E, López-García A, Carme Masamunt M, Veny M, Esteller M, et al. Differences in Peripheral and Tissue Immune Cell Populations Following Haematopoietic Stem Cell Transplantation in Crohns Disease Patients. Journal of Crohn’s and Colitis. https://doi.org/10.1093/ecco-jcc/jjy203.
78. Weingarden AR, Vaughn BP. Intestinal microbiota, fecal microbiota transplantation, and inflammatory bowel disease. Gut Microbes. 2017;8:238–52.
79. Beck LC, Granger CL, Masi AC, Stewart CJ. Use of omic technologies in early life gastrointestinal health and disease: From bench to bedside. Expert Review of Proteomics. 2021;18:247–59.
80. Häsler R, Sheibani-Tezerji R, Sinha A, Barann M, Rehman A, Esser D, et al. Uncoupling of mucosal gene regulation, mRNA splicing and adherent microbiota signatures in inflammatory bowel disease. Gut. 2017;66:2087–97.
81. Tang MS, Bowcutt R, Leung JM, Wolff MJ, Gundra UM, Hudesman D, et al. Integrated Analysis of Biopsies from Inflammatory Bowel Disease Patients Identifies SAA1 as a Link Between Mucosal Microbes with TH17 and TH22 Cells. Inflammatory Bowel Diseases. 2017;23:1544–54.
82. Hernández-Rocha C, Borowski K, Turpin W, Filice M, Nayeri S, Raygoza Garay JA, et al. Integrative analysis of colonic biopsies from inflammatory bowel disease patients identifies an interaction between microbial bile-acid inducible gene abundance and human angiopoietin-like 4 gene expression. Journal of Crohn’s and Colitis. 2021. https://doi.org/10.1093/ecco-jcc/jjab096.
83. Hu S, Vila AV, Gacesa R, Collij V, Stevens C, Fu JM, et al. Whole exome sequencing analyses reveal gene–microbiota interactions in the context of IBD. Gut. 2021;70:285–96.
84. Mayorgas A, Dotti I, Salas A. Microbial Metabolites, Postbiotics, and Intestinal Epithelial Function. Molecular Nutrition & Food Research. 2021;65:2000188.
85. Planell N, Lozano JJ, Mora-Buch R, Masamunt MC, Jimeno M, Ordás I, et al. Transcriptional analysis of the intestinal mucosa of patients with ulcerative colitis in remission reveals lasting epithelial cell alterations. Gut. 2013;62:967–76.
86. Leal RF, Planell N, Kajekar R, Lozano JJ, Ordás I, Dotti I, et al. Identification of inflammatory mediators in patients with Crohn’s disease unresponsive to anti-TNF? therapy. Gut. 2015;64:233–42.
87. Massimino L, Lamparelli LA, Houshyar Y, D’Alessio S, Peyrin-Biroulet L, Vetrano S, et al. The Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework. Nature Computational Science. 2021;1:511–5.
88. Knights D, Lassen KG, Xavier RJ. Advances in inflammatory bowel disease pathogenesis: Linking host genetics and the microbiome. Gut. 2013;62.
89. Repnik K, Potočnik U. eQTL analysis links inflammatory bowel disease associated 1q21 locus to ECM1 gene. Journal of Applied Genetics. 2016;57:363–72.
90. Hu S, Uniken Venema WT, Westra H-J, Vich Vila A, Barbieri R, Voskuil MD, et al. Inflammation status modulates the effect of host genetic variation on intestinal gene expression in inflammatory bowel disease. Nature Communications. 2021;12:1122.
91. Jung S, Liu W, Baek J, Moon JW, Ye BD, Lee H-S, et al. Expression quantitative trait loci (eQTL) mapping in korean patients with crohns disease and identification of potential causal genes through integration with disease associations. Frontiers in Genetics. 2020;11.
92. Dai Y, Pei G, Zhao Z, Jia P. A convergent study of genetic variants associated with crohns disease: Evidence from GWAS, gene expression, methylation, eQTL and TWAS. Frontiers in Genetics. 2019;10.
93. Ahmed I, Roy BC, Khan SA, Septer S, Umar S. Microbiome, metabolome and inflammatory bowel disease. Microorganisms. 2016;4:20.
94. Gallagher K, Catesson A, Griffin JL, Holmes E, Williams HRT. Metabolomic Analysis in Inflammatory Bowel Disease: A Systematic Review. Journal of Crohn’s & Colitis. 2021;15:813–26.
95. Krassowski M, Das V, Sahu SK, Misra BB. State of the field in multi-omics research: From computational needs to data mining and sharing. Frontiers in Genetics. 2020;11:1598.
96. Yannakoudakis H, Cummins R. Evaluating the performance of automated text scoring systems. Denver, Colorado: Association for Computational Linguistics; 2015. p. 213223.
98. Biancolillo A. Method development in the area of multi-block analysis focused on food analysis. PhD thesis.
99. Wu C, Zhou F, Ren J, Li X, Jiang Y, Ma S. A Selective Review of Multi-Level Omics Data Integration Using Variable Selection. High-Throughput. 2019;8:4.
100. Cavill R, Jennen D, Kleinjans J, Briedé JJ. Transcriptomic and metabolomic data integration. Briefings in Bioinformatics. 2016;17:891–901.
101. Chong J, Xia J. Computational approaches for integrative analysis of the metabolome and microbiome. Metabolites. 2017;7:62.
102. Huang S, Chaudhary K, Garmire LX. More is better: Recent progress in multi-omics data integration methods. Frontiers in Genetics. 2017;8.
103. Rohart F, Mason EA, Matigian N, Mosbergen R, Korn O, Chen T, et al. A molecular classification of human mesenchymal stromal cells. PeerJ. 2016;4:e1845.
104. Ibrahim EC, Guillemot V, Comte M, Tenenhaus A, Zendjidjian XY, Cancel A, et al. Modeling a linkage between blood transcriptional expression and activity in brain regions to infer the phenotype of schizophrenia patients. npj Schizophrenia. 2017;3:25.
105. Wheeler HE, Aquino-Michaels K, Gamazon ER, Trubetskoy VV, Dolan ME, Huang RS, et al. Poly-Omic Prediction of Complex Traits: OmicKriging. Genetic Epidemiology. 2014;38:402–15.
106. Yin L, Chau CKL, Sham P-C, So H-C. Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology. The American Journal of Human Genetics. 2019;105:1193–212.
107. Tarazona S, Arzalluz-Luque A, Conesa A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nature Computational Science. 2021;1–8.
108. Tarazona S, Balzano-Nogueira L, Gómez-Cabrero D, Schmidt A, Imhof A, Hankemeier T, et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nature Communications. 2020;11:3092.
109. Massoni-Badosa R, Iacono G, Moutinho C, Kulis M, Palau N, Marchese D, et al. Sampling time-dependent artifacts in single-cell genomics studies. Genome Biology. 2020;21:112.
110. Zhu Y, Wang L, Yin Y, Yang E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Scientific Reports. 2017;7:5435.
111. Ferreira PG, Muñoz-Aguirre M, Reverter F, Sá Godinho CP, Sousa A, Amadoz A, et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nature Communications. 2018;9:490.
112. Jacob F, Monod J. Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology. 1961;3:318–56.
113. Koh HWL, Fermin D, Vogel C, Choi KP, Ewing RM, Choi H. iOmicsPASS: Network-based integration of multiomics data for predictive subnetwork discovery. npj Systems Biology and Applications. 2019;5:1–0.
114. Yule GU. On the theory of correlation for any number of variables, treated by a new system of notation. Proceedings of the Royal Society of London Series A, Containing Papers of a Mathematical and Physical Character. 1907;79:182–93.
115. Tenenhaus A, Tenenhaus M. Regularized Generalized Canonical Correlation Analysis. Psychometrika. 2011;76:257–84.
116. Tenenhaus A, Philippe C, Guillemot V, Le Cao K-A, Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. Biostatistics. 2014;15:569–83.
117. Culhane AC, Perri‘ere G, Higgins DG. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003;4:59.
118. Vito RD, Bellio R, Trippa L, Parmigiani G. Multi-study factor analysis. Biometrics. 2019;75:337–46.
119. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, et al. Multi-Omics Factor Analysisa framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology. 2018;14:e8124.
120. Gomez-Cabrero D, Tarazona S, Ferreirós-Vidal I, Ramirez RN, Company C, Schmidt A, et al. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Scientific Data. 2019;6:1–5.
121. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nature Methods. 2018;15:962.
122. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nature Methods. 2015;12:902–3.
123. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biology. 2011;12:R60.
124. Didier G, Valdeolivas A, Baudot A. Identifying communities from multiplex biological networks by randomized optimization of modularity. F1000Research. 2018;7.
125. Valdeolivas A, Tichit L, Navarro C, Perrin S, Odelin G, Levy N, et al. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics (Oxford, England). 2019;35:497–505.
126. Pio-Lopez L, Valdeolivas A, Tichit L, Remy É, Baudot A. MultiVERSE: A multiplex and multiplex-heterogeneous network embedding approach. arXiv:200810085 [cs, q-bio]. 2021.
127. Bayes T, Price null. An essay towards solving a problem in the doctrine of chances. By the late rev. Mr. Bayes, f. R. s. Philosophical Transactions of the Royal Society of London. 1763;53:370–418.
128. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, et al. Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation. PLOS Biology. 2012;10:e1001301.
129. Lock EF, Dunson DB. Bayesian consensus clustering. Bioinformatics. 2013;29:2610–6.
130. Cantini L, Zakeri P, Hernandez C, Naldi A, Thieffry D, Remy E, et al. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications. 2021;12:124.
131. Tenenhaus M, Tenenhaus A, Groenen PJF. Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods. Psychometrika. 2017;82:737–77.
132. Rohart F, Gautier B, Singh A, Cao K-AL. mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology. 2017;13:e1005752.
133. Virtanen S, Klami A, Khan S, Kaski S. Bayesian Group Factor Analysis. PMLR; 2012. p. 1269–77.
134. Novoa-del-Toro E-M, Mezura-Montes E, Vignes M, Magdinier F, Tichit L, Baudot A. A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks. bioRxiv. 2020;2020.05.25.114215.
135. Lock EF, Hoadley KA, Marron JS, Nobel AB. JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES. The annals of applied statistics. 2013;7:523–42.
136. Yang Z, Michailidis G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics. 2016;32:1–8.
137. Sherry A, Henson RK. Conducting and Interpreting Canonical Correlation Analysis in Personality Research: A User-Friendly Primer. Journal of Personality Assessment. 2005;84:37–48.
138. Sherry A, Henson RK. Conducting and Interpreting Canonical Correlation Analysis in Personality Research: A User-Friendly Primer. 1981.
139. Chung R-H, Kang C-Y. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification. GigaScience. 2019;8.
140. Martínez-Mira C, Conesa A, Tarazona S. MOSim: Multi-Omics Simulation in R. 2018.
141. Patuzzi I, Baruzzo G, Losasso C, Ricci A, Di Camillo B. metaSPARSim: A 16S rRNA gene sequencing count data simulator. BMC Bioinformatics. 2019;20:416.
142. Fritz A, Hofmann P, Majda S, Dahms E, Dröge J, Fiedler J, et al. CAMISIM: Simulating metagenomes and microbial communities. Microbiome. 2019;7:17.
143. Fu J, Frazee AC, Collado-Torres L, Jaffe AE, Leek JT. Ballgown: Flexible, isoform-level differential expression analysis. Bioconductor version: Release (3.13); 2021.
144. Frazee AC, Jaffe AE, Kirchner R, Leek JT. Polyester: Simulate RNA-seq reads. Bioconductor version: Release (3.13); 2021.
145. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research. 2012;40:4288–97.
146. De Souza HSP, Fiocchi C, Iliopoulos D. The IBD interactome: An integrated view of aetiology, pathogenesis and therapy. 2017;14.
147. Valles-Colomer M, Darzi Y, Vieira-Silva S, Falony G, Raes J, Joossens M. Meta-omics in inflammatory bowel disease research: Applications, challenges, and guidelines. Journal of Crohn’s and Colitis. 2016;10:735–46.
148. Sudhakar P, Alsoud D, Wellens J, Verstockt S, Arnauts K, Verstockt B, et al. Tailoring multi-omics to inflammatory bowel diseases: All for one and one for all. Journal of Crohn’s and Colitis. 2022;jjac027.
149. Puget S, Philippe C, Bax DA, Job B, Varlet P, Junier M-P, et al. Mesenchymal Transition and PDGFRA Amplification/Mutation Are Key Distinct Oncogenic Events in Pediatric Diffuse Intrinsic Pontine Gliomas. PLOS ONE. 2012;7:e30313.
150. Morgan XC, Kabakchiev B, Waldron L, Tyler AD, Tickle TL, Milgrom R, et al. Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease. Genome Biology. 2015;16:67.
151. Howell KJ, Kraiczy J, Nayak KM, Gasparetto M, Ross A, Lee C, et al. DNA Methylation and Transcription Patterns in Intestinal Epithelial Cells From Pediatric Patients With Inflammatory Bowel Diseases Differentiate Disease Subtypes and Associate With Outcome. Gastroenterology. 2018;154:585–98.
152. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17:10–2.
153. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England). 2013;29:15–21.
154. Li B, Dewey CN. RSEM: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
155. Corraliza AM, Ricart E, L’opez-García A, Carme Masamunt M, Veny M, Esteller M, et al. Differences in Peripheral and Tissue Immune Cell Populations Following Haematopoietic Stem Cell Transplantation in Crohn’s Disease Patients. Journal of Crohn’s and Colitis. https://doi.org/10.1093/ecco-jcc/jjy203.
156. Revilla L, Mayorgas A, Corraliza AM, Masamunt MC, Metwaly A, Haller D, et al. Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis. PLOS ONE. 2021;16:e0246367.
157. Berry D, Ben Mahfoudh K, Wagner M, Loy A. Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Applied and Environmental Microbiology. 2011;77:7846–9.
158. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research. 2013;41:e1–1.
159. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods. 2013;10:996–8.
160. Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, et al. IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Scientific Reports. 2016;6.
161. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods. 2016;13:581–3.
162. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research. 2013;41 Database issue:D590–6.
163. Jordan C. Essai sur la géométrie à n dimensions. Bulletin de la Société Mathématique de France. 1875;3:103–74.
164. Tenenhaus M. Component-based Structural Equation Modelling. Total Quality Management & Business Excellence. 2008;19:871–86.
165. Tenenhaus M, Hanafi M. A Bridge Between PLS Path Modeling and Multi-Block Data Analysis. In: Esposito Vinzi V, Chin WW, Henseler J, Wang H, editors. Handbook of Partial Least Squares. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 99–123.
166. Tenenhaus A, Tenenhaus M. Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis. European Journal of Operational Research. 2014;238:391–403.
167. Tenenhaus A, Philippe C, Frouin V. Kernel Generalized Canonical Correlation Analysis. Computational Statistics & Data Analysis. 2015;90:114–31.
168. Gloaguen A, Philippe C, Frouin V, Gennari G, Dehaene-Lambertz G, Le Brusquet L, et al. Multiway generalized canonical correlation analysis. Biostatistics. 2020. https://doi.org/10.1093/biostatistics/kxaa010.
169. Schäfer J, Strimmer K. A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics. Statistical Applications in Genetics and Molecular Biology. 2005;4.
170. Horst P. Relations among m sets of measures. Psychometrika. 1961;26:129–49.
171. KETTENRING JR. Canonical analysis of several sets of variables. Biometrika. 1971;58:433–51.
172. Van de Geer JP. Linear relations among k sets of variables. Psychometrika. 1984;49:79–94.
173. Meng C, Kuster B, Culhane AC, Gholami AM. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics. 2014;15:162.
174. Planell N, Lagani V, Sebastian-Leon P, van der Kloet F, Ewing E, Karathanasis N, et al. STATegra: Multi-omics data integration a conceptual scheme with a bioinformatics pipeline. Frontiers in Genetics. 2021;12.
175. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021;2:100141.
176. Richter FC, Friedrich M, Pohin M, Alsaleh G, Guschina I, Wideman SK, et al. Cell-extrinsic autophagy in mature adipocytes regulates anti-inflammatory response to intestinal tissue injury through lipid mobilization. 2021;2021.10.25.465200.
177. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. 2005;102:15545–50.
178. Protiva P, Pendyala S, Nelson C, Augenlicht LH, Lipkin M, Holt PR. Calcium and 1,25-dihydroxyvitamin D3 modulate genes of immune and inflammatory pathways in the human colon: A human crossover trial. The American Journal of Clinical Nutrition. 2016;103:1224–31.
179. Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. 2021. https://doi.org/https://doi.org/10.1101/060012.
180. Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, et al. The reactome pathway knowledgebase. Nucleic Acids Research. 2016;44.
181. Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.
182. Escudero-Hernández C, Beelen Granlund A van, Bruland T, Sandvik AK, Koch S, Østvik AE, et al. Transcriptomic Profiling of Collagenous Colitis Identifies Hallmarks of Nondestructive Inflammatory Bowel Disease. Cellular and Molecular Gastroenterology and Hepatology. 2021;12:665–87.
183. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecology. 2001;26:32–46.
184. Warton DI, Wright ST, Wang Y. Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution. 2012;3:89–101.
185. Goeman JJ, van de Geer SA, van Houwelingen HC. Testing against a high dimensional alternative. Journal of the Royal Statistical Society Series B (Statistical Methodology). 2006;68:477–93.
186. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: Community ecology package. 2020.
187. Ritchie MEM, Phipson B, Wu D, Hu Y, Law CWC, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015;43:e47.
188. Law CW, Chen Y, Shi W, Smyth GK. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology. 2014;15:R29.
189. Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Research. 2018;5.
190. Yoav Benjamini, Yosef Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 57.
191. Langfelder P, Horvath S. WGCNA: An r package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
192. Filzmoser P, Viertl R. Testing hypotheses with fuzzy data: The fuzzy p -value. Metrika. 2004;59:21–9.
193. Dubois D, Prade H. [Proceedings 1993] second IEEE international conference on fuzzy systems. 1993. p. 1059–1068 vol.2.
194. Revilla Sancho L, Lozano J-J, Salas A. experDesign: stratifying samples into batches with minimal bias. Journal of Open Source Software. 2021;6:3358.
195. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: An open-source package for r and s+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
196. Wickham H. Advanced r. Second Edition. Boca Raton: Chapman & Hall; 2019.
197. Greenland S, Brumback B. An overview of relations among causal modelling methods. International Journal of Epidemiology. 2002;31:1030–7.
198. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. The ISME Journal. 2017;11:2639–43.
199. Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, et al. Microbiome differential abundance methods produce different results across 38 datasets. Nature Communications. 2022;13:342.
200. Louca S, Doebeli M, Parfrey LW. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome. 2018;6:41.
201. Nadalian B, Yadegar A, Houri H, Olfatifar M, Shahrokh S, Asadzadeh Aghdaei H, et al. Prevalence of the pathobiont adherent-invasive Escherichia coli and inflammatory bowel disease: a systematic review and meta-analysis. Journal of Gastroenterology and Hepatology. 2021;36:852–63.
202. Chalise P, Raghavan R, Fridley BL. InterSIM: Simulation tool for multiple integrative omic datasets. Computer methods and programs in biomedicine. 2016;128:69–74.
203. Chung R-H, Kang C-Y. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification. GigaScience. 2019;8.
204. Ugidos M, Tarazona S, Prats-Montalbán JM, Ferrer A, Conesa A. MultiBaC: A strategy to remove batch effects between different omic data types: Statistical Methods in Medical Research. 2020. https://doi.org/10.1177/0962280220907365.
205. Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, et al. Methods for the integration of multi-omics data: Mathematical aspects. BMC Bioinformatics. 2016;17:S15.
206. Holmes E, Li JV, Marchesi JR, Nicholson JK. Gut Microbiota Composition and Activity in Relation to Host Metabolic Phenotype and Disease Risk. Cell Metabolism. 2012;16:559–64.
207. Stappenbeck TS, Hooper LV, Gordon JI. Developmental regulation of intestinal angiogenesis by indigenous microbes via Paneth cells. Proceedings of the National Academy of Sciences of the United States of America. 2002;99:15451–5.
208. Brand EC, Klaassen MAY, Gacesa R, Vich Vila A, Ghosh H, Zoete MR de, et al. Healthy Cotwins Share Gut Microbiome Signatures With Their Inflammatory Bowel Disease Twins and Unrelated Patients. Gastroenterology. 2021;160:1970–85.
209. Susin A, Wang Y, Lê Cao K-A, Calle ML. Variable selection in microbiome compositional data analysis. NAR Genomics and Bioinformatics. 2020;2:lqaa029.
210. Sartor RB. Mechanisms of disease: pathogenesis of Crohn’s disease and ulcerative colitis. Nature Clinical Practice Gastroenterology & Hepatology. 2006;3:390–407.
211. Vandeputte D, Kathagen G, D’hoe K, Vieira-Silva S, Valles-Colomer M, Sabino J, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 2017;551:507–11.
212. Vila-Casadesús M, Gironella M, Lozano JJ. MiRComb: An R Package to Analyse miRNA-mRNA Interactions. Examples across Five Digestive Cancers. PloS One. 2016;11:e0151127.
213. Goldberg D. What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys. 1991;23:548.
214. Yan L, Ma C, Wang D, Hu Q, Qin M, Conroy JM, et al. OSAT: A tool for sample-to-batch allocations in genomics experiments. BMC Genomics. 2012;13:689.
215. Papenberg M, Klau GW. Using anticlustering to partition data sets into equivalent parts. Psychological Methods. 2020. https://doi.org/10.1037/met0000301.
216. Sinke L, Cats D, Heijmans BT. Omixer: Multivariate and reproducible sample randomization to proactively counter batch effects in omics studies. Bioinformatics. 2021. https://doi.org/10.1093/bioinformatics/btab159.
217. Haberman Y, Tickle TL, Dexheimer PJ, Kim M-O, Tang D, Karns R, et al. Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature. The Journal of Clinical Investigation. 2014;124:3617–33.
218. Community TTW. The turing way: A handbook for reproducible, ethical and collaborative research. The Turing Way Community; 2021.
219. Meng C, Kuster B, Culhane AC, Gholami AM. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics. 2014;15:162.
220. Pearl J. Transportability across studies: A formal approach. 2011.
221. Simpson EH. The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society: Series B (Methodological). 1951;13:238–41.
222. Stillman DJ. Interactions of a Eukaryotic RNA Polymerase III Transcription Factor with Promoters. Biology; 1984.
223. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843–54.
224. Hamilton AJ, Baulcombe DC. A species of small antisense RNA in posttranscriptional gene silencing in plants. Science. 1999;286:950–2.
225. Thornburg ZR, Bianchi DM, Brier TA, Gilbert BR, Earnest TM, Melo MCR, et al. Fundamental behaviors emerge from simulations of a living minimal cell. Cell. 2022;185:345–360.e28.
226. Criss ZK, Bhasin N, Di Rienzi SC, Rajan A, Deans-Fielder K, Swaminathan G, et al. Drivers of transcriptional variance in human intestinal epithelial organoids. Physiological Genomics. 2021;53:486–508.
227. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. Enterotypes of the human gut microbiome. Nature. 2011;473:174–80.
228. Koren O, Knights D, Gonzalez A, Waldron L, Segata N, Knight R, et al. A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets. PLOS Computational Biology. 2013;9:e1002863.
229. Cheng M, Ning K. Stereotypes About Enterotype: the Old and New Ideas. Genomics, Proteomics & Bioinformatics. 2019;17:4–12.
230. Hasan N, Yang H. Factors affecting the composition of the gut microbiota, and its modulation. PeerJ. 2019;7:e7502.
231. Bringiotti R, Ierardi E, Lovero R, Losurdo G, Leo AD, Principi M. Intestinal microbiota: The explosive mixture at the origin of inflammatory bowel disease? World Journal of Gastrointestinal Pathophysiology. 2014;5:550–9.
232. Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569:655.
233. Douglas GM, Hansen R, Jones CMA, Dunn KA, Comeau AM, Bielawski JP, et al. Multi-omics differentially classify disease state and treatment outcome in pediatric crohns disease. Microbiome. 2018;6:13.
234. Halfvarson J, Brislawn CJ, Lamendella R, Vázquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nature Microbiology. 2017;2:1–7.
235. Edgar R. Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ. 2018;6:e5030.
236. Ungar B, Yavzori M, Fudim E, Picard O, Kopylov U, Eliakim R, et al. Host transcriptome signatures in human faecal-washes predict histological remission in patients with IBD. Gut. 2022. https://doi.org/10.1136/gutjnl-2021-325516.
237. Zhang X, Li L, Butcher J, Stintzi A, Figeys D. Advancing functional and translational microbiome research using meta-omics approaches. Microbiome. 2019;7:154.
238. Jiang D, Armour CR, Hu C, Mei M, Tian C, Sharpton TJ, et al. Microbiome multi-omics network analysis: Statistical considerations, limitations, and opportunities. Frontiers in Genetics. 2019;10.
239. Haynes WA, Tomczak A, Khatri P. Gene annotation bias impedes biomedical research. Scientific Reports. 2018;8:1362.
240. Collij V, Klaassen MAY, Weersma RK, Vila AV. Gut microbiota in inflammatory bowel diseases: moving from basic science to clinical applications. Human Genetics. 2021;140:703–8.
241. Parkhomenko E, Tritchler D, Beyene J. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration. Statistical Applications in Genetics and Molecular Biology. 2009;8.
242. Waaijenborg S, Hamer PCV de W, Zwinderman AH. Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis. Statistical Applications in Genetics and Molecular Biology. 2008;7.
243. Witten DM, Tibshirani RJ. Extensions of sparse canonical correlation analysis with applications to genomic data. Statistical Applications in Genetics and Molecular Biology. 2009;8:127.
244. Lê Cao K-A, Martin PG, Robert-Granié C, Besse P. Sparse canonical methods for biological data integration: Application to a cross-platform study. BMC Bioinformatics. 2009;10:34.
245. Hwang H. Regularized Generalized Structured Component Analysis. Psychometrika. 2009;74:517–30.
246. Soneson C, Lilljebjörn H, Fioretos T, Fontes M. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. BMC Bioinformatics. 2010;11:191.
247. Zhang S, Li Q, Liu J, Zhou XJ. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011;27:i401–9.
248. Lee W, Lee D, Lee Y, Pawitan Y. Sparse Canonical Covariance Analysis for High-throughput Data. Statistical Applications in Genetics and Molecular Biology. 2011;10.
249. Abdi H, Williams LJ, Valentin D, Bennani-Dosse M. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Computational Statistics. 2012;4:124–67.
250. Zhang S, Liu C-C, Li W, Shen H, Laird PW, Zhou XJ. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Research. 2012;40:9379–91.
251. Li W, Zhang S, Liu C-C, Zhou XJ. Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics. 2012;28:2458–66.
252. Abdi H, Williams LJ, Valentin D. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Computational Statistics. 2013;5:149–79.
253. Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017;33:2232–4.
254. Ray P, Zheng L, Lucas J, Carin L. Bayesian joint analysis of heterogeneous genomics data. Bioinformatics. 2014;30:1370–6.
255. Bunte K, Leppäaho E, Saarinen I, Kaski S. Sparse group factor analysis for biclustering of multiple data sources. Bioinformatics. 2016;32:2457–63.
256. Chen M, Gao C, Ren Z, Zhou HH. Sparse CCA via precision adjusted iterative thresholding. arXiv:13116186 [math, stat]. 2013.
257. Leppäaho E, Ammad-ud-din M, Kaski S. GFA: Exploratory analysis of multiple data sources with group factor analysis. Journal of Machine Learning Research. 2017;18:1–5.
258. Klami A, Bouchard G, Tripathi A. Group-sparse embeddings in collective matrix factorization. arXiv:13125921 [cs, stat]. 2014.
259. Meng C, Basunia A, Peters B, Gholami AM, Kuster B, Culhane AC. MOGSA: integrative single sample gene-set analysis of multiple omics data. 2018.
260. Zhao S, Gao C, Mukherjee S, Engelhardt BE. Bayesian group latent factor analysis with structured sparsity. arXiv:14112698 [q-bio, stat]. 2015.
261. Voillet V, Besse P, Liaubet L, San Cristobal M, González I. Handling missing rows in multi-omics data integration: Multiple imputation in multiple factor analysis framework. BMC Bioinformatics. 2016;17:402.
262. Beaton D, Dunlop J, Abdi H, Alzheimer’s Disease Neuroimaging Initiative. Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data. Psychological Methods. 2016;21:621–51.
263. Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, et al. DIABLO: An integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35:3055–62.
264. Yoon G, Carroll RJ, Gaynanova I. Sparse semiparametric canonical correlation analysis for data of mixed types. arXiv:180705274 [stat]. 2019.
265. Gaynanova I, Li G. Structural learning and integrative decomposition of multi-view data. arXiv:170706573 [stat]. 2017.
266. Madrigal P. fCCAC: Functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics. 2017;33:746–8.
267. Yoshida K, Yoshimoto J, Doya K. Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data. BMC Bioinformatics. 2017;18:108.
268. Kawaguchi A, Yamashita F. Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics. 2017;18:651–65.
269. Feng Q, Jiang M, Hannig J, Marron JS. Angle-based joint and individual variation explained. arXiv:170402060 [stat]. 2018.
270. Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC, et al. MOFA+: A statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology. 2020;21:111.
271. Brown BC, Bray NL, Pachter L. Expression reflects population structure. 2018. https://doi.org/10.1101/364448.
272. Zhang Y, Gaynanova I. Joint association and classification analysis of multi-view data. arXiv:181108511 [cs, stat]. 2020.
273. Tang TM, Allen GI. Integrated principal components analysis. arXiv:181000832 [stat]. 2021.
274. Min EJ, Safo SE, Long Q. Penalized co-inertia analysis with applications to -omics data. Bioinformatics (Oxford, England). 2019;35:1018–25.
275. Safo SE, Li S, Long Q. Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information. Biometrics. 2018;74:300–12.
276. Min W, Liu J, Zhang S. Sparse weighted canonical correlation analysis. arXiv:171004792 [cs, stat]. 2017.
277. Bouhaddani S el, Uh H-W, Jongbloed G, Hayward C, Klarić L, Kiełbasa SM, et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics. 2018;19:371.
278. Pimentel H, Zhiyue H, Huang H. Biclustering by sparse canonical correlation analysis. 2018;6:11.
279. Kim Y, Bismeijer T, Zwart W, Wessels LFA, Vis DJ. Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo. Nature Communications. 2019;10:5034.
280. Lock EF, Park JY, Hoadley KA. Bidimensional linked matrix factorization for pan-omics pan-cancer analysis. arXiv:200202601 [cs, q-bio, stat]. 2020.
281. Ronen J, Hayat S, Akalin A. Evaluation of colorectal cancer subtypes and cell lines using deep learning. Life Science Alliance. 2019;2.
282. Shi WJ, Zhuang Y, Russell PH, Hobbs BD, Parker MM, Castaldi PJ, et al. Unsupervised discovery of phenotype-specific multi-omics networks. Bioinformatics. 2019;35:4336–43.
283. Csala A, Zwinderman AH, Hof MH. Multiset sparse partial least squares path modeling for high dimensional omics data analysis. BMC Bioinformatics. 2020;21:9.
284. Fan Z, Zhou Y, Ressom HW. MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery. Metabolites. 2020;10:144.
285. Shu H, Wang X, Zhu H. D-CCA: A decomposition-based canonical correlation analysis for high-dimensional datasets. Journal of the American Statistical Association. 2020;115:292–306.
286. Hawinkel S, Bijnens L, Cao K-AL, Thas O. Model-based joint visualization of multiple compositional omics datasets. NAR Genomics and Bioinformatics. 2020;2.
287. Gundersen G, Dumitrascu B, Ash JT, Engelhardt BE. Uncertainty in Artificial Intelligence. PMLR; 2020. p. 945–55.
288. Velten B, Braunger JM, Arnol D, Argelaguet R, Stegle O. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. bioRxiv. 2020;2020.11.03.366674.